diff --git a/build/doctrees/environment.pickle b/build/doctrees/environment.pickle
index 6121d81..b2ef3f4 100644
Binary files a/build/doctrees/environment.pickle and b/build/doctrees/environment.pickle differ
diff --git a/build/doctrees/nbsphinx/notebooks/DemoNotebook_ammico.ipynb b/build/doctrees/nbsphinx/notebooks/DemoNotebook_ammico.ipynb
index 55ae8da..9dbe482 100644
--- a/build/doctrees/nbsphinx/notebooks/DemoNotebook_ammico.ipynb
+++ b/build/doctrees/nbsphinx/notebooks/DemoNotebook_ammico.ipynb
@@ -14,15 +14,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:42:44.420408Z",
-     "iopub.status.busy": "2024-02-19T08:42:44.420216Z",
-     "iopub.status.idle": "2024-02-19T08:42:44.428568Z",
-     "shell.execute_reply": "2024-02-19T08:42:44.428037Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# if running on google colab\n",
@@ -52,257 +45,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:42:44.430935Z",
-     "iopub.status.busy": "2024-02-19T08:42:44.430571Z",
-     "iopub.status.idle": "2024-02-19T08:42:51.757352Z",
-     "shell.execute_reply": "2024-02-19T08:42:51.756689Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Downloading readme:   0%|          | 0.00/21.0 [00:00<?, ?B/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Downloading readme: 100%|██████████| 21.0/21.0 [00:00<00:00, 178kB/s]"
-     ]
-    },
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-     "name": "stderr",
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-      "\n"
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-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Generating train split: 0 examples [00:00, ? examples/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Generating train split: 6 examples [00:00, 423.54 examples/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from datasets import load_dataset\n",
     "from pathlib import Path\n",
@@ -320,15 +65,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:42:51.760070Z",
-     "iopub.status.busy": "2024-02-19T08:42:51.759727Z",
-     "iopub.status.idle": "2024-02-19T08:42:52.642174Z",
-     "shell.execute_reply": "2024-02-19T08:42:52.641528Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "data_path = \"./data-test\"\n",
@@ -349,15 +87,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:42:52.645250Z",
-     "iopub.status.busy": "2024-02-19T08:42:52.644756Z",
-     "iopub.status.idle": "2024-02-19T08:43:01.940227Z",
-     "shell.execute_reply": "2024-02-19T08:43:01.939551Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
@@ -395,15 +126,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:43:01.943248Z",
-     "iopub.status.busy": "2024-02-19T08:43:01.942735Z",
-     "iopub.status.idle": "2024-02-19T08:43:01.945881Z",
-     "shell.execute_reply": "2024-02-19T08:43:01.945338Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = \"/content/drive/MyDrive/misinformation-data/misinformation-campaign-981aa55a3b13.json\""
@@ -431,15 +155,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:43:01.948209Z",
-     "iopub.status.busy": "2024-02-19T08:43:01.947847Z",
-     "iopub.status.idle": "2024-02-19T08:43:01.952312Z",
-     "shell.execute_reply": "2024-02-19T08:43:01.951836Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "image_dict = ammico.find_files(\n",
@@ -460,38 +177,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:43:01.954766Z",
-     "iopub.status.busy": "2024-02-19T08:43:01.954396Z",
-     "iopub.status.idle": "2024-02-19T08:43:01.978861Z",
-     "shell.execute_reply": "2024-02-19T08:43:01.977627Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"100%\"\n",
-       "            height=\"650\"\n",
-       "            src=\"http://127.0.0.1:8055/\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x7f966243a8e0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "analysis_explorer = ammico.AnalysisExplorer(image_dict)\n",
     "analysis_explorer.run_server(port=8055)"
@@ -514,15 +202,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:43:01.981825Z",
-     "iopub.status.busy": "2024-02-19T08:43:01.981158Z",
-     "iopub.status.idle": "2024-02-19T08:43:01.984935Z",
-     "shell.execute_reply": "2024-02-19T08:43:01.983983Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# dump file name\n",
@@ -540,221 +221,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:43:01.988248Z",
-     "iopub.status.busy": "2024-02-19T08:43:01.987632Z",
-     "iopub.status.idle": "2024-02-19T08:44:04.645259Z",
-     "shell.execute_reply": "2024-02-19T08:44:04.644561Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Downloading data from 'https://github.com/serengil/deepface_models/releases/download/v1.0/retinaface.h5' to file '/home/runner/.cache/pooch/3be32af6e4183fa0156bc33bda371147-retinaface.h5'.\n"
-     ]
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found missing key(s) {'no_faces', 'age', 'wears_mask', 'emotion (category)', 'multiple_faces', 'emotion', 'gender', 'race', 'face'} in subdict img1 - setting to None.\n",
-      "Found missing key(s) {'no_faces', 'age', 'wears_mask', 'emotion (category)', 'multiple_faces', 'emotion', 'gender', 'race', 'face'} in subdict img2 - setting to None.\n",
-      "Found missing key(s) {'no_faces', 'age', 'wears_mask', 'emotion (category)', 'multiple_faces', 'emotion', 'gender', 'race', 'face'} in subdict img3 - setting to None.\n",
-      "Found missing key(s) {'no_faces', 'age', 'wears_mask', 'emotion (category)', 'multiple_faces', 'emotion', 'gender', 'race', 'face'} in subdict img0 - setting to None.\n",
-      "Found missing key(s) {'no_faces', 'age', 'wears_mask', 'emotion (category)', 'multiple_faces', 'emotion', 'gender', 'race', 'face'} in subdict img5 - setting to None.\n"
-     ]
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-     "output_type": "stream",
-     "text": [
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-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Downloading data from 'https://github.com/chandrikadeb7/Face-Mask-Detection/raw/v1.0.0/mask_detector.model' to file '/home/runner/.cache/pooch/865b4b1e20f798935b70082440d5fb21-mask_detector.model'.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
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-      "\r",
-      "1/1 [==============================] - ETA: 0s"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Downloading data from 'https://github.com/serengil/deepface_models/releases/download/v1.0/age_model_weights.h5' to file '/home/runner/.cache/pooch/39859d3331cd91ac06154cc306e1acc8-age_model_weights.h5'.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Downloading data from 'https://github.com/serengil/deepface_models/releases/download/v1.0/facial_expression_model_weights.h5' to file '/home/runner/.cache/pooch/dd5d5d6d8f5cecdc0fa6cb34d4d82d16-facial_expression_model_weights.h5'.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Downloading data from 'https://github.com/serengil/deepface_models/releases/download/v1.0/gender_model_weights.h5' to file '/home/runner/.cache/pooch/2e0d8fb96c5ee966ade0f3f2360f6478-gender_model_weights.h5'.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Downloading data from 'https://github.com/serengil/deepface_models/releases/download/v1.0/race_model_single_batch.h5' to file '/home/runner/.cache/pooch/382cb5446128012fa5305ddb9d608751-race_model_single_batch.h5'.\n"
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-     "name": "stdout",
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-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for num, key in tqdm(enumerate(image_dict.keys()), total=len(image_dict)):    # loop through all images\n",
     "    image_dict[key] = ammico.EmotionDetector(image_dict[key]).analyse_image() # analyse image with EmotionDetector and update dict\n",
@@ -773,4980 +242,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:44:04.647938Z",
-     "iopub.status.busy": "2024-02-19T08:44:04.647467Z",
-     "iopub.status.idle": "2024-02-19T08:45:58.641407Z",
-     "shell.execute_reply": "2024-02-19T08:45:58.640478Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "  0%|          | 0/6 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting en-core-web-md==3.7.1\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_md-3.7.1/en_core_web_md-3.7.1-py3-none-any.whl (42.8 MB)\n",
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-     ]
-    },
-    {
-     "name": "stdout",
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-     "text": [
-      "Installing collected packages: en-core-web-md\n"
-     ]
-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Successfully installed en-core-web-md-3.7.1\n",
-      "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
-      "You can now load the package via spacy.load('en_core_web_md')\n",
-      "\u001b[38;5;3m⚠ Restart to reload dependencies\u001b[0m\n",
-      "If you are in a Jupyter or Colab notebook, you may need to restart Python in\n",
-      "order to load all the package's dependencies. You can do this by selecting the\n",
-      "'Restart kernel' or 'Restart runtime' option.\n"
-     ]
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+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for num, key in tqdm(enumerate(image_dict.keys()), total=len(image_dict)):  # loop through all images\n",
     "    image_dict[key] = ammico.TextDetector(image_dict[key], analyse_text=True).analyse_image() # analyse image with EmotionDetector and update dict\n",
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+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# clear memory on cuda first? Faces seems to always not release GPU\n",
     "# initialize the models\n",
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-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# initialize the models\n",
     "image_summary_detector = ammico.SummaryDetector(subdict = image_dict, analysis_type=\"summary\", model_type=\"base\")\n",
@@ -7339,15 +323,8 @@
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    "cell_type": "code",
-   "execution_count": 13,
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-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:50:22.307625Z",
-     "iopub.status.busy": "2024-02-19T08:50:22.307231Z",
-     "iopub.status.idle": "2024-02-19T08:50:22.310969Z",
-     "shell.execute_reply": "2024-02-19T08:50:22.310432Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "image_df = ammico.get_dataframe(image_dict)"
@@ -7362,192 +339,9 @@
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    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:50:22.313326Z",
-     "iopub.status.busy": "2024-02-19T08:50:22.312953Z",
-     "iopub.status.idle": "2024-02-19T08:50:22.351063Z",
-     "shell.execute_reply": "2024-02-19T08:50:22.350534Z"
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-       "      <td>[None]</td>\n",
-       "      <td>...</td>\n",
-       "      <td>en</td>\n",
-       "      <td>THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO M...</td>\n",
-       "      <td>THE PROBLEM DOM NVS TIO MINA Monographs on Num...</td>\n",
-       "      <td>H. H. W. WILKINSON: The Algebri</td>\n",
-       "      <td>NEGATIVE</td>\n",
-       "      <td>0.97</td>\n",
-       "      <td>[ALGEBRAIC EIGENVAL, NVS TIO MI, J, H, WILKINSON]</td>\n",
-       "      <td>[MISC, ORG, ORG, ORG, ORG]</td>\n",
-       "      <td>a yellow book with green lettering on it</td>\n",
-       "      <td>[an old book with a picture of the slogan of t...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3 rows × 21 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             filename face multiple_faces  no_faces wears_mask     age  \\\n",
-       "0  data-test/img4.png   No             No         0       [No]  [None]   \n",
-       "1  data-test/img1.png   No             No         0       [No]  [None]   \n",
-       "2  data-test/img2.png   No             No         0       [No]  [None]   \n",
-       "\n",
-       "   gender    race emotion emotion (category)  ... text_language  \\\n",
-       "0  [None]  [None]  [None]             [None]  ...            en   \n",
-       "1  [None]  [None]  [None]             [None]  ...            en   \n",
-       "2  [None]  [None]  [None]             [None]  ...            en   \n",
-       "\n",
-       "                                        text_english  \\\n",
-       "0                                        MOODOVIN XI   \n",
-       "1  SCATTERING THEORY The Quantum Theory of Nonrel...   \n",
-       "2  THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO M...   \n",
-       "\n",
-       "                                          text_clean  \\\n",
-       "0                                                 XI   \n",
-       "1  THEORY The Quantum Theory of Collisions JOHN R...   \n",
-       "2  THE PROBLEM DOM NVS TIO MINA Monographs on Num...   \n",
-       "\n",
-       "                                        text_summary sentiment  \\\n",
-       "0   MOODOVIN XI XI: Vladimir Putin, Vladimir Vlad...  POSITIVE   \n",
-       "1   SCATTERING THEORY The Quantum Theory of Nonre...  POSITIVE   \n",
-       "2                    H. H. W. WILKINSON: The Algebri  NEGATIVE   \n",
-       "\n",
-       "  sentiment_score                                             entity  \\\n",
-       "0            0.66                                      [MOODOVIN XI]   \n",
-       "1            0.91  [Non, ##vist, Col, ##N, R, T, ##AYL, Universit...   \n",
-       "2            0.97  [ALGEBRAIC EIGENVAL, NVS TIO MI, J, H, WILKINSON]   \n",
-       "\n",
-       "                                   entity_type  \\\n",
-       "0                                        [ORG]   \n",
-       "1  [MISC, MISC, MISC, ORG, PER, PER, ORG, ORG]   \n",
-       "2                   [MISC, ORG, ORG, ORG, ORG]   \n",
-       "\n",
-       "                                 const_image_summary  \\\n",
-       "0  a river running through a city next to tall bu...   \n",
-       "1  a close up of a piece of paper with writing on it   \n",
-       "2           a yellow book with green lettering on it   \n",
-       "\n",
-       "                         3_non-deterministic_summary  \n",
-       "0  [buildings near a waterway with small boats pa...  \n",
-       "1  [a white paper with some black writing on it, ...  \n",
-       "2  [an old book with a picture of the slogan of t...  \n",
-       "\n",
-       "[3 rows x 21 columns]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "image_df.head(3)"
    ]
@@ -7561,34 +355,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:50:22.391547Z",
-     "iopub.status.busy": "2024-02-19T08:50:22.391161Z",
-     "iopub.status.idle": "2024-02-19T08:50:25.022235Z",
-     "shell.execute_reply": "2024-02-19T08:50:25.021403Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "OSError",
-     "evalue": "Cannot save file into a non-existent directory: '/content/drive/MyDrive/misinformation-data'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mimage_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/content/drive/MyDrive/misinformation-data/data_out.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/util/_decorators.py:333\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    327\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m    328\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    329\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m    330\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    331\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m    332\u001b[0m     )\n\u001b[0;32m--> 333\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/core/generic.py:3961\u001b[0m, in \u001b[0;36mNDFrame.to_csv\u001b[0;34m(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, lineterminator, chunksize, date_format, doublequote, escapechar, decimal, errors, storage_options)\u001b[0m\n\u001b[1;32m   3950\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m, ABCDataFrame) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mto_frame()\n\u001b[1;32m   3952\u001b[0m formatter \u001b[38;5;241m=\u001b[39m DataFrameFormatter(\n\u001b[1;32m   3953\u001b[0m     frame\u001b[38;5;241m=\u001b[39mdf,\n\u001b[1;32m   3954\u001b[0m     header\u001b[38;5;241m=\u001b[39mheader,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3958\u001b[0m     decimal\u001b[38;5;241m=\u001b[39mdecimal,\n\u001b[1;32m   3959\u001b[0m )\n\u001b[0;32m-> 3961\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mDataFrameRenderer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mformatter\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_csv\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3962\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath_or_buf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3963\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlineterminator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlineterminator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3964\u001b[0m \u001b[43m    \u001b[49m\u001b[43msep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msep\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3965\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3966\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3967\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3968\u001b[0m \u001b[43m    \u001b[49m\u001b[43mquoting\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquoting\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3969\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3970\u001b[0m \u001b[43m    \u001b[49m\u001b[43mindex_label\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex_label\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3971\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3972\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunksize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunksize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3973\u001b[0m \u001b[43m    \u001b[49m\u001b[43mquotechar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquotechar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3974\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdate_format\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdate_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3975\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdoublequote\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdoublequote\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3976\u001b[0m \u001b[43m    \u001b[49m\u001b[43mescapechar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mescapechar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3977\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3978\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/io/formats/format.py:1014\u001b[0m, in \u001b[0;36mDataFrameRenderer.to_csv\u001b[0;34m(self, path_or_buf, encoding, sep, columns, index_label, mode, compression, quoting, quotechar, lineterminator, chunksize, date_format, doublequote, escapechar, errors, storage_options)\u001b[0m\n\u001b[1;32m    993\u001b[0m     created_buffer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    995\u001b[0m csv_formatter \u001b[38;5;241m=\u001b[39m CSVFormatter(\n\u001b[1;32m    996\u001b[0m     path_or_buf\u001b[38;5;241m=\u001b[39mpath_or_buf,\n\u001b[1;32m    997\u001b[0m     lineterminator\u001b[38;5;241m=\u001b[39mlineterminator,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1012\u001b[0m     formatter\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfmt,\n\u001b[1;32m   1013\u001b[0m )\n\u001b[0;32m-> 1014\u001b[0m \u001b[43mcsv_formatter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1016\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m created_buffer:\n\u001b[1;32m   1017\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(path_or_buf, StringIO)\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/io/formats/csvs.py:251\u001b[0m, in \u001b[0;36mCSVFormatter.save\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    247\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    248\u001b[0m \u001b[38;5;124;03mCreate the writer & save.\u001b[39;00m\n\u001b[1;32m    249\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    250\u001b[0m \u001b[38;5;66;03m# apply compression and byte/text conversion\u001b[39;00m\n\u001b[0;32m--> 251\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    252\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    253\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    254\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    255\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    256\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    257\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    258\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m handles:\n\u001b[1;32m    259\u001b[0m     \u001b[38;5;66;03m# Note: self.encoding is irrelevant here\u001b[39;00m\n\u001b[1;32m    260\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwriter \u001b[38;5;241m=\u001b[39m csvlib\u001b[38;5;241m.\u001b[39mwriter(\n\u001b[1;32m    261\u001b[0m         handles\u001b[38;5;241m.\u001b[39mhandle,\n\u001b[1;32m    262\u001b[0m         lineterminator\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlineterminator,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    267\u001b[0m         quotechar\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mquotechar,\n\u001b[1;32m    268\u001b[0m     )\n\u001b[1;32m    270\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_save()\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/io/common.py:749\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    747\u001b[0m \u001b[38;5;66;03m# Only for write methods\u001b[39;00m\n\u001b[1;32m    748\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode \u001b[38;5;129;01mand\u001b[39;00m is_path:\n\u001b[0;32m--> 749\u001b[0m     \u001b[43mcheck_parent_directory\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    751\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m compression:\n\u001b[1;32m    752\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m compression \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mzstd\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    753\u001b[0m         \u001b[38;5;66;03m# compression libraries do not like an explicit text-mode\u001b[39;00m\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/io/common.py:616\u001b[0m, in \u001b[0;36mcheck_parent_directory\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m    614\u001b[0m parent \u001b[38;5;241m=\u001b[39m Path(path)\u001b[38;5;241m.\u001b[39mparent\n\u001b[1;32m    615\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m parent\u001b[38;5;241m.\u001b[39mis_dir():\n\u001b[0;32m--> 616\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(\u001b[38;5;124mrf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot save file into a non-existent directory: \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparent\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "\u001b[0;31mOSError\u001b[0m: Cannot save file into a non-existent directory: '/content/drive/MyDrive/misinformation-data'"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "image_df.to_csv(\"/content/drive/MyDrive/misinformation-data/data_out.csv\")"
    ]
@@ -7611,15 +380,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:50:25.032719Z",
-     "iopub.status.busy": "2024-02-19T08:50:25.032386Z",
-     "iopub.status.idle": "2024-02-19T08:50:25.035336Z",
-     "shell.execute_reply": "2024-02-19T08:50:25.034770Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = \"/content/drive/MyDrive/misinformation-data/misinformation-campaign-981aa55a3b13.json\"\n"
@@ -7636,88 +398,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "execution": {
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-      "\r",
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-      "\r",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for num, key in tqdm(enumerate(image_dict.keys()), total=len(image_dict)):  # loop through all images\n",
     "    image_dict[key] = ammico.TextDetector(image_dict[key],                  # analyse image with TextDetector and update dict\n",
@@ -7793,15 +476,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:51:21.186991Z",
-     "iopub.status.busy": "2024-02-19T08:51:21.186599Z",
-     "iopub.status.idle": "2024-02-19T08:51:27.937584Z",
-     "shell.execute_reply": "2024-02-19T08:51:27.936965Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "image_summary_detector = ammico.SummaryDetector(image_dict, analysis_type=\"summary\", model_type=\"base\")"
@@ -7809,88 +485,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:51:27.945516Z",
-     "iopub.status.busy": "2024-02-19T08:51:27.945076Z",
-     "iopub.status.idle": "2024-02-19T08:52:37.388418Z",
-     "shell.execute_reply": "2024-02-19T08:52:37.387691Z"
-    }
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-    {
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-     "output_type": "stream",
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-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for num, key in tqdm(enumerate(image_dict.keys()),total=len(image_dict)):\n",
     "    image_dict[key] = image_summary_detector.analyse_image(subdict = image_dict[key], analysis_type=\"summary\")\n",
@@ -7909,15 +506,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:52:37.396555Z",
-     "iopub.status.busy": "2024-02-19T08:52:37.396110Z",
-     "iopub.status.idle": "2024-02-19T08:52:37.399491Z",
-     "shell.execute_reply": "2024-02-19T08:52:37.398848Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "list_of_questions = [\n",
@@ -7936,654 +526,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:52:37.405491Z",
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-     "iopub.status.idle": "2024-02-19T08:52:56.401148Z",
-     "shell.execute_reply": "2024-02-19T08:52:56.400055Z"
-    }
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-     "ename": "OSError",
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-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[21], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m image_summary_vqa_detector \u001b[38;5;241m=\u001b[39m \u001b[43mammico\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSummaryDetector\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43manalysis_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mquestions\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m                                                    \u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvqa\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m num, key \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28menumerate\u001b[39m(image_dict\u001b[38;5;241m.\u001b[39mkeys()),total\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(image_dict)):\n\u001b[1;32m      5\u001b[0m     image_dict[key] \u001b[38;5;241m=\u001b[39m image_summary_vqa_detector\u001b[38;5;241m.\u001b[39manalyse_image(subdict\u001b[38;5;241m=\u001b[39mimage_dict[key], \n\u001b[1;32m      6\u001b[0m                                                                analysis_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquestions\u001b[39m\u001b[38;5;124m\"\u001b[39m, \n\u001b[1;32m      7\u001b[0m                                                                list_of_questions \u001b[38;5;241m=\u001b[39m list_of_questions)\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/summary.py:141\u001b[0m, in \u001b[0;36mSummaryDetector.__init__\u001b[0;34m(self, subdict, model_type, analysis_type, list_of_questions, summary_model, summary_vis_processors, summary_vqa_model, summary_vqa_vis_processors, summary_vqa_txt_processors, summary_vqa_model_new, summary_vqa_vis_processors_new, summary_vqa_txt_processors_new, device_type)\u001b[0m\n\u001b[1;32m    127\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vis_processors \u001b[38;5;241m=\u001b[39m summary_vis_processors\n\u001b[1;32m    128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m    129\u001b[0m     model_type \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mallowed_model_types\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (summary_vqa_model \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    135\u001b[0m     )\n\u001b[1;32m    136\u001b[0m ):\n\u001b[1;32m    137\u001b[0m     (\n\u001b[1;32m    138\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_model,\n\u001b[1;32m    139\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_vis_processors,\n\u001b[1;32m    140\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_txt_processors,\n\u001b[0;32m--> 141\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_vqa_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    142\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    143\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_model \u001b[38;5;241m=\u001b[39m summary_vqa_model\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/summary.py:232\u001b[0m, in \u001b[0;36mSummaryDetector.load_vqa_model\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    216\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_vqa_model\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    217\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    218\u001b[0m \u001b[38;5;124;03m    Load blip_vqa model and preprocessors for visual and text inputs from lavis.models.\u001b[39;00m\n\u001b[1;32m    219\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    226\u001b[0m \n\u001b[1;32m    227\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m    228\u001b[0m     (\n\u001b[1;32m    229\u001b[0m         summary_vqa_model,\n\u001b[1;32m    230\u001b[0m         summary_vqa_vis_processors,\n\u001b[1;32m    231\u001b[0m         summary_vqa_txt_processors,\n\u001b[0;32m--> 232\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[43mload_model_and_preprocess\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    233\u001b[0m \u001b[43m        \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mblip_vqa\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    234\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvqav2\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    235\u001b[0m \u001b[43m        \u001b[49m\u001b[43mis_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    236\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msummary_device\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    237\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    238\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m summary_vqa_model, summary_vqa_vis_processors, summary_vqa_txt_processors\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/__init__.py:195\u001b[0m, in \u001b[0;36mload_model_and_preprocess\u001b[0;34m(name, model_type, is_eval, device)\u001b[0m\n\u001b[1;32m    192\u001b[0m model_cls \u001b[38;5;241m=\u001b[39m registry\u001b[38;5;241m.\u001b[39mget_model_class(name)\n\u001b[1;32m    194\u001b[0m \u001b[38;5;66;03m# load model\u001b[39;00m\n\u001b[0;32m--> 195\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_cls\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    197\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_eval:\n\u001b[1;32m    198\u001b[0m     model\u001b[38;5;241m.\u001b[39meval()\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/base_model.py:70\u001b[0m, in \u001b[0;36mBaseModel.from_pretrained\u001b[0;34m(cls, model_type)\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     61\u001b[0m \u001b[38;5;124;03mBuild a pretrained model from default configuration file, specified by model_type.\u001b[39;00m\n\u001b[1;32m     62\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[38;5;124;03m    - model (nn.Module): pretrained or finetuned model, depending on the configuration.\u001b[39;00m\n\u001b[1;32m     68\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     69\u001b[0m model_cfg \u001b[38;5;241m=\u001b[39m OmegaConf\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mdefault_config_path(model_type))\u001b[38;5;241m.\u001b[39mmodel\n\u001b[0;32m---> 70\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_cfg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/blip_models/blip_vqa.py:373\u001b[0m, in \u001b[0;36mBlipVQA.from_config\u001b[0;34m(cls, cfg)\u001b[0m\n\u001b[1;32m    364\u001b[0m max_txt_len \u001b[38;5;241m=\u001b[39m cfg\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax_txt_len\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m35\u001b[39m)\n\u001b[1;32m    366\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(\n\u001b[1;32m    367\u001b[0m     image_encoder\u001b[38;5;241m=\u001b[39mimage_encoder,\n\u001b[1;32m    368\u001b[0m     text_encoder\u001b[38;5;241m=\u001b[39mtext_encoder,\n\u001b[1;32m    369\u001b[0m     text_decoder\u001b[38;5;241m=\u001b[39mtext_decoder,\n\u001b[1;32m    370\u001b[0m     max_txt_len\u001b[38;5;241m=\u001b[39mmax_txt_len,\n\u001b[1;32m    371\u001b[0m )\n\u001b[0;32m--> 373\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_checkpoint_from_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcfg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    375\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/base_model.py:95\u001b[0m, in \u001b[0;36mBaseModel.load_checkpoint_from_config\u001b[0;34m(self, cfg, **kwargs)\u001b[0m\n\u001b[1;32m     91\u001b[0m     finetune_path \u001b[38;5;241m=\u001b[39m cfg\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfinetuned\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m     92\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m (\n\u001b[1;32m     93\u001b[0m         finetune_path \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     94\u001b[0m     ), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound load_finetuned is True, but finetune_path is None.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 95\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_checkpoint\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl_or_filename\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfinetune_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     96\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     97\u001b[0m     \u001b[38;5;66;03m# load pre-trained weights\u001b[39;00m\n\u001b[1;32m     98\u001b[0m     pretrain_path \u001b[38;5;241m=\u001b[39m cfg\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpretrained\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/base_model.py:37\u001b[0m, in \u001b[0;36mBaseModel.load_checkpoint\u001b[0;34m(self, url_or_filename)\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     31\u001b[0m \u001b[38;5;124;03mLoad from a finetuned checkpoint.\u001b[39;00m\n\u001b[1;32m     32\u001b[0m \n\u001b[1;32m     33\u001b[0m \u001b[38;5;124;03mThis should expect no mismatch in the model keys and the checkpoint keys.\u001b[39;00m\n\u001b[1;32m     34\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     36\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_url(url_or_filename):\n\u001b[0;32m---> 37\u001b[0m     cached_file \u001b[38;5;241m=\u001b[39m \u001b[43mdownload_cached_file\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     38\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl_or_filename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_hash\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m     39\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     40\u001b[0m     checkpoint \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mload(cached_file, map_location\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     41\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misfile(url_or_filename):\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/common/dist_utils.py:132\u001b[0m, in \u001b[0;36mdownload_cached_file\u001b[0;34m(url, check_hash, progress)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cached_file\n\u001b[1;32m    131\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_main_process():\n\u001b[0;32m--> 132\u001b[0m     \u001b[43mtimm_hub\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_cached_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_hash\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    134\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_dist_avail_and_initialized():\n\u001b[1;32m    135\u001b[0m     dist\u001b[38;5;241m.\u001b[39mbarrier()\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/timm/models/hub.py:51\u001b[0m, in \u001b[0;36mdownload_cached_file\u001b[0;34m(url, check_hash, progress)\u001b[0m\n\u001b[1;32m     49\u001b[0m         r \u001b[38;5;241m=\u001b[39m HASH_REGEX\u001b[38;5;241m.\u001b[39msearch(filename)  \u001b[38;5;66;03m# r is Optional[Match[str]]\u001b[39;00m\n\u001b[1;32m     50\u001b[0m         hash_prefix \u001b[38;5;241m=\u001b[39m r\u001b[38;5;241m.\u001b[39mgroup(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m r \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 51\u001b[0m     \u001b[43mdownload_url_to_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcached_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhash_prefix\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprogress\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cached_file\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/hub.py:636\u001b[0m, in \u001b[0;36mdownload_url_to_file\u001b[0;34m(url, dst, hash_prefix, progress)\u001b[0m\n\u001b[1;32m    634\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(buffer) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    635\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m--> 636\u001b[0m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    637\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m hash_prefix \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    638\u001b[0m     sha256\u001b[38;5;241m.\u001b[39mupdate(buffer)\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/tempfile.py:478\u001b[0m, in \u001b[0;36m_TemporaryFileWrapper.__getattr__.<locals>.func_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    476\u001b[0m \u001b[38;5;129m@_functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m    477\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfunc_wrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 478\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[0;31mOSError\u001b[0m: [Errno 28] No space left on device"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "image_summary_vqa_detector = ammico.SummaryDetector(image_dict, analysis_type=\"questions\", \n",
     "                                                    model_type=\"vqa\")\n",
@@ -8606,462 +551,9 @@
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-     "shell.execute_reply": "2024-02-19T08:53:18.175071Z"
-    }
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-    {
-     "ename": "OSError",
-     "evalue": "[Errno 28] No space left on device",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[22], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m image_summary_vqa_detector \u001b[38;5;241m=\u001b[39m \u001b[43mammico\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSummaryDetector\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43manalysis_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msummary_and_questions\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m                                                    \u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbase\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m num, key \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28menumerate\u001b[39m(image_dict\u001b[38;5;241m.\u001b[39mkeys()),total\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(image_dict)):\n\u001b[1;32m      4\u001b[0m     image_dict[key] \u001b[38;5;241m=\u001b[39m image_summary_vqa_detector\u001b[38;5;241m.\u001b[39manalyse_image(subdict\u001b[38;5;241m=\u001b[39mimage_dict[key], \n\u001b[1;32m      5\u001b[0m                                                                analysis_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msummary_and_questions\u001b[39m\u001b[38;5;124m\"\u001b[39m, \n\u001b[1;32m      6\u001b[0m                                                                list_of_questions \u001b[38;5;241m=\u001b[39m list_of_questions)\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/summary.py:141\u001b[0m, in \u001b[0;36mSummaryDetector.__init__\u001b[0;34m(self, subdict, model_type, analysis_type, list_of_questions, summary_model, summary_vis_processors, summary_vqa_model, summary_vqa_vis_processors, summary_vqa_txt_processors, summary_vqa_model_new, summary_vqa_vis_processors_new, summary_vqa_txt_processors_new, device_type)\u001b[0m\n\u001b[1;32m    127\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vis_processors \u001b[38;5;241m=\u001b[39m summary_vis_processors\n\u001b[1;32m    128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m    129\u001b[0m     model_type \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mallowed_model_types\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (summary_vqa_model \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    135\u001b[0m     )\n\u001b[1;32m    136\u001b[0m ):\n\u001b[1;32m    137\u001b[0m     (\n\u001b[1;32m    138\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_model,\n\u001b[1;32m    139\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_vis_processors,\n\u001b[1;32m    140\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_txt_processors,\n\u001b[0;32m--> 141\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_vqa_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    142\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    143\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_model \u001b[38;5;241m=\u001b[39m summary_vqa_model\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/summary.py:232\u001b[0m, in \u001b[0;36mSummaryDetector.load_vqa_model\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    216\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_vqa_model\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    217\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    218\u001b[0m \u001b[38;5;124;03m    Load blip_vqa model and preprocessors for visual and text inputs from lavis.models.\u001b[39;00m\n\u001b[1;32m    219\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    226\u001b[0m \n\u001b[1;32m    227\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m    228\u001b[0m     (\n\u001b[1;32m    229\u001b[0m         summary_vqa_model,\n\u001b[1;32m    230\u001b[0m         summary_vqa_vis_processors,\n\u001b[1;32m    231\u001b[0m         summary_vqa_txt_processors,\n\u001b[0;32m--> 232\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[43mload_model_and_preprocess\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    233\u001b[0m \u001b[43m        \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mblip_vqa\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    234\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvqav2\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    235\u001b[0m \u001b[43m        \u001b[49m\u001b[43mis_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    236\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msummary_device\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    237\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    238\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m summary_vqa_model, summary_vqa_vis_processors, summary_vqa_txt_processors\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/__init__.py:195\u001b[0m, in \u001b[0;36mload_model_and_preprocess\u001b[0;34m(name, model_type, is_eval, device)\u001b[0m\n\u001b[1;32m    192\u001b[0m model_cls \u001b[38;5;241m=\u001b[39m registry\u001b[38;5;241m.\u001b[39mget_model_class(name)\n\u001b[1;32m    194\u001b[0m \u001b[38;5;66;03m# load model\u001b[39;00m\n\u001b[0;32m--> 195\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_cls\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    197\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_eval:\n\u001b[1;32m    198\u001b[0m     model\u001b[38;5;241m.\u001b[39meval()\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/base_model.py:70\u001b[0m, in \u001b[0;36mBaseModel.from_pretrained\u001b[0;34m(cls, model_type)\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     61\u001b[0m \u001b[38;5;124;03mBuild a pretrained model from default configuration file, specified by model_type.\u001b[39;00m\n\u001b[1;32m     62\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[38;5;124;03m    - model (nn.Module): pretrained or finetuned model, depending on the configuration.\u001b[39;00m\n\u001b[1;32m     68\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     69\u001b[0m model_cfg \u001b[38;5;241m=\u001b[39m OmegaConf\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mdefault_config_path(model_type))\u001b[38;5;241m.\u001b[39mmodel\n\u001b[0;32m---> 70\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_cfg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/blip_models/blip_vqa.py:373\u001b[0m, in \u001b[0;36mBlipVQA.from_config\u001b[0;34m(cls, cfg)\u001b[0m\n\u001b[1;32m    364\u001b[0m max_txt_len \u001b[38;5;241m=\u001b[39m cfg\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax_txt_len\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m35\u001b[39m)\n\u001b[1;32m    366\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(\n\u001b[1;32m    367\u001b[0m     image_encoder\u001b[38;5;241m=\u001b[39mimage_encoder,\n\u001b[1;32m    368\u001b[0m     text_encoder\u001b[38;5;241m=\u001b[39mtext_encoder,\n\u001b[1;32m    369\u001b[0m     text_decoder\u001b[38;5;241m=\u001b[39mtext_decoder,\n\u001b[1;32m    370\u001b[0m     max_txt_len\u001b[38;5;241m=\u001b[39mmax_txt_len,\n\u001b[1;32m    371\u001b[0m )\n\u001b[0;32m--> 373\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_checkpoint_from_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcfg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    375\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/base_model.py:95\u001b[0m, in \u001b[0;36mBaseModel.load_checkpoint_from_config\u001b[0;34m(self, cfg, **kwargs)\u001b[0m\n\u001b[1;32m     91\u001b[0m     finetune_path \u001b[38;5;241m=\u001b[39m cfg\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfinetuned\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m     92\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m (\n\u001b[1;32m     93\u001b[0m         finetune_path \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     94\u001b[0m     ), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound load_finetuned is True, but finetune_path is None.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 95\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_checkpoint\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl_or_filename\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfinetune_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     96\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     97\u001b[0m     \u001b[38;5;66;03m# load pre-trained weights\u001b[39;00m\n\u001b[1;32m     98\u001b[0m     pretrain_path \u001b[38;5;241m=\u001b[39m cfg\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpretrained\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/base_model.py:37\u001b[0m, in \u001b[0;36mBaseModel.load_checkpoint\u001b[0;34m(self, url_or_filename)\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     31\u001b[0m \u001b[38;5;124;03mLoad from a finetuned checkpoint.\u001b[39;00m\n\u001b[1;32m     32\u001b[0m \n\u001b[1;32m     33\u001b[0m \u001b[38;5;124;03mThis should expect no mismatch in the model keys and the checkpoint keys.\u001b[39;00m\n\u001b[1;32m     34\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     36\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_url(url_or_filename):\n\u001b[0;32m---> 37\u001b[0m     cached_file \u001b[38;5;241m=\u001b[39m \u001b[43mdownload_cached_file\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     38\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl_or_filename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_hash\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m     39\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     40\u001b[0m     checkpoint \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mload(cached_file, map_location\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     41\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misfile(url_or_filename):\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/common/dist_utils.py:132\u001b[0m, in \u001b[0;36mdownload_cached_file\u001b[0;34m(url, check_hash, progress)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cached_file\n\u001b[1;32m    131\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_main_process():\n\u001b[0;32m--> 132\u001b[0m     \u001b[43mtimm_hub\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_cached_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_hash\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    134\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_dist_avail_and_initialized():\n\u001b[1;32m    135\u001b[0m     dist\u001b[38;5;241m.\u001b[39mbarrier()\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/timm/models/hub.py:51\u001b[0m, in \u001b[0;36mdownload_cached_file\u001b[0;34m(url, check_hash, progress)\u001b[0m\n\u001b[1;32m     49\u001b[0m         r \u001b[38;5;241m=\u001b[39m HASH_REGEX\u001b[38;5;241m.\u001b[39msearch(filename)  \u001b[38;5;66;03m# r is Optional[Match[str]]\u001b[39;00m\n\u001b[1;32m     50\u001b[0m         hash_prefix \u001b[38;5;241m=\u001b[39m r\u001b[38;5;241m.\u001b[39mgroup(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m r \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 51\u001b[0m     \u001b[43mdownload_url_to_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcached_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhash_prefix\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprogress\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cached_file\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/hub.py:636\u001b[0m, in \u001b[0;36mdownload_url_to_file\u001b[0;34m(url, dst, hash_prefix, progress)\u001b[0m\n\u001b[1;32m    634\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(buffer) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    635\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m--> 636\u001b[0m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    637\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m hash_prefix \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    638\u001b[0m     sha256\u001b[38;5;241m.\u001b[39mupdate(buffer)\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/tempfile.py:478\u001b[0m, in \u001b[0;36m_TemporaryFileWrapper.__getattr__.<locals>.func_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    476\u001b[0m \u001b[38;5;129m@_functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m    477\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfunc_wrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 478\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[0;31mOSError\u001b[0m: [Errno 28] No space left on device"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "image_summary_vqa_detector = ammico.SummaryDetector(image_dict, analysis_type=\"summary_and_questions\", \n",
     "                                                    model_type=\"base\")\n",
@@ -9097,792 +589,9 @@
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-    {
-     "ename": "OSError",
-     "evalue": "[Errno 28] No space left on device",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[23], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m obj \u001b[38;5;241m=\u001b[39m \u001b[43mammico\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSummaryDetector\u001b[49m\u001b[43m(\u001b[49m\u001b[43msubdict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mimage_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43manalysis_type\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msummary_and_questions\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mblip2_t5_caption_coco_flant5xl\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m# list of the new models that can be used:\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# \"blip2_t5_pretrain_flant5xxl\",\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# \"blip2_t5_pretrain_flant5xl\",\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     14\u001b[0m \n\u001b[1;32m     15\u001b[0m \u001b[38;5;66;03m#also you can perform all calculation on cpu if you set device_type= \"cpu\" or gpu if you set device_type= \"cuda\"\u001b[39;00m\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/summary.py:156\u001b[0m, in \u001b[0;36mSummaryDetector.__init__\u001b[0;34m(self, subdict, model_type, analysis_type, list_of_questions, summary_model, summary_vis_processors, summary_vqa_model, summary_vqa_vis_processors, summary_vqa_txt_processors, summary_vqa_model_new, summary_vqa_vis_processors_new, summary_vqa_txt_processors_new, device_type)\u001b[0m\n\u001b[1;32m    145\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_txt_processors \u001b[38;5;241m=\u001b[39m summary_vqa_txt_processors\n\u001b[1;32m    146\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m    147\u001b[0m     model_type \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mallowed_new_model_types\n\u001b[1;32m    148\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (summary_vqa_model_new \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    149\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (summary_vqa_vis_processors_new \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    150\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (summary_vqa_txt_processors_new \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    151\u001b[0m ):\n\u001b[1;32m    152\u001b[0m     (\n\u001b[1;32m    153\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_model_new,\n\u001b[1;32m    154\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_vis_processors_new,\n\u001b[1;32m    155\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_txt_processors_new,\n\u001b[0;32m--> 156\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_new_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    157\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    158\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_model_new \u001b[38;5;241m=\u001b[39m summary_vqa_model_new\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/summary.py:479\u001b[0m, in \u001b[0;36mSummaryDetector.load_new_model\u001b[0;34m(self, model_type)\u001b[0m\n\u001b[1;32m    455\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    456\u001b[0m \u001b[38;5;124;03mLoad new BLIP2 models.\u001b[39;00m\n\u001b[1;32m    457\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    464\u001b[0m \u001b[38;5;124;03m    txt_processors (dict): preprocessors for text inputs.\u001b[39;00m\n\u001b[1;32m    465\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    466\u001b[0m select_model \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    467\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mblip2_t5_pretrain_flant5xxl\u001b[39m\u001b[38;5;124m\"\u001b[39m: SummaryDetector\u001b[38;5;241m.\u001b[39mload_model_blip2_t5_pretrain_flant5xxl,\n\u001b[1;32m    468\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mblip2_t5_pretrain_flant5xl\u001b[39m\u001b[38;5;124m\"\u001b[39m: SummaryDetector\u001b[38;5;241m.\u001b[39mload_model_blip2_t5_pretrain_flant5xl,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    473\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mblip2_opt_caption_coco_opt6.7b\u001b[39m\u001b[38;5;124m\"\u001b[39m: SummaryDetector\u001b[38;5;241m.\u001b[39mload_model_base_blip2_opt_caption_coco_opt67b,\n\u001b[1;32m    474\u001b[0m }\n\u001b[1;32m    475\u001b[0m (\n\u001b[1;32m    476\u001b[0m     summary_vqa_model,\n\u001b[1;32m    477\u001b[0m     summary_vqa_vis_processors,\n\u001b[1;32m    478\u001b[0m     summary_vqa_txt_processors,\n\u001b[0;32m--> 479\u001b[0m ) \u001b[38;5;241m=\u001b[39m \u001b[43mselect_model\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    480\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m summary_vqa_model, summary_vqa_vis_processors, summary_vqa_txt_processors\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/summary.py:543\u001b[0m, in \u001b[0;36mSummaryDetector.load_model_blip2_t5_caption_coco_flant5xl\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    528\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_model_blip2_t5_caption_coco_flant5xl\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    529\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    530\u001b[0m \u001b[38;5;124;03m    Load BLIP2 model with caption_coco_flant5xl architecture.\u001b[39;00m\n\u001b[1;32m    531\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    537\u001b[0m \u001b[38;5;124;03m        txt_processors (dict): preprocessors for text inputs.\u001b[39;00m\n\u001b[1;32m    538\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m    539\u001b[0m     (\n\u001b[1;32m    540\u001b[0m         summary_vqa_model,\n\u001b[1;32m    541\u001b[0m         summary_vqa_vis_processors,\n\u001b[1;32m    542\u001b[0m         summary_vqa_txt_processors,\n\u001b[0;32m--> 543\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[43mload_model_and_preprocess\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    544\u001b[0m \u001b[43m        \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mblip2_t5\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    545\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcaption_coco_flant5xl\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    546\u001b[0m \u001b[43m        \u001b[49m\u001b[43mis_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    547\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msummary_device\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    548\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    549\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m summary_vqa_model, summary_vqa_vis_processors, summary_vqa_txt_processors\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/__init__.py:195\u001b[0m, in \u001b[0;36mload_model_and_preprocess\u001b[0;34m(name, model_type, is_eval, device)\u001b[0m\n\u001b[1;32m    192\u001b[0m model_cls \u001b[38;5;241m=\u001b[39m registry\u001b[38;5;241m.\u001b[39mget_model_class(name)\n\u001b[1;32m    194\u001b[0m \u001b[38;5;66;03m# load model\u001b[39;00m\n\u001b[0;32m--> 195\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_cls\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    197\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_eval:\n\u001b[1;32m    198\u001b[0m     model\u001b[38;5;241m.\u001b[39meval()\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/base_model.py:70\u001b[0m, in \u001b[0;36mBaseModel.from_pretrained\u001b[0;34m(cls, model_type)\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     61\u001b[0m \u001b[38;5;124;03mBuild a pretrained model from default configuration file, specified by model_type.\u001b[39;00m\n\u001b[1;32m     62\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[38;5;124;03m    - model (nn.Module): pretrained or finetuned model, depending on the configuration.\u001b[39;00m\n\u001b[1;32m     68\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     69\u001b[0m model_cfg \u001b[38;5;241m=\u001b[39m OmegaConf\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mdefault_config_path(model_type))\u001b[38;5;241m.\u001b[39mmodel\n\u001b[0;32m---> 70\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_cfg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/blip2_models/blip2_t5.py:368\u001b[0m, in \u001b[0;36mBlip2T5.from_config\u001b[0;34m(cls, cfg)\u001b[0m\n\u001b[1;32m    364\u001b[0m max_txt_len \u001b[38;5;241m=\u001b[39m cfg\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax_txt_len\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m32\u001b[39m)\n\u001b[1;32m    366\u001b[0m apply_lemmatizer \u001b[38;5;241m=\u001b[39m cfg\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply_lemmatizer\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m--> 368\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m    369\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvit_model\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvit_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    370\u001b[0m \u001b[43m    \u001b[49m\u001b[43mimg_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mimg_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    371\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdrop_path_rate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrop_path_rate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    372\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_grad_checkpoint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_grad_checkpoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    373\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvit_precision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvit_precision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    374\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfreeze_vit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfreeze_vit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    375\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnum_query_token\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_query_token\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    376\u001b[0m \u001b[43m    \u001b[49m\u001b[43mt5_model\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mt5_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    377\u001b[0m \u001b[43m    \u001b[49m\u001b[43mprompt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprompt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    378\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_txt_len\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_txt_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    379\u001b[0m \u001b[43m    \u001b[49m\u001b[43mapply_lemmatizer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mapply_lemmatizer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    380\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    381\u001b[0m model\u001b[38;5;241m.\u001b[39mload_checkpoint_from_config(cfg)\n\u001b[1;32m    383\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/blip2_models/blip2_t5.py:61\u001b[0m, in \u001b[0;36mBlip2T5.__init__\u001b[0;34m(self, vit_model, img_size, drop_path_rate, use_grad_checkpoint, vit_precision, freeze_vit, num_query_token, t5_model, prompt, max_txt_len, apply_lemmatizer)\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m()\n\u001b[1;32m     59\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtokenizer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minit_tokenizer()\n\u001b[0;32m---> 61\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvisual_encoder, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mln_vision \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minit_vision_encoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     62\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvit_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mimg_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdrop_path_rate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_grad_checkpoint\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvit_precision\u001b[49m\n\u001b[1;32m     63\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     64\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m freeze_vit:\n\u001b[1;32m     65\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m name, param \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvisual_encoder\u001b[38;5;241m.\u001b[39mnamed_parameters():\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/blip2_models/blip2.py:72\u001b[0m, in \u001b[0;36mBlip2Base.init_vision_encoder\u001b[0;34m(cls, model_name, img_size, drop_path_rate, use_grad_checkpoint, precision)\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m model_name \u001b[38;5;129;01min\u001b[39;00m [\n\u001b[1;32m     68\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meva_clip_g\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclip_L\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     70\u001b[0m ], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvit model must be eva_clip_g or clip_L\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m model_name \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meva_clip_g\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m---> 72\u001b[0m     visual_encoder \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_eva_vit_g\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[43m        \u001b[49m\u001b[43mimg_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdrop_path_rate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_grad_checkpoint\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprecision\u001b[49m\n\u001b[1;32m     74\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     75\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m model_name \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclip_L\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m     76\u001b[0m     visual_encoder \u001b[38;5;241m=\u001b[39m create_clip_vit_L(img_size, use_grad_checkpoint, precision)\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/eva_vit.py:430\u001b[0m, in \u001b[0;36mcreate_eva_vit_g\u001b[0;34m(img_size, drop_path_rate, use_checkpoint, precision)\u001b[0m\n\u001b[1;32m    416\u001b[0m model \u001b[38;5;241m=\u001b[39m VisionTransformer(\n\u001b[1;32m    417\u001b[0m     img_size\u001b[38;5;241m=\u001b[39mimg_size,\n\u001b[1;32m    418\u001b[0m     patch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m14\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    427\u001b[0m     use_checkpoint\u001b[38;5;241m=\u001b[39muse_checkpoint,\n\u001b[1;32m    428\u001b[0m )  \n\u001b[1;32m    429\u001b[0m url \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/eva_vit_g.pth\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 430\u001b[0m cached_file \u001b[38;5;241m=\u001b[39m \u001b[43mdownload_cached_file\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    431\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_hash\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m    432\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    433\u001b[0m state_dict \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mload(cached_file, map_location\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m)    \n\u001b[1;32m    434\u001b[0m interpolate_pos_embed(model,state_dict)\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/common/dist_utils.py:132\u001b[0m, in \u001b[0;36mdownload_cached_file\u001b[0;34m(url, check_hash, progress)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cached_file\n\u001b[1;32m    131\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_main_process():\n\u001b[0;32m--> 132\u001b[0m     \u001b[43mtimm_hub\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_cached_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_hash\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    134\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_dist_avail_and_initialized():\n\u001b[1;32m    135\u001b[0m     dist\u001b[38;5;241m.\u001b[39mbarrier()\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/timm/models/hub.py:51\u001b[0m, in \u001b[0;36mdownload_cached_file\u001b[0;34m(url, check_hash, progress)\u001b[0m\n\u001b[1;32m     49\u001b[0m         r \u001b[38;5;241m=\u001b[39m HASH_REGEX\u001b[38;5;241m.\u001b[39msearch(filename)  \u001b[38;5;66;03m# r is Optional[Match[str]]\u001b[39;00m\n\u001b[1;32m     50\u001b[0m         hash_prefix \u001b[38;5;241m=\u001b[39m r\u001b[38;5;241m.\u001b[39mgroup(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m r \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 51\u001b[0m     \u001b[43mdownload_url_to_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcached_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhash_prefix\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprogress\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cached_file\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/hub.py:636\u001b[0m, in \u001b[0;36mdownload_url_to_file\u001b[0;34m(url, dst, hash_prefix, progress)\u001b[0m\n\u001b[1;32m    634\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(buffer) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    635\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m--> 636\u001b[0m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    637\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m hash_prefix \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    638\u001b[0m     sha256\u001b[38;5;241m.\u001b[39mupdate(buffer)\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/tempfile.py:478\u001b[0m, in \u001b[0;36m_TemporaryFileWrapper.__getattr__.<locals>.func_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    476\u001b[0m \u001b[38;5;129m@_functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m    477\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfunc_wrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 478\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[0;31mOSError\u001b[0m: [Errno 28] No space left on device"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "obj = ammico.SummaryDetector(subdict=image_dict, analysis_type = \"summary_and_questions\", model_type = \"blip2_t5_caption_coco_flant5xl\")\n",
     "# list of the new models that can be used:\n",
@@ -9903,28 +612,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:53:49.445792Z",
-     "iopub.status.busy": "2024-02-19T08:53:49.445585Z",
-     "iopub.status.idle": "2024-02-19T08:53:49.470611Z",
-     "shell.execute_reply": "2024-02-19T08:53:49.470071Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'obj' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[24], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m image_dict:\n\u001b[0;32m----> 2\u001b[0m     image_dict[key] \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241m.\u001b[39manalyse_image(subdict \u001b[38;5;241m=\u001b[39m image_dict[key], analysis_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msummary_and_questions\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# analysis_type can be \u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m# \"summary\",\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m# \"questions\",\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# \"summary_and_questions\".\u001b[39;00m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'obj' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for key in image_dict:\n",
     "    image_dict[key] = obj.analyse_image(subdict = image_dict[key], analysis_type=\"summary_and_questions\")\n",
@@ -9937,171 +627,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:53:49.473138Z",
-     "iopub.status.busy": "2024-02-19T08:53:49.472942Z",
-     "iopub.status.idle": "2024-02-19T08:53:49.480775Z",
-     "shell.execute_reply": "2024-02-19T08:53:49.480208Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'img4': {'filename': 'data-test/img4.png',\n",
-       "  'face': 'No',\n",
-       "  'multiple_faces': 'No',\n",
-       "  'no_faces': 0,\n",
-       "  'wears_mask': ['No'],\n",
-       "  'age': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'emotion': [None],\n",
-       "  'emotion (category)': [None],\n",
-       "  'text': 'MOODOVIN XI',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'MOODOVIN XI',\n",
-       "  'text_clean': 'XI',\n",
-       "  'text_summary': ' MOODOVIN XI XI: Vladimir Putin, Vladimir Vladmir Zelizer, Vladimir',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 0.66,\n",
-       "  'entity': ['MOODOVIN XI'],\n",
-       "  'entity_type': ['ORG'],\n",
-       "  'const_image_summary': 'a river running through a city next to tall buildings',\n",
-       "  '3_non-deterministic_summary': ['there is a pretty house that sits above the water',\n",
-       "   'there is a building with a balcony and lots of plants on the side of it',\n",
-       "   'several buildings with a river flowing through it']},\n",
-       " 'img1': {'filename': 'data-test/img1.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions JOHN R. TAYLOR University of Colorado',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions JOHN R. TAYLOR University of Colorado',\n",
-       "  'text_clean': 'THEORY The Quantum Theory of Collisions JOHN R. TAYLOR University of Colorado',\n",
-       "  'text_summary': ' SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 0.91,\n",
-       "  'entity': ['Non',\n",
-       "   '##vist',\n",
-       "   'Col',\n",
-       "   '##N',\n",
-       "   'R',\n",
-       "   'T',\n",
-       "   '##AYL',\n",
-       "   'University of Colorado'],\n",
-       "  'entity_type': ['MISC', 'MISC', 'MISC', 'ORG', 'PER', 'PER', 'ORG', 'ORG'],\n",
-       "  'const_image_summary': 'a close up of a piece of paper with writing on it',\n",
-       "  '3_non-deterministic_summary': ['a book opened to the book title for a novel',\n",
-       "   'there are many text on this page',\n",
-       "   'the text in a book is a handwritten poem']},\n",
-       " 'img2': {'filename': 'data-test/img2.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J.. H. WILKINSON',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J.. H. WILKINSON',\n",
-       "  'text_clean': 'THE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J .. H. WILKINSON',\n",
-       "  'text_summary': ' H. H. W. WILKINSON: The Algebri',\n",
-       "  'sentiment': 'NEGATIVE',\n",
-       "  'sentiment_score': 0.97,\n",
-       "  'entity': ['ALGEBRAIC EIGENVAL', 'NVS TIO MI', 'J', 'H', 'WILKINSON'],\n",
-       "  'entity_type': ['MISC', 'ORG', 'ORG', 'ORG', 'ORG'],\n",
-       "  'const_image_summary': 'a yellow book with green lettering on it',\n",
-       "  '3_non-deterministic_summary': ['a book cover with green writing on a black background',\n",
-       "   'the title page of a book with information from its authors',\n",
-       "   'a book about the age - related engineering and engineering']},\n",
-       " 'img3': {'filename': 'data-test/img3.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'm OOOO 0000 www.',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'm OOOO 0000 www.',\n",
-       "  'text_clean': 'm www .',\n",
-       "  'text_summary': ' www. m OOOO 0000 0000 www.m.m OOOo 0000',\n",
-       "  'sentiment': 'NEGATIVE',\n",
-       "  'sentiment_score': 0.62,\n",
-       "  'entity': [],\n",
-       "  'entity_type': [],\n",
-       "  'const_image_summary': 'a bus that is sitting on the side of a road',\n",
-       "  '3_non-deterministic_summary': ['there are cars and a bus on the side of the road',\n",
-       "   'a bus that is sitting in the middle of a street',\n",
-       "   'an aerial view of an empty city street with two large buses passing by']},\n",
-       " 'img0': {'filename': 'data-test/img0.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'Mathematische Formelsammlung für Ingenieure und Naturwissenschaftler Mit zahlreichen Abbildungen und Rechenbeispielen und einer ausführlichen Integraltafel 3., verbesserte Auflage',\n",
-       "  'text_language': 'de',\n",
-       "  'text_english': 'Mathematical formula collection for engineers and scientists With numerous illustrations and calculation examples and a detailed integral table 3rd, improved edition',\n",
-       "  'text_clean': 'Mathematical formula collection for engineers and scientists With numerous illustrations and calculation examples and a detailed integral table 3rd , improved edition',\n",
-       "  'text_summary': ' Mathematical formula collection for engineers and scientists . Includes numerous illustrations and calculation examples . Includes',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 1.0,\n",
-       "  'entity': [],\n",
-       "  'entity_type': [],\n",
-       "  'const_image_summary': 'a close up of an open book with writing on it',\n",
-       "  '3_non-deterministic_summary': ['a close up of a book with many languages',\n",
-       "   'a book that is opened up in german',\n",
-       "   'book about mathemarche formulals and their meaning']},\n",
-       " 'img5': {'filename': 'data-test/img5.png',\n",
-       "  'no_faces': 1,\n",
-       "  'age': [26],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': ['Negative'],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': ['sad'],\n",
-       "  'gender': ['Man'],\n",
-       "  'race': [None],\n",
-       "  'face': 'Yes',\n",
-       "  'text': None,\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': '',\n",
-       "  'text_clean': '',\n",
-       "  'text_summary': ' CNN.com will feature iReporter photos in a weekly Travel Snapshots gallery .',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 0.75,\n",
-       "  'entity': [],\n",
-       "  'entity_type': [],\n",
-       "  'const_image_summary': 'a person running on a beach near a rock formation',\n",
-       "  '3_non-deterministic_summary': ['a woman is running down the beach next to some rocks',\n",
-       "   'a woman running along the beach by the ocean',\n",
-       "   'there is a person running on the beach next to the ocean']}}"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "image_dict"
    ]
@@ -10117,15 +645,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:53:49.483664Z",
-     "iopub.status.busy": "2024-02-19T08:53:49.483466Z",
-     "iopub.status.idle": "2024-02-19T08:53:49.486232Z",
-     "shell.execute_reply": "2024-02-19T08:53:49.485670Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "list_of_questions = [\n",
@@ -10136,28 +657,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:53:49.488981Z",
-     "iopub.status.busy": "2024-02-19T08:53:49.488787Z",
-     "iopub.status.idle": "2024-02-19T08:53:49.509942Z",
-     "shell.execute_reply": "2024-02-19T08:53:49.509438Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'obj' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[27], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m image_dict:\n\u001b[0;32m----> 2\u001b[0m     image_dict[key] \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241m.\u001b[39manalyse_image(subdict \u001b[38;5;241m=\u001b[39m image_dict[key], analysis_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquestions\u001b[39m\u001b[38;5;124m\"\u001b[39m, list_of_questions\u001b[38;5;241m=\u001b[39mlist_of_questions)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'obj' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for key in image_dict:\n",
     "    image_dict[key] = obj.analyse_image(subdict = image_dict[key], analysis_type=\"questions\", list_of_questions=list_of_questions)"
@@ -10174,15 +676,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:53:49.513195Z",
-     "iopub.status.busy": "2024-02-19T08:53:49.512999Z",
-     "iopub.status.idle": "2024-02-19T08:53:49.515828Z",
-     "shell.execute_reply": "2024-02-19T08:53:49.515286Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "list_of_questions = [\n",
@@ -10193,28 +688,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:53:49.520071Z",
-     "iopub.status.busy": "2024-02-19T08:53:49.519734Z",
-     "iopub.status.idle": "2024-02-19T08:53:49.540462Z",
-     "shell.execute_reply": "2024-02-19T08:53:49.539940Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'obj' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[29], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m image_dict:\n\u001b[0;32m----> 2\u001b[0m     image_dict[key] \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241m.\u001b[39manalyse_image(subdict \u001b[38;5;241m=\u001b[39m image_dict[key], analysis_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquestions\u001b[39m\u001b[38;5;124m\"\u001b[39m, list_of_questions\u001b[38;5;241m=\u001b[39mlist_of_questions)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'obj' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for key in image_dict:\n",
     "    image_dict[key] = obj.analyse_image(subdict = image_dict[key], analysis_type=\"questions\", list_of_questions=list_of_questions)"
@@ -10222,171 +698,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:53:49.544909Z",
-     "iopub.status.busy": "2024-02-19T08:53:49.544431Z",
-     "iopub.status.idle": "2024-02-19T08:53:49.551819Z",
-     "shell.execute_reply": "2024-02-19T08:53:49.551270Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'img4': {'filename': 'data-test/img4.png',\n",
-       "  'face': 'No',\n",
-       "  'multiple_faces': 'No',\n",
-       "  'no_faces': 0,\n",
-       "  'wears_mask': ['No'],\n",
-       "  'age': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'emotion': [None],\n",
-       "  'emotion (category)': [None],\n",
-       "  'text': 'MOODOVIN XI',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'MOODOVIN XI',\n",
-       "  'text_clean': 'XI',\n",
-       "  'text_summary': ' MOODOVIN XI XI: Vladimir Putin, Vladimir Vladmir Zelizer, Vladimir',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 0.66,\n",
-       "  'entity': ['MOODOVIN XI'],\n",
-       "  'entity_type': ['ORG'],\n",
-       "  'const_image_summary': 'a river running through a city next to tall buildings',\n",
-       "  '3_non-deterministic_summary': ['there is a pretty house that sits above the water',\n",
-       "   'there is a building with a balcony and lots of plants on the side of it',\n",
-       "   'several buildings with a river flowing through it']},\n",
-       " 'img1': {'filename': 'data-test/img1.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions JOHN R. TAYLOR University of Colorado',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions JOHN R. TAYLOR University of Colorado',\n",
-       "  'text_clean': 'THEORY The Quantum Theory of Collisions JOHN R. TAYLOR University of Colorado',\n",
-       "  'text_summary': ' SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 0.91,\n",
-       "  'entity': ['Non',\n",
-       "   '##vist',\n",
-       "   'Col',\n",
-       "   '##N',\n",
-       "   'R',\n",
-       "   'T',\n",
-       "   '##AYL',\n",
-       "   'University of Colorado'],\n",
-       "  'entity_type': ['MISC', 'MISC', 'MISC', 'ORG', 'PER', 'PER', 'ORG', 'ORG'],\n",
-       "  'const_image_summary': 'a close up of a piece of paper with writing on it',\n",
-       "  '3_non-deterministic_summary': ['a book opened to the book title for a novel',\n",
-       "   'there are many text on this page',\n",
-       "   'the text in a book is a handwritten poem']},\n",
-       " 'img2': {'filename': 'data-test/img2.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J.. H. WILKINSON',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J.. H. WILKINSON',\n",
-       "  'text_clean': 'THE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J .. H. WILKINSON',\n",
-       "  'text_summary': ' H. H. W. WILKINSON: The Algebri',\n",
-       "  'sentiment': 'NEGATIVE',\n",
-       "  'sentiment_score': 0.97,\n",
-       "  'entity': ['ALGEBRAIC EIGENVAL', 'NVS TIO MI', 'J', 'H', 'WILKINSON'],\n",
-       "  'entity_type': ['MISC', 'ORG', 'ORG', 'ORG', 'ORG'],\n",
-       "  'const_image_summary': 'a yellow book with green lettering on it',\n",
-       "  '3_non-deterministic_summary': ['a book cover with green writing on a black background',\n",
-       "   'the title page of a book with information from its authors',\n",
-       "   'a book about the age - related engineering and engineering']},\n",
-       " 'img3': {'filename': 'data-test/img3.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'm OOOO 0000 www.',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'm OOOO 0000 www.',\n",
-       "  'text_clean': 'm www .',\n",
-       "  'text_summary': ' www. m OOOO 0000 0000 www.m.m OOOo 0000',\n",
-       "  'sentiment': 'NEGATIVE',\n",
-       "  'sentiment_score': 0.62,\n",
-       "  'entity': [],\n",
-       "  'entity_type': [],\n",
-       "  'const_image_summary': 'a bus that is sitting on the side of a road',\n",
-       "  '3_non-deterministic_summary': ['there are cars and a bus on the side of the road',\n",
-       "   'a bus that is sitting in the middle of a street',\n",
-       "   'an aerial view of an empty city street with two large buses passing by']},\n",
-       " 'img0': {'filename': 'data-test/img0.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'Mathematische Formelsammlung für Ingenieure und Naturwissenschaftler Mit zahlreichen Abbildungen und Rechenbeispielen und einer ausführlichen Integraltafel 3., verbesserte Auflage',\n",
-       "  'text_language': 'de',\n",
-       "  'text_english': 'Mathematical formula collection for engineers and scientists With numerous illustrations and calculation examples and a detailed integral table 3rd, improved edition',\n",
-       "  'text_clean': 'Mathematical formula collection for engineers and scientists With numerous illustrations and calculation examples and a detailed integral table 3rd , improved edition',\n",
-       "  'text_summary': ' Mathematical formula collection for engineers and scientists . Includes numerous illustrations and calculation examples . Includes',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 1.0,\n",
-       "  'entity': [],\n",
-       "  'entity_type': [],\n",
-       "  'const_image_summary': 'a close up of an open book with writing on it',\n",
-       "  '3_non-deterministic_summary': ['a close up of a book with many languages',\n",
-       "   'a book that is opened up in german',\n",
-       "   'book about mathemarche formulals and their meaning']},\n",
-       " 'img5': {'filename': 'data-test/img5.png',\n",
-       "  'no_faces': 1,\n",
-       "  'age': [26],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': ['Negative'],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': ['sad'],\n",
-       "  'gender': ['Man'],\n",
-       "  'race': [None],\n",
-       "  'face': 'Yes',\n",
-       "  'text': None,\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': '',\n",
-       "  'text_clean': '',\n",
-       "  'text_summary': ' CNN.com will feature iReporter photos in a weekly Travel Snapshots gallery .',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 0.75,\n",
-       "  'entity': [],\n",
-       "  'entity_type': [],\n",
-       "  'const_image_summary': 'a person running on a beach near a rock formation',\n",
-       "  '3_non-deterministic_summary': ['a woman is running down the beach next to some rocks',\n",
-       "   'a woman running along the beach by the ocean',\n",
-       "   'there is a person running on the beach next to the ocean']}}"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "image_dict"
    ]
@@ -10400,15 +714,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:53:49.554718Z",
-     "iopub.status.busy": "2024-02-19T08:53:49.554288Z",
-     "iopub.status.idle": "2024-02-19T08:53:49.557147Z",
-     "shell.execute_reply": "2024-02-19T08:53:49.556606Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "list_of_questions = [\n",
@@ -10419,28 +726,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:53:49.559854Z",
-     "iopub.status.busy": "2024-02-19T08:53:49.559416Z",
-     "iopub.status.idle": "2024-02-19T08:53:49.581420Z",
-     "shell.execute_reply": "2024-02-19T08:53:49.580718Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'obj' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[32], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m image_dict:\n\u001b[0;32m----> 2\u001b[0m     image_dict[key] \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241m.\u001b[39manalyse_image(subdict \u001b[38;5;241m=\u001b[39m image_dict[key], analysis_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquestions\u001b[39m\u001b[38;5;124m\"\u001b[39m, list_of_questions\u001b[38;5;241m=\u001b[39mlist_of_questions, consequential_questions\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'obj' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for key in image_dict:\n",
     "    image_dict[key] = obj.analyse_image(subdict = image_dict[key], analysis_type=\"questions\", list_of_questions=list_of_questions, consequential_questions=True)"
@@ -10448,171 +736,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:53:49.584997Z",
-     "iopub.status.busy": "2024-02-19T08:53:49.584485Z",
-     "iopub.status.idle": "2024-02-19T08:53:49.592760Z",
-     "shell.execute_reply": "2024-02-19T08:53:49.592039Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'img4': {'filename': 'data-test/img4.png',\n",
-       "  'face': 'No',\n",
-       "  'multiple_faces': 'No',\n",
-       "  'no_faces': 0,\n",
-       "  'wears_mask': ['No'],\n",
-       "  'age': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'emotion': [None],\n",
-       "  'emotion (category)': [None],\n",
-       "  'text': 'MOODOVIN XI',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'MOODOVIN XI',\n",
-       "  'text_clean': 'XI',\n",
-       "  'text_summary': ' MOODOVIN XI XI: Vladimir Putin, Vladimir Vladmir Zelizer, Vladimir',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 0.66,\n",
-       "  'entity': ['MOODOVIN XI'],\n",
-       "  'entity_type': ['ORG'],\n",
-       "  'const_image_summary': 'a river running through a city next to tall buildings',\n",
-       "  '3_non-deterministic_summary': ['there is a pretty house that sits above the water',\n",
-       "   'there is a building with a balcony and lots of plants on the side of it',\n",
-       "   'several buildings with a river flowing through it']},\n",
-       " 'img1': {'filename': 'data-test/img1.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions JOHN R. TAYLOR University of Colorado',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions JOHN R. TAYLOR University of Colorado',\n",
-       "  'text_clean': 'THEORY The Quantum Theory of Collisions JOHN R. TAYLOR University of Colorado',\n",
-       "  'text_summary': ' SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 0.91,\n",
-       "  'entity': ['Non',\n",
-       "   '##vist',\n",
-       "   'Col',\n",
-       "   '##N',\n",
-       "   'R',\n",
-       "   'T',\n",
-       "   '##AYL',\n",
-       "   'University of Colorado'],\n",
-       "  'entity_type': ['MISC', 'MISC', 'MISC', 'ORG', 'PER', 'PER', 'ORG', 'ORG'],\n",
-       "  'const_image_summary': 'a close up of a piece of paper with writing on it',\n",
-       "  '3_non-deterministic_summary': ['a book opened to the book title for a novel',\n",
-       "   'there are many text on this page',\n",
-       "   'the text in a book is a handwritten poem']},\n",
-       " 'img2': {'filename': 'data-test/img2.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J.. H. WILKINSON',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J.. H. WILKINSON',\n",
-       "  'text_clean': 'THE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J .. H. WILKINSON',\n",
-       "  'text_summary': ' H. H. W. WILKINSON: The Algebri',\n",
-       "  'sentiment': 'NEGATIVE',\n",
-       "  'sentiment_score': 0.97,\n",
-       "  'entity': ['ALGEBRAIC EIGENVAL', 'NVS TIO MI', 'J', 'H', 'WILKINSON'],\n",
-       "  'entity_type': ['MISC', 'ORG', 'ORG', 'ORG', 'ORG'],\n",
-       "  'const_image_summary': 'a yellow book with green lettering on it',\n",
-       "  '3_non-deterministic_summary': ['a book cover with green writing on a black background',\n",
-       "   'the title page of a book with information from its authors',\n",
-       "   'a book about the age - related engineering and engineering']},\n",
-       " 'img3': {'filename': 'data-test/img3.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'm OOOO 0000 www.',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'm OOOO 0000 www.',\n",
-       "  'text_clean': 'm www .',\n",
-       "  'text_summary': ' www. m OOOO 0000 0000 www.m.m OOOo 0000',\n",
-       "  'sentiment': 'NEGATIVE',\n",
-       "  'sentiment_score': 0.62,\n",
-       "  'entity': [],\n",
-       "  'entity_type': [],\n",
-       "  'const_image_summary': 'a bus that is sitting on the side of a road',\n",
-       "  '3_non-deterministic_summary': ['there are cars and a bus on the side of the road',\n",
-       "   'a bus that is sitting in the middle of a street',\n",
-       "   'an aerial view of an empty city street with two large buses passing by']},\n",
-       " 'img0': {'filename': 'data-test/img0.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'Mathematische Formelsammlung für Ingenieure und Naturwissenschaftler Mit zahlreichen Abbildungen und Rechenbeispielen und einer ausführlichen Integraltafel 3., verbesserte Auflage',\n",
-       "  'text_language': 'de',\n",
-       "  'text_english': 'Mathematical formula collection for engineers and scientists With numerous illustrations and calculation examples and a detailed integral table 3rd, improved edition',\n",
-       "  'text_clean': 'Mathematical formula collection for engineers and scientists With numerous illustrations and calculation examples and a detailed integral table 3rd , improved edition',\n",
-       "  'text_summary': ' Mathematical formula collection for engineers and scientists . Includes numerous illustrations and calculation examples . Includes',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 1.0,\n",
-       "  'entity': [],\n",
-       "  'entity_type': [],\n",
-       "  'const_image_summary': 'a close up of an open book with writing on it',\n",
-       "  '3_non-deterministic_summary': ['a close up of a book with many languages',\n",
-       "   'a book that is opened up in german',\n",
-       "   'book about mathemarche formulals and their meaning']},\n",
-       " 'img5': {'filename': 'data-test/img5.png',\n",
-       "  'no_faces': 1,\n",
-       "  'age': [26],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': ['Negative'],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': ['sad'],\n",
-       "  'gender': ['Man'],\n",
-       "  'race': [None],\n",
-       "  'face': 'Yes',\n",
-       "  'text': None,\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': '',\n",
-       "  'text_clean': '',\n",
-       "  'text_summary': ' CNN.com will feature iReporter photos in a weekly Travel Snapshots gallery .',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 0.75,\n",
-       "  'entity': [],\n",
-       "  'entity_type': [],\n",
-       "  'const_image_summary': 'a person running on a beach near a rock formation',\n",
-       "  '3_non-deterministic_summary': ['a woman is running down the beach next to some rocks',\n",
-       "   'a woman running along the beach by the ocean',\n",
-       "   'there is a person running on the beach next to the ocean']}}"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "image_dict"
    ]
@@ -10640,97 +766,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:53:49.596180Z",
-     "iopub.status.busy": "2024-02-19T08:53:49.595807Z",
-     "iopub.status.idle": "2024-02-19T08:54:11.388417Z",
-     "shell.execute_reply": "2024-02-19T08:54:11.387832Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "1/1 [==============================] - ETA: 0s"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "1/1 [==============================] - 1s 535ms/step\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "1/1 [==============================] - ETA: 0s"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "1/1 [==============================] - 0s 343ms/step\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "1/1 [==============================] - ETA: 0s"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "1/1 [==============================] - 0s 226ms/step\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "1/1 [==============================] - ETA: 0s"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "1/1 [==============================] - 0s 233ms/step\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "1/1 [==============================] - ETA: 0s"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for key in image_dict.keys():\n",
     "    image_dict[key] = ammico.EmotionDetector(image_dict[key], emotion_threshold=50, race_threshold=50).analyse_image()"
@@ -10793,15 +831,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:11.393676Z",
-     "iopub.status.busy": "2024-02-19T08:54:11.393305Z",
-     "iopub.status.idle": "2024-02-19T08:54:11.396416Z",
-     "shell.execute_reply": "2024-02-19T08:54:11.395833Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "model_type = \"blip\"\n",
@@ -10821,15 +852,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:11.399454Z",
-     "iopub.status.busy": "2024-02-19T08:54:11.399132Z",
-     "iopub.status.idle": "2024-02-19T08:54:11.402161Z",
-     "shell.execute_reply": "2024-02-19T08:54:11.401648Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "my_obj = ammico.MultimodalSearch(image_dict)"
@@ -10837,597 +861,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:11.404913Z",
-     "iopub.status.busy": "2024-02-19T08:54:11.404591Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.394925Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.394163Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
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-      "\r",
-      "  0%|          | 0.00/1.97G [00:00<?, ?B/s]"
-     ]
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-    {
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-      "\r",
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-      "\n"
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-    {
-     "ename": "OSError",
-     "evalue": "[Errno 28] No space left on device",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[37], line 8\u001b[0m\n\u001b[1;32m      1\u001b[0m (\n\u001b[1;32m      2\u001b[0m     model,\n\u001b[1;32m      3\u001b[0m     vis_processors,\n\u001b[1;32m      4\u001b[0m     txt_processors,\n\u001b[1;32m      5\u001b[0m     image_keys,\n\u001b[1;32m      6\u001b[0m     image_names,\n\u001b[1;32m      7\u001b[0m     features_image_stacked,\n\u001b[0;32m----> 8\u001b[0m ) \u001b[38;5;241m=\u001b[39m \u001b[43mmy_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparsing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath_to_save_tensors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/content/drive/MyDrive/misinformation-data/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     11\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/multimodal_search.py:363\u001b[0m, in \u001b[0;36mMultimodalSearch.parsing_images\u001b[0;34m(self, model_type, path_to_save_tensors, path_to_load_tensors)\u001b[0m\n\u001b[1;32m    349\u001b[0m select_extract_image_features \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    350\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mblip2\u001b[39m\u001b[38;5;124m\"\u001b[39m: MultimodalSearch\u001b[38;5;241m.\u001b[39mextract_image_features_blip2,\n\u001b[1;32m    351\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mblip\u001b[39m\u001b[38;5;124m\"\u001b[39m: MultimodalSearch\u001b[38;5;241m.\u001b[39mextract_image_features_basic,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    355\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclip_vitl14_336\u001b[39m\u001b[38;5;124m\"\u001b[39m: MultimodalSearch\u001b[38;5;241m.\u001b[39mextract_image_features_clip,\n\u001b[1;32m    356\u001b[0m }\n\u001b[1;32m    358\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m model_type \u001b[38;5;129;01min\u001b[39;00m select_model\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[1;32m    359\u001b[0m     (\n\u001b[1;32m    360\u001b[0m         model,\n\u001b[1;32m    361\u001b[0m         vis_processors,\n\u001b[1;32m    362\u001b[0m         txt_processors,\n\u001b[0;32m--> 363\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[43mselect_model\u001b[49m\u001b[43m[\u001b[49m\n\u001b[1;32m    364\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel_type\u001b[49m\n\u001b[1;32m    365\u001b[0m \u001b[43m    \u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mMultimodalSearch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmultimodal_device\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    366\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    367\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mSyntaxError\u001b[39;00m(\n\u001b[1;32m    368\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease, use one of the following models: blip2, blip, albef, clip_base, clip_vitl14, clip_vitl14_336\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    369\u001b[0m     )\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/multimodal_search.py:55\u001b[0m, in \u001b[0;36mMultimodalSearch.load_feature_extractor_model_blip\u001b[0;34m(self, device)\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_feature_extractor_model_blip\u001b[39m(\u001b[38;5;28mself\u001b[39m, device: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m     44\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     45\u001b[0m \u001b[38;5;124;03m    Load base blip_feature_extractor model and preprocessors for visual and text inputs from lavis.models.\u001b[39;00m\n\u001b[1;32m     46\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[38;5;124;03m        txt_processors (dict): preprocessors for text inputs.\u001b[39;00m\n\u001b[1;32m     54\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m---> 55\u001b[0m     model, vis_processors, txt_processors \u001b[38;5;241m=\u001b[39m \u001b[43mload_model_and_preprocess\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     56\u001b[0m \u001b[43m        \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mblip_feature_extractor\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     57\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbase\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     58\u001b[0m \u001b[43m        \u001b[49m\u001b[43mis_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     59\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     60\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     61\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m model, vis_processors, txt_processors\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/__init__.py:195\u001b[0m, in \u001b[0;36mload_model_and_preprocess\u001b[0;34m(name, model_type, is_eval, device)\u001b[0m\n\u001b[1;32m    192\u001b[0m model_cls \u001b[38;5;241m=\u001b[39m registry\u001b[38;5;241m.\u001b[39mget_model_class(name)\n\u001b[1;32m    194\u001b[0m \u001b[38;5;66;03m# load model\u001b[39;00m\n\u001b[0;32m--> 195\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_cls\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    197\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_eval:\n\u001b[1;32m    198\u001b[0m     model\u001b[38;5;241m.\u001b[39meval()\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/base_model.py:70\u001b[0m, in \u001b[0;36mBaseModel.from_pretrained\u001b[0;34m(cls, model_type)\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     61\u001b[0m \u001b[38;5;124;03mBuild a pretrained model from default configuration file, specified by model_type.\u001b[39;00m\n\u001b[1;32m     62\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[38;5;124;03m    - model (nn.Module): pretrained or finetuned model, depending on the configuration.\u001b[39;00m\n\u001b[1;32m     68\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     69\u001b[0m model_cfg \u001b[38;5;241m=\u001b[39m OmegaConf\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mdefault_config_path(model_type))\u001b[38;5;241m.\u001b[39mmodel\n\u001b[0;32m---> 70\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_cfg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/blip_models/blip_feature_extractor.py:208\u001b[0m, in \u001b[0;36mBlipFeatureExtractor.from_config\u001b[0;34m(cls, cfg)\u001b[0m\n\u001b[1;32m    206\u001b[0m pretrain_path \u001b[38;5;241m=\u001b[39m cfg\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpretrained\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    207\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pretrain_path \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 208\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_from_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl_or_filename\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpretrain_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    209\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    210\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo pretrained weights are loaded.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/blip_models/blip.py:30\u001b[0m, in \u001b[0;36mBlipBase.load_from_pretrained\u001b[0;34m(self, url_or_filename)\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_from_pretrained\u001b[39m(\u001b[38;5;28mself\u001b[39m, url_or_filename):\n\u001b[1;32m     29\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m is_url(url_or_filename):\n\u001b[0;32m---> 30\u001b[0m         cached_file \u001b[38;5;241m=\u001b[39m \u001b[43mdownload_cached_file\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     31\u001b[0m \u001b[43m            \u001b[49m\u001b[43murl_or_filename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_hash\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m     32\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     33\u001b[0m         checkpoint \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mload(cached_file, map_location\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     34\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misfile(url_or_filename):\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/common/dist_utils.py:132\u001b[0m, in \u001b[0;36mdownload_cached_file\u001b[0;34m(url, check_hash, progress)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cached_file\n\u001b[1;32m    131\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_main_process():\n\u001b[0;32m--> 132\u001b[0m     \u001b[43mtimm_hub\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_cached_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_hash\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    134\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_dist_avail_and_initialized():\n\u001b[1;32m    135\u001b[0m     dist\u001b[38;5;241m.\u001b[39mbarrier()\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/timm/models/hub.py:51\u001b[0m, in \u001b[0;36mdownload_cached_file\u001b[0;34m(url, check_hash, progress)\u001b[0m\n\u001b[1;32m     49\u001b[0m         r \u001b[38;5;241m=\u001b[39m HASH_REGEX\u001b[38;5;241m.\u001b[39msearch(filename)  \u001b[38;5;66;03m# r is Optional[Match[str]]\u001b[39;00m\n\u001b[1;32m     50\u001b[0m         hash_prefix \u001b[38;5;241m=\u001b[39m r\u001b[38;5;241m.\u001b[39mgroup(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m r \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 51\u001b[0m     \u001b[43mdownload_url_to_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcached_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhash_prefix\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprogress\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cached_file\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/hub.py:636\u001b[0m, in \u001b[0;36mdownload_url_to_file\u001b[0;34m(url, dst, hash_prefix, progress)\u001b[0m\n\u001b[1;32m    634\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(buffer) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    635\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m--> 636\u001b[0m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    637\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m hash_prefix \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    638\u001b[0m     sha256\u001b[38;5;241m.\u001b[39mupdate(buffer)\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/tempfile.py:478\u001b[0m, in \u001b[0;36m_TemporaryFileWrapper.__getattr__.<locals>.func_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    476\u001b[0m \u001b[38;5;129m@_functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m    477\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfunc_wrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 478\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[0;31mOSError\u001b[0m: [Errno 28] No space left on device"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "(\n",
     "    model,\n",
@@ -11453,15 +889,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.399067Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.398613Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.401763Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.401225Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# (\n",
@@ -11495,15 +924,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.406155Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.405642Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.409654Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.408999Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import importlib_resources                                                                       # only require for image query example\n",
@@ -11529,28 +951,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.412858Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.412664Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.436288Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.435775Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'model' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[40], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m similarity, sorted_lists \u001b[38;5;241m=\u001b[39m my_obj\u001b[38;5;241m.\u001b[39mmultimodal_search(\n\u001b[0;32m----> 2\u001b[0m     \u001b[43mmodel\u001b[49m,\n\u001b[1;32m      3\u001b[0m     vis_processors,\n\u001b[1;32m      4\u001b[0m     txt_processors,\n\u001b[1;32m      5\u001b[0m     model_type,\n\u001b[1;32m      6\u001b[0m     image_keys,\n\u001b[1;32m      7\u001b[0m     features_image_stacked,\n\u001b[1;32m      8\u001b[0m     search_query,\n\u001b[1;32m      9\u001b[0m     filter_number_of_images\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m,\n\u001b[1;32m     10\u001b[0m )\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "similarity, sorted_lists = my_obj.multimodal_search(\n",
     "    model,\n",
@@ -11566,56 +969,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.443401Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.442981Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.462491Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.461996Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'similarity' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[41], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msimilarity\u001b[49m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'similarity' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "similarity"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.465916Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.465540Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.484725Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.484175Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'sorted_lists' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[42], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msorted_lists\u001b[49m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'sorted_lists' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "sorted_lists"
    ]
@@ -11629,171 +994,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.487891Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.487505Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.494730Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.494169Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'img4': {'filename': 'data-test/img4.png',\n",
-       "  'face': 'No',\n",
-       "  'multiple_faces': 'No',\n",
-       "  'no_faces': 0,\n",
-       "  'wears_mask': ['No'],\n",
-       "  'age': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'emotion': [None],\n",
-       "  'emotion (category)': [None],\n",
-       "  'text': 'MOODOVIN XI',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'MOODOVIN XI',\n",
-       "  'text_clean': 'XI',\n",
-       "  'text_summary': ' MOODOVIN XI XI: Vladimir Putin, Vladimir Vladmir Zelizer, Vladimir',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 0.66,\n",
-       "  'entity': ['MOODOVIN XI'],\n",
-       "  'entity_type': ['ORG'],\n",
-       "  'const_image_summary': 'a river running through a city next to tall buildings',\n",
-       "  '3_non-deterministic_summary': ['there is a pretty house that sits above the water',\n",
-       "   'there is a building with a balcony and lots of plants on the side of it',\n",
-       "   'several buildings with a river flowing through it']},\n",
-       " 'img1': {'filename': 'data-test/img1.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions JOHN R. TAYLOR University of Colorado',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions JOHN R. TAYLOR University of Colorado',\n",
-       "  'text_clean': 'THEORY The Quantum Theory of Collisions JOHN R. TAYLOR University of Colorado',\n",
-       "  'text_summary': ' SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 0.91,\n",
-       "  'entity': ['Non',\n",
-       "   '##vist',\n",
-       "   'Col',\n",
-       "   '##N',\n",
-       "   'R',\n",
-       "   'T',\n",
-       "   '##AYL',\n",
-       "   'University of Colorado'],\n",
-       "  'entity_type': ['MISC', 'MISC', 'MISC', 'ORG', 'PER', 'PER', 'ORG', 'ORG'],\n",
-       "  'const_image_summary': 'a close up of a piece of paper with writing on it',\n",
-       "  '3_non-deterministic_summary': ['a book opened to the book title for a novel',\n",
-       "   'there are many text on this page',\n",
-       "   'the text in a book is a handwritten poem']},\n",
-       " 'img2': {'filename': 'data-test/img2.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J.. H. WILKINSON',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J.. H. WILKINSON',\n",
-       "  'text_clean': 'THE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J .. H. WILKINSON',\n",
-       "  'text_summary': ' H. H. W. WILKINSON: The Algebri',\n",
-       "  'sentiment': 'NEGATIVE',\n",
-       "  'sentiment_score': 0.97,\n",
-       "  'entity': ['ALGEBRAIC EIGENVAL', 'NVS TIO MI', 'J', 'H', 'WILKINSON'],\n",
-       "  'entity_type': ['MISC', 'ORG', 'ORG', 'ORG', 'ORG'],\n",
-       "  'const_image_summary': 'a yellow book with green lettering on it',\n",
-       "  '3_non-deterministic_summary': ['a book cover with green writing on a black background',\n",
-       "   'the title page of a book with information from its authors',\n",
-       "   'a book about the age - related engineering and engineering']},\n",
-       " 'img3': {'filename': 'data-test/img3.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'm OOOO 0000 www.',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'm OOOO 0000 www.',\n",
-       "  'text_clean': 'm www .',\n",
-       "  'text_summary': ' www. m OOOO 0000 0000 www.m.m OOOo 0000',\n",
-       "  'sentiment': 'NEGATIVE',\n",
-       "  'sentiment_score': 0.62,\n",
-       "  'entity': [],\n",
-       "  'entity_type': [],\n",
-       "  'const_image_summary': 'a bus that is sitting on the side of a road',\n",
-       "  '3_non-deterministic_summary': ['there are cars and a bus on the side of the road',\n",
-       "   'a bus that is sitting in the middle of a street',\n",
-       "   'an aerial view of an empty city street with two large buses passing by']},\n",
-       " 'img0': {'filename': 'data-test/img0.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'Mathematische Formelsammlung für Ingenieure und Naturwissenschaftler Mit zahlreichen Abbildungen und Rechenbeispielen und einer ausführlichen Integraltafel 3., verbesserte Auflage',\n",
-       "  'text_language': 'de',\n",
-       "  'text_english': 'Mathematical formula collection for engineers and scientists With numerous illustrations and calculation examples and a detailed integral table 3rd, improved edition',\n",
-       "  'text_clean': 'Mathematical formula collection for engineers and scientists With numerous illustrations and calculation examples and a detailed integral table 3rd , improved edition',\n",
-       "  'text_summary': ' Mathematical formula collection for engineers and scientists . Includes numerous illustrations and calculation examples . Includes',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 1.0,\n",
-       "  'entity': [],\n",
-       "  'entity_type': [],\n",
-       "  'const_image_summary': 'a close up of an open book with writing on it',\n",
-       "  '3_non-deterministic_summary': ['a close up of a book with many languages',\n",
-       "   'a book that is opened up in german',\n",
-       "   'book about mathemarche formulals and their meaning']},\n",
-       " 'img5': {'filename': 'data-test/img5.png',\n",
-       "  'no_faces': 1,\n",
-       "  'age': [26],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': ['Negative'],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': ['sad'],\n",
-       "  'gender': ['Man'],\n",
-       "  'race': [None],\n",
-       "  'face': 'Yes',\n",
-       "  'text': None,\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': '',\n",
-       "  'text_clean': '',\n",
-       "  'text_summary': ' CNN.com will feature iReporter photos in a weekly Travel Snapshots gallery .',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 0.75,\n",
-       "  'entity': [],\n",
-       "  'entity_type': [],\n",
-       "  'const_image_summary': 'a person running on a beach near a rock formation',\n",
-       "  '3_non-deterministic_summary': ['a woman is running down the beach next to some rocks',\n",
-       "   'a woman running along the beach by the ocean',\n",
-       "   'there is a person running on the beach next to the ocean']}}"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "image_dict"
    ]
@@ -11807,57 +1010,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.498308Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.498116Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.561629Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.560724Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Your search query: politician press conference'"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'--------------------------------------------------'"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'Results:'"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "ename": "KeyError",
-     "evalue": "'politician press conference'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[44], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmy_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow_results\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[43msearch_query\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# you can change the index to see the results for other queries\u001b[39;49;00m\n\u001b[1;32m      3\u001b[0m \u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/multimodal_search.py:970\u001b[0m, in \u001b[0;36mMultimodalSearch.show_results\u001b[0;34m(self, query, itm, image_gradcam_with_itm)\u001b[0m\n\u001b[1;32m    967\u001b[0m     current_querry_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(query\u001b[38;5;241m.\u001b[39mvalues())[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    968\u001b[0m     current_querry_rank \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrank \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mlist\u001b[39m(query\u001b[38;5;241m.\u001b[39mvalues())[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m--> 970\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28;43msorted\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m    971\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubdict\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcurrent_querry_val\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreverse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m    972\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m    973\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m s[\u001b[38;5;241m1\u001b[39m][current_querry_rank] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    974\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/multimodal_search.py:971\u001b[0m, in \u001b[0;36mMultimodalSearch.show_results.<locals>.<lambda>\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m    967\u001b[0m     current_querry_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(query\u001b[38;5;241m.\u001b[39mvalues())[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    968\u001b[0m     current_querry_rank \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrank \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mlist\u001b[39m(query\u001b[38;5;241m.\u001b[39mvalues())[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    970\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28msorted\u001b[39m(\n\u001b[0;32m--> 971\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubdict\u001b[38;5;241m.\u001b[39mitems(), key\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m t: \u001b[43mt\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcurrent_querry_val\u001b[49m\u001b[43m]\u001b[49m, reverse\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    972\u001b[0m ):\n\u001b[1;32m    973\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m s[\u001b[38;5;241m1\u001b[39m][current_querry_rank] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    974\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'politician press conference'"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "my_obj.show_results(\n",
     "    search_query[0], # you can change the index to see the results for other queries\n",
@@ -11866,68 +1021,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.564539Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.564124Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.742817Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.742251Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Your search query: '"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/jpeg": 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",
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",
-      "text/plain": [
-       "<PIL.Image.Image image mode=RGB size=400x185>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'--------------------------------------------------'"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'Results:'"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "ename": "KeyError",
-     "evalue": "'/home/runner/work/AMMICO/AMMICO/ammico/data/test-crop-image.png'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[45], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmy_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow_results\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[43msearch_query\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# you can change the index to see the results for other queries\u001b[39;49;00m\n\u001b[1;32m      3\u001b[0m \u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/multimodal_search.py:970\u001b[0m, in \u001b[0;36mMultimodalSearch.show_results\u001b[0;34m(self, query, itm, image_gradcam_with_itm)\u001b[0m\n\u001b[1;32m    967\u001b[0m     current_querry_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(query\u001b[38;5;241m.\u001b[39mvalues())[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    968\u001b[0m     current_querry_rank \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrank \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mlist\u001b[39m(query\u001b[38;5;241m.\u001b[39mvalues())[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m--> 970\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28;43msorted\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m    971\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubdict\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcurrent_querry_val\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreverse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m    972\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m    973\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m s[\u001b[38;5;241m1\u001b[39m][current_querry_rank] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    974\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/multimodal_search.py:971\u001b[0m, in \u001b[0;36mMultimodalSearch.show_results.<locals>.<lambda>\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m    967\u001b[0m     current_querry_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(query\u001b[38;5;241m.\u001b[39mvalues())[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    968\u001b[0m     current_querry_rank \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrank \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mlist\u001b[39m(query\u001b[38;5;241m.\u001b[39mvalues())[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    970\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28msorted\u001b[39m(\n\u001b[0;32m--> 971\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubdict\u001b[38;5;241m.\u001b[39mitems(), key\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m t: \u001b[43mt\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcurrent_querry_val\u001b[49m\u001b[43m]\u001b[49m, reverse\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    972\u001b[0m ):\n\u001b[1;32m    973\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m s[\u001b[38;5;241m1\u001b[39m][current_querry_rank] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    974\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
-      "\u001b[0;31mKeyError\u001b[0m: '/home/runner/work/AMMICO/AMMICO/ammico/data/test-crop-image.png'"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "my_obj.show_results(\n",
     "    search_query[3], # you can change the index to see the results for other queries\n",
@@ -11945,15 +1041,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.746808Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.746601Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.749457Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.748884Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "itm_model = \"blip_base\"\n",
@@ -11963,28 +1052,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.752555Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.752084Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.773035Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.772413Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'image_keys' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[47], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m itm_scores, image_gradcam_with_itm \u001b[38;5;241m=\u001b[39m my_obj\u001b[38;5;241m.\u001b[39mimage_text_match_reordering(\n\u001b[1;32m      2\u001b[0m     search_query,\n\u001b[1;32m      3\u001b[0m     itm_model,\n\u001b[0;32m----> 4\u001b[0m     \u001b[43mimage_keys\u001b[49m,\n\u001b[1;32m      5\u001b[0m     sorted_lists,\n\u001b[1;32m      6\u001b[0m     batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m      7\u001b[0m     need_grad_cam\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m      8\u001b[0m )\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'image_keys' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "itm_scores, image_gradcam_with_itm = my_obj.image_text_match_reordering(\n",
     "    search_query,\n",
@@ -12005,28 +1075,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.775851Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.775512Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.795328Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.794723Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'image_gradcam_with_itm' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[48], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m my_obj\u001b[38;5;241m.\u001b[39mshow_results(\n\u001b[0;32m----> 2\u001b[0m     search_query[\u001b[38;5;241m0\u001b[39m], itm\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, image_gradcam_with_itm\u001b[38;5;241m=\u001b[39m\u001b[43mimage_gradcam_with_itm\u001b[49m\n\u001b[1;32m      3\u001b[0m )\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'image_gradcam_with_itm' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "my_obj.show_results(\n",
     "    search_query[0], itm=True, image_gradcam_with_itm=image_gradcam_with_itm\n",
@@ -12049,28 +1100,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.799498Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.799139Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.818722Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.818206Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "module 'ammico' has no attribute 'append_data_to_dict'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[49], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m outdict \u001b[38;5;241m=\u001b[39m \u001b[43mammico\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mappend_data_to_dict\u001b[49m(image_dict)\n\u001b[1;32m      2\u001b[0m df \u001b[38;5;241m=\u001b[39m ammico\u001b[38;5;241m.\u001b[39mdump_df(outdict)\n",
-      "\u001b[0;31mAttributeError\u001b[0m: module 'ammico' has no attribute 'append_data_to_dict'"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "outdict = ammico.append_data_to_dict(image_dict)\n",
     "df = ammico.dump_df(outdict)"
@@ -12085,28 +1117,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.822275Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.821902Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.841826Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.841214Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'df' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[50], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m10\u001b[39m)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df.head(10)"
    ]
@@ -12120,28 +1133,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.845662Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.845309Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.864880Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.864321Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'df' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[51], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241m.\u001b[39mto_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/content/drive/MyDrive/misinformation-data/data_out.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df.to_csv(\"/content/drive/MyDrive/misinformation-data/data_out.csv\")"
    ]
@@ -12166,38 +1160,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.868479Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.868084Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.911851Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.911132Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"100%\"\n",
-       "            height=\"650\"\n",
-       "            src=\"http://127.0.0.1:8057/\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x7f9791095040>"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "analysis_explorer = ammico.AnalysisExplorer(image_dict)\n",
     "analysis_explorer.run_server(port = 8057)"
@@ -12212,15 +1177,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.917049Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.916702Z",
-     "iopub.status.idle": "2024-02-19T08:54:38.080384Z",
-     "shell.execute_reply": "2024-02-19T08:54:38.079754Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "for key in image_dict.keys():\n",
@@ -12236,15 +1194,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:38.085405Z",
-     "iopub.status.busy": "2024-02-19T08:54:38.084973Z",
-     "iopub.status.idle": "2024-02-19T08:54:38.089091Z",
-     "shell.execute_reply": "2024-02-19T08:54:38.088517Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "df = ammico.get_dataframe(image_dict)"
@@ -12259,243 +1210,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:38.097290Z",
-     "iopub.status.busy": "2024-02-19T08:54:38.096874Z",
-     "iopub.status.idle": "2024-02-19T08:54:38.119692Z",
-     "shell.execute_reply": "2024-02-19T08:54:38.119109Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
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-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
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-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>data-test/img4.png</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>0</td>\n",
-       "      <td>[No]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.16</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.10</td>\n",
-       "      <td>0.42</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>0.21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>data-test/img1.png</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>0</td>\n",
-       "      <td>[No]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.96</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>data-test/img2.png</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>0</td>\n",
-       "      <td>[No]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>0.15</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>data-test/img3.png</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>0</td>\n",
-       "      <td>[No]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.06</td>\n",
-       "      <td>0.92</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>data-test/img0.png</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>0</td>\n",
-       "      <td>[No]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.98</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>data-test/img5.png</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>1</td>\n",
-       "      <td>[No]</td>\n",
-       "      <td>[26]</td>\n",
-       "      <td>[Man]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[sad]</td>\n",
-       "      <td>[Negative]</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.12</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>6 rows × 33 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             filename face multiple_faces  no_faces wears_mask     age  \\\n",
-       "0  data-test/img4.png   No             No         0       [No]  [None]   \n",
-       "1  data-test/img1.png   No             No         0       [No]  [None]   \n",
-       "2  data-test/img2.png   No             No         0       [No]  [None]   \n",
-       "3  data-test/img3.png   No             No         0       [No]  [None]   \n",
-       "4  data-test/img0.png   No             No         0       [No]  [None]   \n",
-       "5  data-test/img5.png  Yes             No         1       [No]    [26]   \n",
-       "\n",
-       "   gender    race emotion emotion (category)  ...  blue yellow cyan orange  \\\n",
-       "0  [None]  [None]  [None]             [None]  ...  0.16   0.00    0      0   \n",
-       "1  [None]  [None]  [None]             [None]  ...  0.00   0.00    0      0   \n",
-       "2  [None]  [None]  [None]             [None]  ...  0.00   0.75    0      0   \n",
-       "3  [None]  [None]  [None]             [None]  ...  0.00   0.00    0      0   \n",
-       "4  [None]  [None]  [None]             [None]  ...  0.00   0.00    0      0   \n",
-       "5   [Man]  [None]   [sad]         [Negative]  ...  0.12   0.00    0      0   \n",
-       "\n",
-       "  purple pink  brown  grey white black  \n",
-       "0   0.00    0   0.10  0.42  0.05  0.21  \n",
-       "1   0.00    0   0.00  0.96  0.00  0.04  \n",
-       "2   0.00    0   0.04  0.15  0.00  0.02  \n",
-       "3   0.02    0   0.06  0.92  0.01  0.00  \n",
-       "4   0.00    0   0.00  0.98  0.00  0.02  \n",
-       "5   0.00    0   0.02  0.50  0.00  0.00  \n",
-       "\n",
-       "[6 rows x 33 columns]"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df.head(10)"
    ]
@@ -12509,34 +1226,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:38.124073Z",
-     "iopub.status.busy": "2024-02-19T08:54:38.123661Z",
-     "iopub.status.idle": "2024-02-19T08:54:38.203962Z",
-     "shell.execute_reply": "2024-02-19T08:54:38.203276Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "OSError",
-     "evalue": "Cannot save file into a non-existent directory: '/content/drive/MyDrive/misinformation-data'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[56], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/content/drive/MyDrive/misinformation-data/data_out.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/util/_decorators.py:333\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    327\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m    328\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    329\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m    330\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    331\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m    332\u001b[0m     )\n\u001b[0;32m--> 333\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/core/generic.py:3961\u001b[0m, in \u001b[0;36mNDFrame.to_csv\u001b[0;34m(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, lineterminator, chunksize, date_format, doublequote, escapechar, decimal, errors, storage_options)\u001b[0m\n\u001b[1;32m   3950\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m, ABCDataFrame) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mto_frame()\n\u001b[1;32m   3952\u001b[0m formatter \u001b[38;5;241m=\u001b[39m DataFrameFormatter(\n\u001b[1;32m   3953\u001b[0m     frame\u001b[38;5;241m=\u001b[39mdf,\n\u001b[1;32m   3954\u001b[0m     header\u001b[38;5;241m=\u001b[39mheader,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3958\u001b[0m     decimal\u001b[38;5;241m=\u001b[39mdecimal,\n\u001b[1;32m   3959\u001b[0m )\n\u001b[0;32m-> 3961\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mDataFrameRenderer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mformatter\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_csv\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3962\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath_or_buf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3963\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlineterminator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlineterminator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3964\u001b[0m \u001b[43m    \u001b[49m\u001b[43msep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msep\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3965\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3966\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3967\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3968\u001b[0m \u001b[43m    \u001b[49m\u001b[43mquoting\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquoting\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3969\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3970\u001b[0m \u001b[43m    \u001b[49m\u001b[43mindex_label\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex_label\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3971\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3972\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunksize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunksize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3973\u001b[0m \u001b[43m    \u001b[49m\u001b[43mquotechar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquotechar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3974\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdate_format\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdate_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3975\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdoublequote\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdoublequote\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3976\u001b[0m \u001b[43m    \u001b[49m\u001b[43mescapechar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mescapechar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3977\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3978\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/io/formats/format.py:1014\u001b[0m, in \u001b[0;36mDataFrameRenderer.to_csv\u001b[0;34m(self, path_or_buf, encoding, sep, columns, index_label, mode, compression, quoting, quotechar, lineterminator, chunksize, date_format, doublequote, escapechar, errors, storage_options)\u001b[0m\n\u001b[1;32m    993\u001b[0m     created_buffer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    995\u001b[0m csv_formatter \u001b[38;5;241m=\u001b[39m CSVFormatter(\n\u001b[1;32m    996\u001b[0m     path_or_buf\u001b[38;5;241m=\u001b[39mpath_or_buf,\n\u001b[1;32m    997\u001b[0m     lineterminator\u001b[38;5;241m=\u001b[39mlineterminator,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1012\u001b[0m     formatter\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfmt,\n\u001b[1;32m   1013\u001b[0m )\n\u001b[0;32m-> 1014\u001b[0m \u001b[43mcsv_formatter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1016\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m created_buffer:\n\u001b[1;32m   1017\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(path_or_buf, StringIO)\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/io/formats/csvs.py:251\u001b[0m, in \u001b[0;36mCSVFormatter.save\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    247\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    248\u001b[0m \u001b[38;5;124;03mCreate the writer & save.\u001b[39;00m\n\u001b[1;32m    249\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    250\u001b[0m \u001b[38;5;66;03m# apply compression and byte/text conversion\u001b[39;00m\n\u001b[0;32m--> 251\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    252\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    253\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    254\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    255\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    256\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    257\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    258\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m handles:\n\u001b[1;32m    259\u001b[0m     \u001b[38;5;66;03m# Note: self.encoding is irrelevant here\u001b[39;00m\n\u001b[1;32m    260\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwriter \u001b[38;5;241m=\u001b[39m csvlib\u001b[38;5;241m.\u001b[39mwriter(\n\u001b[1;32m    261\u001b[0m         handles\u001b[38;5;241m.\u001b[39mhandle,\n\u001b[1;32m    262\u001b[0m         lineterminator\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlineterminator,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    267\u001b[0m         quotechar\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mquotechar,\n\u001b[1;32m    268\u001b[0m     )\n\u001b[1;32m    270\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_save()\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/io/common.py:749\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    747\u001b[0m \u001b[38;5;66;03m# Only for write methods\u001b[39;00m\n\u001b[1;32m    748\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode \u001b[38;5;129;01mand\u001b[39;00m is_path:\n\u001b[0;32m--> 749\u001b[0m     \u001b[43mcheck_parent_directory\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    751\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m compression:\n\u001b[1;32m    752\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m compression \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mzstd\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    753\u001b[0m         \u001b[38;5;66;03m# compression libraries do not like an explicit text-mode\u001b[39;00m\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/io/common.py:616\u001b[0m, in \u001b[0;36mcheck_parent_directory\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m    614\u001b[0m parent \u001b[38;5;241m=\u001b[39m Path(path)\u001b[38;5;241m.\u001b[39mparent\n\u001b[1;32m    615\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m parent\u001b[38;5;241m.\u001b[39mis_dir():\n\u001b[0;32m--> 616\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(\u001b[38;5;124mrf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot save file into a non-existent directory: \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparent\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "\u001b[0;31mOSError\u001b[0m: Cannot save file into a non-existent directory: '/content/drive/MyDrive/misinformation-data'"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df.to_csv(\"/content/drive/MyDrive/misinformation-data/data_out.csv\")"
    ]
@@ -12571,7 +1263,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,
diff --git a/build/doctrees/nbsphinx/notebooks/Example cropposts.ipynb b/build/doctrees/nbsphinx/notebooks/Example cropposts.ipynb
index 0fb807c..d7c2bcb 100644
--- a/build/doctrees/nbsphinx/notebooks/Example cropposts.ipynb	
+++ b/build/doctrees/nbsphinx/notebooks/Example cropposts.ipynb	
@@ -21,16 +21,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "2",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:55:18.705057Z",
-     "iopub.status.busy": "2024-02-19T08:55:18.704857Z",
-     "iopub.status.idle": "2024-02-19T08:55:18.719904Z",
-     "shell.execute_reply": "2024-02-19T08:55:18.719429Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Please ignore this cell: extra install steps that are only executed when running the notebook on Google Colab\n",
@@ -57,16 +50,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "3",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:55:18.722234Z",
-     "iopub.status.busy": "2024-02-19T08:55:18.721861Z",
-     "iopub.status.idle": "2024-02-19T08:55:34.897136Z",
-     "shell.execute_reply": "2024-02-19T08:55:34.896567Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import ammico.cropposts as crpo\n",
@@ -88,38 +74,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:55:34.900259Z",
-     "iopub.status.busy": "2024-02-19T08:55:34.899574Z",
-     "iopub.status.idle": "2024-02-19T08:55:35.590003Z",
-     "shell.execute_reply": "2024-02-19T08:55:35.589334Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# load ref view for cropping the same type social media posts images.\n",
     "# substitute the below paths for your samples\n",
@@ -149,58 +107,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "7",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:55:35.594186Z",
-     "iopub.status.busy": "2024-02-19T08:55:35.593795Z",
-     "iopub.status.idle": "2024-02-19T08:55:38.382987Z",
-     "shell.execute_reply": "2024-02-19T08:55:38.382273Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# crop a posts from reference view, check the cropping \n",
     "# this will only plot something if the reference is found on the image\n",
@@ -223,30 +133,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "9",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:55:38.385523Z",
-     "iopub.status.busy": "2024-02-19T08:55:38.385315Z",
-     "iopub.status.idle": "2024-02-19T08:55:39.135269Z",
-     "shell.execute_reply": "2024-02-19T08:55:39.134594Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "No files found in data/ with pattern '['png', 'jpg', 'jpeg', 'gif', 'webp', 'avif', 'tiff']'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[5], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m ref_dir \u001b[38;5;241m=\u001b[39m pkg \u001b[38;5;241m/\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mref\u001b[39m\u001b[38;5;124m\"\u001b[39m \n\u001b[1;32m      3\u001b[0m save_crop_dir \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata/crop/\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 5\u001b[0m files \u001b[38;5;241m=\u001b[39m \u001b[43mutils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfind_files\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcrop_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43mlimit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      6\u001b[0m ref_files \u001b[38;5;241m=\u001b[39m utils\u001b[38;5;241m.\u001b[39mfind_files(path\u001b[38;5;241m=\u001b[39mref_dir\u001b[38;5;241m.\u001b[39mas_posix(), limit\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m100\u001b[39m)\n\u001b[1;32m      8\u001b[0m crpo\u001b[38;5;241m.\u001b[39mcrop_media_posts(files, ref_files, save_crop_dir, plt_match\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, plt_crop\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, plt_image\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/utils.py:134\u001b[0m, in \u001b[0;36mfind_files\u001b[0;34m(path, pattern, recursive, limit, random_seed)\u001b[0m\n\u001b[1;32m    131\u001b[0m     results\u001b[38;5;241m.\u001b[39mextend(_match_pattern(path, p, recursive\u001b[38;5;241m=\u001b[39mrecursive))\n\u001b[1;32m    133\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(results) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 134\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo files found in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m with pattern \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpattern\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    136\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m random_seed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    137\u001b[0m     random\u001b[38;5;241m.\u001b[39mseed(random_seed)\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: No files found in data/ with pattern '['png', 'jpg', 'jpeg', 'gif', 'webp', 'avif', 'tiff']'"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "\n",
     "crop_dir = \"data/\"\n",
@@ -285,7 +175,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.9.16"
   }
  },
  "nbformat": 4,
diff --git a/build/doctrees/nbsphinx/notebooks_DemoNotebook_ammico_88_1.jpg b/build/doctrees/nbsphinx/notebooks_DemoNotebook_ammico_88_1.jpg
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@@ -120,7 +120,7 @@
 <div class="line"><code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">git+https://github.com/ssciwr/AMMICO.git</span></code></div>
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 <section id="Use-a-test-dataset">
 <h2>Use a test dataset<a class="headerlink" href="#Use-a-test-dataset" title="Link to this heading"></a></h2>
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 <p>Next you need to provide a path for the saved images - a folder where the data is stored locally. This directory is automatically created if it does not exist.</p>
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 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data_path</span> <span class="o">=</span> <span class="s2">&quot;./data-test&quot;</span>
@@ -622,7 +175,7 @@
 <section id="Import-the-ammico-package.">
 <h2>Import the ammico package.<a class="headerlink" href="#Import-the-ammico-package." title="Link to this heading"></a></h2>
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 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
@@ -650,7 +203,7 @@ tf.ones([2, 2])
 </div>
 <p>where you place the key on your Google Drive if running on colab, or place it in a local folder on your machine.</p>
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 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># os.environ[&quot;GOOGLE_APPLICATION_CREDENTIALS&quot;] = &quot;/content/drive/MyDrive/misinformation-data/misinformation-campaign-981aa55a3b13.json&quot;</span>
@@ -694,7 +247,7 @@ tf.ones([2, 2])
 <p>The <code class="docutils literal notranslate"><span class="pre">find_files</span></code> function returns a nested dict that contains the file ids and the paths to the files and is empty otherwise. This dict is filled step by step with more data as each detector class is run on the data (see below).</p>
 <p>If you downloaded the test dataset above, you can directly provide the path you already set for the test directory, <code class="docutils literal notranslate"><span class="pre">data_path</span></code>.</p>
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 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">image_dict</span> <span class="o">=</span> <span class="n">ammico</span><span class="o">.</span><span class="n">find_files</span><span class="p">(</span>
@@ -710,8 +263,8 @@ tf.ones([2, 2])
 <p>A Dash user interface is to select the most suitable options for the analysis, before running a complete analysis on the whole data set. The options for each detector module are explained below in the corresponding sections; for example, different models can be selected that will provide slightly different results. This way, the user can interactively explore which settings provide the most accurate results. In the interface, the nested <code class="docutils literal notranslate"><span class="pre">image_dict</span></code> is passed through the <code class="docutils literal notranslate"><span class="pre">AnalysisExplorer</span></code>
 class. The interface is run on a specific port which is passed using the <code class="docutils literal notranslate"><span class="pre">port</span></code> keyword; if a port is already in use, it will return an error message, in which case the user should select a different port number. The interface opens a dash app inside the Jupyter Notebook and allows selection of the input file in the top left dropdown menu, as well as selection of the detector type in the top right, with options for each detector type as explained below. The output of the detector is shown
 directly on the right next to the image. This way, the user can directly inspect how updating the options for each detector changes the computed results, and find the best settings for a production run.</p>
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 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">analysis_explorer</span> <span class="o">=</span> <span class="n">ammico</span><span class="o">.</span><span class="n">AnalysisExplorer</span><span class="p">(</span><span class="n">image_dict</span><span class="p">)</span>
@@ -719,26 +272,13 @@ directly on the right next to the image. This way, the user can directly inspect
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 </section>
 <section id="Step-3:-Analyze-all-images">
 <h2>Step 3: Analyze all images<a class="headerlink" href="#Step-3:-Analyze-all-images" title="Link to this heading"></a></h2>
 <p>The analysis can be run in production on all images in the data set. Depending on the size of the data set and the computing resources available, this can take some time.</p>
 <p>It is also possible to set the dump file creation <code class="docutils literal notranslate"><span class="pre">dump_file</span></code> in order to save the calculated data every <code class="docutils literal notranslate"><span class="pre">dump_every</span></code> images.</p>
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@@ -749,8 +289,8 @@ directly on the right next to the image. This way, the user can directly inspect
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 <p>The desired detector modules are called sequentially in any order, for example the <code class="docutils literal notranslate"><span class="pre">EmotionDetector</span></code>:</p>
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 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">num</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">image_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">()),</span> <span class="n">total</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">image_dict</span><span class="p">)):</span>    <span class="c1"># loop through all images</span>
@@ -762,442 +302,9 @@ directly on the right next to the image. This way, the user can directly inspect
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-Found missing key(s) {&#39;no_faces&#39;, &#39;age&#39;, &#39;wears_mask&#39;, &#39;emotion (category)&#39;, &#39;multiple_faces&#39;, &#39;emotion&#39;, &#39;gender&#39;, &#39;race&#39;, &#39;face&#39;} in subdict img3 - setting to None.
-Found missing key(s) {&#39;no_faces&#39;, &#39;age&#39;, &#39;wears_mask&#39;, &#39;emotion (category)&#39;, &#39;multiple_faces&#39;, &#39;emotion&#39;, &#39;gender&#39;, &#39;race&#39;, &#39;face&#39;} in subdict img0 - setting to None.
-Found missing key(s) {&#39;no_faces&#39;, &#39;age&#39;, &#39;wears_mask&#39;, &#39;emotion (category)&#39;, &#39;multiple_faces&#39;, &#39;emotion&#39;, &#39;gender&#39;, &#39;race&#39;, &#39;face&#39;} in subdict img5 - setting to None.
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-<p>     ━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━ 23.8/42.8 MB 115.6 MB/s eta 0:00:01
-     ━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━ 28.2/42.8 MB 125.8 MB/s eta 0:00:01
-     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━ 32.9/42.8 MB 128.8 MB/s eta 0:00:01
-     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━ 37.8/42.8 MB 134.9 MB/s eta 0:00:01
-     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 42.8/42.8 MB 145.2 MB/s eta 0:00:01
-     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 42.8/42.8 MB 145.2 MB/s eta 0:00:01</p>
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-<span class="ansi-red-intense-fg">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span><span class="ansi-red-intense-fg">╸</span> <span class="ansi-green-fg">42.8/42.8 MB</span> <span class="ansi-red-fg">145.2 MB/s</span> eta <span class="ansi-cyan-fg">0:00:01</span>
-<span class="ansi-black-intense-fg">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-green-fg">42.8/42.8 MB</span> <span class="ansi-red-fg">60.8 MB/s</span> eta <span class="ansi-cyan-fg">0:00:00</span>
-   Requirement already satisfied: spacy&lt;3.8.0,&gt;=3.7.2 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from en-core-web-md==3.7.1) (3.7.4)
-   Requirement already satisfied: spacy-legacy&lt;3.1.0,&gt;=3.0.11 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (3.0.12)
-   Requirement already satisfied: spacy-loggers&lt;2.0.0,&gt;=1.0.0 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (1.0.5)
-   Requirement already satisfied: murmurhash&lt;1.1.0,&gt;=0.28.0 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (1.0.10)
-   Requirement already satisfied: cymem&lt;2.1.0,&gt;=2.0.2 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (2.0.8)
-   Requirement already satisfied: preshed&lt;3.1.0,&gt;=3.0.2 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (3.0.9)
-   Requirement already satisfied: thinc&lt;8.3.0,&gt;=8.2.2 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (8.2.3)
-   Requirement already satisfied: wasabi&lt;1.2.0,&gt;=0.9.1 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (1.1.2)
-   Requirement already satisfied: srsly&lt;3.0.0,&gt;=2.4.3 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (2.4.8)
-   Requirement already satisfied: catalogue&lt;2.1.0,&gt;=2.0.6 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (2.0.10)
-   Requirement already satisfied: weasel&lt;0.4.0,&gt;=0.1.0 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (0.3.4)
-   Requirement already satisfied: typer&lt;0.10.0,&gt;=0.3.0 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (0.9.0)
-   Requirement already satisfied: smart-open&lt;7.0.0,&gt;=5.2.1 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (6.4.0)
-   Requirement already satisfied: tqdm&lt;5.0.0,&gt;=4.38.0 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (4.66.2)
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-   Requirement already satisfied: setuptools in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (58.1.0)
-   Requirement already satisfied: packaging&gt;=20.0 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (23.2)
-   Requirement already satisfied: langcodes&lt;4.0.0,&gt;=3.2.0 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (3.3.0)
-   Requirement already satisfied: numpy&gt;=1.19.0 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (1.23.4)
-   Requirement already satisfied: typing-extensions&gt;=4.2.0 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from pydantic!=1.8,!=1.8.1,&lt;3.0.0,&gt;=1.7.4-&gt;spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (4.5.0)
-   Requirement already satisfied: charset-normalizer&lt;4,&gt;=2 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from requests&lt;3.0.0,&gt;=2.13.0-&gt;spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (3.3.2)
-   Requirement already satisfied: idna&lt;4,&gt;=2.5 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from requests&lt;3.0.0,&gt;=2.13.0-&gt;spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (2.10)
-   Requirement already satisfied: urllib3&lt;3,&gt;=1.21.1 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from requests&lt;3.0.0,&gt;=2.13.0-&gt;spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (2.2.1)
-   Requirement already satisfied: certifi&gt;=2017.4.17 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from requests&lt;3.0.0,&gt;=2.13.0-&gt;spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (2024.2.2)
-   </pre></div></div>
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-<p>     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 42.8/42.8 MB 145.2 MB/s eta 0:00:01
-     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 42.8/42.8 MB 60.8 MB/s eta 0:00:00</p>
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-<div><p>[?25hRequirement already satisfied: spacy&lt;3.8.0,&gt;=3.7.2 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from en-core-web-md==3.7.1) (3.7.4)
-Requirement already satisfied: spacy-legacy&lt;3.1.0,&gt;=3.0.11 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (3.0.12)
-Requirement already satisfied: spacy-loggers&lt;2.0.0,&gt;=1.0.0 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (1.0.5)
-Requirement already satisfied: murmurhash&lt;1.1.0,&gt;=0.28.0 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (1.0.10)
-Requirement already satisfied: cymem&lt;2.1.0,&gt;=2.0.2 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (2.0.8)
-Requirement already satisfied: preshed&lt;3.1.0,&gt;=3.0.2 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (3.0.9)
-Requirement already satisfied: thinc&lt;8.3.0,&gt;=8.2.2 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (8.2.3)
-Requirement already satisfied: wasabi&lt;1.2.0,&gt;=0.9.1 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (1.1.2)
-Requirement already satisfied: srsly&lt;3.0.0,&gt;=2.4.3 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (2.4.8)
-Requirement already satisfied: catalogue&lt;2.1.0,&gt;=2.0.6 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (2.0.10)
-Requirement already satisfied: weasel&lt;0.4.0,&gt;=0.1.0 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (0.3.4)
-Requirement already satisfied: typer&lt;0.10.0,&gt;=0.3.0 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (0.9.0)
-Requirement already satisfied: smart-open&lt;7.0.0,&gt;=5.2.1 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (6.4.0)
-Requirement already satisfied: tqdm&lt;5.0.0,&gt;=4.38.0 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (4.66.2)
-Requirement already satisfied: requests&lt;3.0.0,&gt;=2.13.0 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (2.31.0)
-Requirement already satisfied: pydantic!=1.8,!=1.8.1,&lt;3.0.0,&gt;=1.7.4 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (1.10.14)
-Requirement already satisfied: jinja2 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (3.1.3)
-Requirement already satisfied: setuptools in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (58.1.0)
-Requirement already satisfied: packaging&gt;=20.0 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (23.2)
-Requirement already satisfied: langcodes&lt;4.0.0,&gt;=3.2.0 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (3.3.0)
-Requirement already satisfied: numpy&gt;=1.19.0 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (1.23.4)
-Requirement already satisfied: typing-extensions&gt;=4.2.0 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from pydantic!=1.8,!=1.8.1,&lt;3.0.0,&gt;=1.7.4-&gt;spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (4.5.0)
-Requirement already satisfied: charset-normalizer&lt;4,&gt;=2 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from requests&lt;3.0.0,&gt;=2.13.0-&gt;spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (3.3.2)
-Requirement already satisfied: idna&lt;4,&gt;=2.5 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from requests&lt;3.0.0,&gt;=2.13.0-&gt;spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (2.10)
-Requirement already satisfied: urllib3&lt;3,&gt;=1.21.1 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from requests&lt;3.0.0,&gt;=2.13.0-&gt;spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (2.2.1)
-Requirement already satisfied: certifi&gt;=2017.4.17 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from requests&lt;3.0.0,&gt;=2.13.0-&gt;spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (2024.2.2)</p>
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-Requirement already satisfied: blis&lt;0.8.0,&gt;=0.7.8 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from thinc&lt;8.3.0,&gt;=8.2.2-&gt;spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (0.7.11)
-Requirement already satisfied: confection&lt;1.0.0,&gt;=0.0.1 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from thinc&lt;8.3.0,&gt;=8.2.2-&gt;spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (0.1.4)
-Requirement already satisfied: click&lt;9.0.0,&gt;=7.1.1 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from typer&lt;0.10.0,&gt;=0.3.0-&gt;spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (8.1.7)
-Requirement already satisfied: cloudpathlib&lt;0.17.0,&gt;=0.7.0 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from weasel&lt;0.4.0,&gt;=0.1.0-&gt;spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (0.16.0)
-Requirement already satisfied: MarkupSafe&gt;=2.0 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from jinja2-&gt;spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (2.1.5)
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-Installing collected packages: en-core-web-md
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-Successfully installed en-core-web-md-3.7.1
-<span class="ansi-green-fg">✔ Download and installation successful</span>
-You can now load the package via spacy.load(&#39;en_core_web_md&#39;)
-<span class="ansi-yellow-fg">⚠ Restart to reload dependencies</span>
-If you are in a Jupyter or Colab notebook, you may need to restart Python in
-order to load all the package&#39;s dependencies. You can do this by selecting the
-&#39;Restart kernel&#39; or &#39;Restart runtime&#39; option.
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-
-<span class="ansi-bold">[</span><span class="ansi-blue-fg">notice</span><span class="ansi-bold">]</span> A new release of pip is available: <span class="ansi-red-fg">23.0.1</span> -&gt; <span class="ansi-green-fg">24.0</span>
-<span class="ansi-bold">[</span><span class="ansi-blue-fg">notice</span><span class="ansi-bold">]</span> To update, run: <span class="ansi-green-fg">pip install --upgrade pip</span>
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 <p>For the computationally demanding <code class="docutils literal notranslate"><span class="pre">SummaryDetector</span></code>, it is best to initialize the model first and then analyze each image while passing the model explicitly. This can be done in a separate loop or in the same loop as for text and emotion detection.</p>
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@@ -8792,4214 +335,9 @@ order to load all the package&#39;s dependencies. You can do this by selecting t
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 <p>The nested dictionary will be updated from containing only the file id’s and paths to the image files, to containing all calculated image features.</p>
 </section>
 <section id="Step-4:-Convert-analysis-output-to-pandas-dataframe-and-write-csv">
 <h2>Step 4: Convert analysis output to pandas dataframe and write csv<a class="headerlink" href="#Step-4:-Convert-analysis-output-to-pandas-dataframe-and-write-csv" title="Link to this heading"></a></h2>
 <p>The content of the nested dictionary can then conveniently be converted into a pandas dataframe for further analysis in Python, or be written as a csv file:</p>
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 <p>Inspect the dataframe:</p>
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 </pre></div>
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-      <td>...</td>
-      <td>en</td>
-      <td>THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO M...</td>
-      <td>THE PROBLEM DOM NVS TIO MINA Monographs on Num...</td>
-      <td>H. H. W. WILKINSON: The Algebri</td>
-      <td>NEGATIVE</td>
-      <td>0.97</td>
-      <td>[ALGEBRAIC EIGENVAL, NVS TIO MI, J, H, WILKINSON]</td>
-      <td>[MISC, ORG, ORG, ORG, ORG]</td>
-      <td>a yellow book with green lettering on it</td>
-      <td>[an old book with a picture of the slogan of t...</td>
-    </tr>
-  </tbody>
-</table>
-<p>3 rows × 21 columns</p>
-</div></div>
-</div>
 <p>Or write to a csv file:</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">image_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s2">&quot;/content/drive/MyDrive/misinformation-data/data_out.csv&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
-<span class="ansi-red-fg">OSError</span>                                   Traceback (most recent call last)
-Cell <span class="ansi-green-fg">In[15], line 1</span>
-<span class="ansi-green-fg">----&gt; 1</span> <span class="ansi-yellow-bg">image_df</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">to_csv</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">/content/drive/MyDrive/misinformation-data/data_out.csv</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg">)</span>
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/util/_decorators.py:333</span>, in <span class="ansi-cyan-fg">deprecate_nonkeyword_arguments.&lt;locals&gt;.decorate.&lt;locals&gt;.wrapper</span><span class="ansi-blue-fg">(*args, **kwargs)</span>
-<span class="ansi-green-intense-fg ansi-bold">    327</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span style="color: rgb(0,135,0)">len</span>(args) <span style="color: rgb(98,98,98)">&gt;</span> num_allow_args:
-<span class="ansi-green-intense-fg ansi-bold">    328</span>     warnings<span style="color: rgb(98,98,98)">.</span>warn(
-<span class="ansi-green-intense-fg ansi-bold">    329</span>         msg<span style="color: rgb(98,98,98)">.</span>format(arguments<span style="color: rgb(98,98,98)">=</span>_format_argument_list(allow_args)),
-<span class="ansi-green-intense-fg ansi-bold">    330</span>         <span class="ansi-bold" style="color: rgb(215,95,95)">FutureWarning</span>,
-<span class="ansi-green-intense-fg ansi-bold">    331</span>         stacklevel<span style="color: rgb(98,98,98)">=</span>find_stack_level(),
-<span class="ansi-green-intense-fg ansi-bold">    332</span>     )
-<span class="ansi-green-fg">--&gt; 333</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">func</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/core/generic.py:3961</span>, in <span class="ansi-cyan-fg">NDFrame.to_csv</span><span class="ansi-blue-fg">(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, lineterminator, chunksize, date_format, doublequote, escapechar, decimal, errors, storage_options)</span>
-<span class="ansi-green-intense-fg ansi-bold">   3950</span> df <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span style="color: rgb(0,135,0)">isinstance</span>(<span style="color: rgb(0,135,0)">self</span>, ABCDataFrame) <span class="ansi-bold" style="color: rgb(0,135,0)">else</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>to_frame()
-<span class="ansi-green-intense-fg ansi-bold">   3952</span> formatter <span style="color: rgb(98,98,98)">=</span> DataFrameFormatter(
-<span class="ansi-green-intense-fg ansi-bold">   3953</span>     frame<span style="color: rgb(98,98,98)">=</span>df,
-<span class="ansi-green-intense-fg ansi-bold">   3954</span>     header<span style="color: rgb(98,98,98)">=</span>header,
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">   3958</span>     decimal<span style="color: rgb(98,98,98)">=</span>decimal,
-<span class="ansi-green-intense-fg ansi-bold">   3959</span> )
-<span class="ansi-green-fg">-&gt; 3961</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">DataFrameRenderer</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">formatter</span><span class="ansi-yellow-bg">)</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">to_csv</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">   3962</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">path_or_buf</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3963</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">lineterminator</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">lineterminator</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3964</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">sep</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">sep</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3965</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">encoding</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">encoding</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3966</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">errors</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">errors</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3967</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">compression</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">compression</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3968</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">quoting</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">quoting</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3969</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">columns</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">columns</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3970</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">index_label</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">index_label</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3971</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">mode</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">mode</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3972</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">chunksize</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">chunksize</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3973</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">quotechar</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">quotechar</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3974</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">date_format</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">date_format</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3975</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">doublequote</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">doublequote</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3976</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">escapechar</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">escapechar</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3977</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">storage_options</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">storage_options</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3978</span> <span class="ansi-yellow-bg">)</span>
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/io/formats/format.py:1014</span>, in <span class="ansi-cyan-fg">DataFrameRenderer.to_csv</span><span class="ansi-blue-fg">(self, path_or_buf, encoding, sep, columns, index_label, mode, compression, quoting, quotechar, lineterminator, chunksize, date_format, doublequote, escapechar, errors, storage_options)</span>
-<span class="ansi-green-intense-fg ansi-bold">    993</span>     created_buffer <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">False</span>
-<span class="ansi-green-intense-fg ansi-bold">    995</span> csv_formatter <span style="color: rgb(98,98,98)">=</span> CSVFormatter(
-<span class="ansi-green-intense-fg ansi-bold">    996</span>     path_or_buf<span style="color: rgb(98,98,98)">=</span>path_or_buf,
-<span class="ansi-green-intense-fg ansi-bold">    997</span>     lineterminator<span style="color: rgb(98,98,98)">=</span>lineterminator,
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">   1012</span>     formatter<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>fmt,
-<span class="ansi-green-intense-fg ansi-bold">   1013</span> )
-<span class="ansi-green-fg">-&gt; 1014</span> <span class="ansi-yellow-bg">csv_formatter</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">save</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">   1016</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> created_buffer:
-<span class="ansi-green-intense-fg ansi-bold">   1017</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">assert</span> <span style="color: rgb(0,135,0)">isinstance</span>(path_or_buf, StringIO)
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/io/formats/csvs.py:251</span>, in <span class="ansi-cyan-fg">CSVFormatter.save</span><span class="ansi-blue-fg">(self)</span>
-<span class="ansi-green-intense-fg ansi-bold">    247</span> <span style="color: rgb(175,0,0)">&#34;&#34;&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">    248</span> <span style="color: rgb(175,0,0)">Create the writer &amp; save.</span>
-<span class="ansi-green-intense-fg ansi-bold">    249</span> <span style="color: rgb(175,0,0)">&#34;&#34;&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">    250</span> <span style="color: rgb(95,135,135)"># apply compression and byte/text conversion</span>
-<span class="ansi-green-fg">--&gt; 251</span> <span class="ansi-bold" style="color: rgb(0,135,0)">with</span> <span class="ansi-yellow-bg">get_handle</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">    252</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">filepath_or_buffer</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    253</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">mode</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    254</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">encoding</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">encoding</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    255</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">errors</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">errors</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    256</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">compression</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">compression</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    257</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">storage_options</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">storage_options</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    258</span> <span class="ansi-yellow-bg">)</span> <span class="ansi-bold" style="color: rgb(0,135,0)">as</span> handles:
-<span class="ansi-green-intense-fg ansi-bold">    259</span>     <span style="color: rgb(95,135,135)"># Note: self.encoding is irrelevant here</span>
-<span class="ansi-green-intense-fg ansi-bold">    260</span>     <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>writer <span style="color: rgb(98,98,98)">=</span> csvlib<span style="color: rgb(98,98,98)">.</span>writer(
-<span class="ansi-green-intense-fg ansi-bold">    261</span>         handles<span style="color: rgb(98,98,98)">.</span>handle,
-<span class="ansi-green-intense-fg ansi-bold">    262</span>         lineterminator<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>lineterminator,
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    267</span>         quotechar<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>quotechar,
-<span class="ansi-green-intense-fg ansi-bold">    268</span>     )
-<span class="ansi-green-intense-fg ansi-bold">    270</span>     <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_save()
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/io/common.py:749</span>, in <span class="ansi-cyan-fg">get_handle</span><span class="ansi-blue-fg">(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)</span>
-<span class="ansi-green-intense-fg ansi-bold">    747</span> <span style="color: rgb(95,135,135)"># Only for write methods</span>
-<span class="ansi-green-intense-fg ansi-bold">    748</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">r</span><span style="color: rgb(175,0,0)">&#34;</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> mode <span class="ansi-bold" style="color: rgb(175,0,255)">and</span> is_path:
-<span class="ansi-green-fg">--&gt; 749</span>     <span class="ansi-yellow-bg">check_parent_directory</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">str</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">handle</span><span class="ansi-yellow-bg">)</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    751</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> compression:
-<span class="ansi-green-intense-fg ansi-bold">    752</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> compression <span style="color: rgb(98,98,98)">!=</span> <span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">zstd</span><span style="color: rgb(175,0,0)">&#34;</span>:
-<span class="ansi-green-intense-fg ansi-bold">    753</span>         <span style="color: rgb(95,135,135)"># compression libraries do not like an explicit text-mode</span>
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/io/common.py:616</span>, in <span class="ansi-cyan-fg">check_parent_directory</span><span class="ansi-blue-fg">(path)</span>
-<span class="ansi-green-intense-fg ansi-bold">    614</span> parent <span style="color: rgb(98,98,98)">=</span> Path(path)<span style="color: rgb(98,98,98)">.</span>parent
-<span class="ansi-green-intense-fg ansi-bold">    615</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> parent<span style="color: rgb(98,98,98)">.</span>is_dir():
-<span class="ansi-green-fg">--&gt; 616</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">raise</span> <span class="ansi-bold" style="color: rgb(215,95,95)">OSError</span>(<span style="color: rgb(175,0,0)">rf</span><span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">Cannot save file into a non-existent directory: </span><span style="color: rgb(175,0,0)">&#39;</span><span class="ansi-bold" style="color: rgb(175,95,135)">{</span>parent<span class="ansi-bold" style="color: rgb(175,95,135)">}</span><span style="color: rgb(175,0,0)">&#39;</span><span style="color: rgb(175,0,0)">&#34;</span>)
-
-<span class="ansi-red-fg">OSError</span>: Cannot save file into a non-existent directory: &#39;/content/drive/MyDrive/misinformation-data&#39;
-</pre></div></div>
-</div>
 </section>
 </section>
 <section id="The-detector-modules">
 <h1>The detector modules<a class="headerlink" href="#The-detector-modules" title="Link to this heading"></a></h1>
 <p>The different detector modules with their options are explained in more detail in this section. ## Text detector Text on the images can be extracted using the <code class="docutils literal notranslate"><span class="pre">TextDetector</span></code> class (<code class="docutils literal notranslate"><span class="pre">text</span></code> module). The text is initally extracted using the Google Cloud Vision API and then translated into English with googletrans. The translated text is cleaned of whitespace, linebreaks, and numbers using Python syntax and spaCy.</p>
-<p><img alt="f45cb22593a7426cb0b34d5935d6c6bc" class="no-scaled-link" src="../_images/text_detector.png" style="width: 800px;" /></p>
+<p><img alt="18149daeb1af49288307fe4281b88a9c" class="no-scaled-link" src="../_images/text_detector.png" style="width: 800px;" /></p>
 <p>The user can set if the text should be further summarized, and analyzed for sentiment and named entity recognition, by setting the keyword <code class="docutils literal notranslate"><span class="pre">analyse_text</span></code> to <code class="docutils literal notranslate"><span class="pre">True</span></code> (the default is <code class="docutils literal notranslate"><span class="pre">False</span></code>). If set, the transformers pipeline is used for each of these tasks, with the default models as of 03/2023. Other models can be selected by setting the optional keyword <code class="docutils literal notranslate"><span class="pre">model_names</span></code> to a list of selected models, on for each task:
 <code class="docutils literal notranslate"><span class="pre">model_names=[&quot;sshleifer/distilbart-cnn-12-6&quot;,</span> <span class="pre">&quot;distilbert-base-uncased-finetuned-sst-2-english&quot;,</span> <span class="pre">&quot;dbmdz/bert-large-cased-finetuned-conll03-english&quot;]</span></code> for summary, sentiment, and ner. To be even more specific, revision numbers can also be selected by specifying the optional keyword <code class="docutils literal notranslate"><span class="pre">revision_numbers</span></code> to a list of revision numbers for each model, for example <code class="docutils literal notranslate"><span class="pre">revision_numbers=[&quot;a4f8f3e&quot;,</span> <span class="pre">&quot;af0f99b&quot;,</span> <span class="pre">&quot;f2482bf&quot;]</span></code>.</p>
 <p>Please note that for the Google Cloud Vision API (the TextDetector class) you need to set a key in order to process the images. This key is ideally set as an environment variable using for example</p>
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 <p>where you place the key on your Google Drive if running on colab, or place it in a local folder on your machine.</p>
 <p>Summarizing, the text detection is carried out using the following method call and keywords, where <code class="docutils literal notranslate"><span class="pre">analyse_text</span></code>, <code class="docutils literal notranslate"><span class="pre">model_names</span></code>, and <code class="docutils literal notranslate"><span class="pre">revision_numbers</span></code> are optional:</p>
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 <p>The models can be adapted interactively in the notebook interface and the best models can then be used in a subsequent analysis of the whole data set.</p>
 <p>A detailed description of the output keys and data types is given in the following table.</p>
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 <h2>Image summary and query<a class="headerlink" href="#Image-summary-and-query" title="Link to this heading"></a></h2>
 <p>The <code class="docutils literal notranslate"><span class="pre">SummaryDetector</span></code> can be used to generate image captions (<code class="docutils literal notranslate"><span class="pre">summary</span></code>) as well as visual question answering (<code class="docutils literal notranslate"><span class="pre">VQA</span></code>).</p>
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 <p>This module is based on the <a class="reference external" href="https://github.com/salesforce/LAVIS">LAVIS</a> library. Since the models can be quite large, an initial object is created which will load the necessary models into RAM/VRAM and then use them in the analysis. The user can specify the type of analysis to be performed using the <code class="docutils literal notranslate"><span class="pre">analysis_type</span></code> keyword. Setting it to <code class="docutils literal notranslate"><span class="pre">summary</span></code> will generate a caption (summary), <code class="docutils literal notranslate"><span class="pre">questions</span></code> will prepare answers (VQA) to a list of questions as set by the user,
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 <p>The implemented models are listed below.</p>
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 <p>For VQA, a list of questions needs to be passed when carrying out the analysis; these should be given as a list of strings.</p>
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-<dt>91%|█████████▏| 1.23G/1.35G [00:13&amp;lt;00:01, 96.0MB/s]</dt><dd><blockquote>
-<div><p>&lt;/pre&gt;</p>
-</div></blockquote>
-</dd>
-<dt>91%|█████████▏| 1.23G/1.35G [00:13&lt;00:01, 96.0MB/s]</dt><dd><blockquote>
-<div><p>end{sphinxVerbatim}</p>
-</div></blockquote>
-</dd>
-</dl>
-<p>91%|█████████▏| 1.23G/1.35G [00:13&lt;00:01, 96.0MB/s]</p>
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-<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
-<span class="ansi-red-fg">OSError</span>                                   Traceback (most recent call last)
-Cell <span class="ansi-green-fg">In[21], line 1</span>
-<span class="ansi-green-fg">----&gt; 1</span> image_summary_vqa_detector <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">ammico</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">SummaryDetector</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">image_dict</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">analysis_type</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">questions</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span>
-<span class="ansi-green-intense-fg ansi-bold">      2</span> <span class="ansi-yellow-bg">                                                    </span><span class="ansi-yellow-bg">model_type</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">vqa</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">      4</span> <span class="ansi-bold" style="color: rgb(0,135,0)">for</span> num, key <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> tqdm(<span style="color: rgb(0,135,0)">enumerate</span>(image_dict<span style="color: rgb(98,98,98)">.</span>keys()),total<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(0,135,0)">len</span>(image_dict)):
-<span class="ansi-green-intense-fg ansi-bold">      5</span>     image_dict[key] <span style="color: rgb(98,98,98)">=</span> image_summary_vqa_detector<span style="color: rgb(98,98,98)">.</span>analyse_image(subdict<span style="color: rgb(98,98,98)">=</span>image_dict[key],
-<span class="ansi-green-intense-fg ansi-bold">      6</span>                                                                analysis_type<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">questions</span><span style="color: rgb(175,0,0)">&#34;</span>,
-<span class="ansi-green-intense-fg ansi-bold">      7</span>                                                                list_of_questions <span style="color: rgb(98,98,98)">=</span> list_of_questions)
-
-File <span class="ansi-green-fg">~/work/AMMICO/AMMICO/ammico/summary.py:141</span>, in <span class="ansi-cyan-fg">SummaryDetector.__init__</span><span class="ansi-blue-fg">(self, subdict, model_type, analysis_type, list_of_questions, summary_model, summary_vis_processors, summary_vqa_model, summary_vqa_vis_processors, summary_vqa_txt_processors, summary_vqa_model_new, summary_vqa_vis_processors_new, summary_vqa_txt_processors_new, device_type)</span>
-<span class="ansi-green-intense-fg ansi-bold">    127</span>     <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>summary_vis_processors <span style="color: rgb(98,98,98)">=</span> summary_vis_processors
-<span class="ansi-green-intense-fg ansi-bold">    128</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> (
-<span class="ansi-green-intense-fg ansi-bold">    129</span>     model_type <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>allowed_model_types
-<span class="ansi-green-intense-fg ansi-bold">    130</span>     <span class="ansi-bold" style="color: rgb(175,0,255)">and</span> (summary_vqa_model <span class="ansi-bold" style="color: rgb(175,0,255)">is</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>)
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    135</span>     )
-<span class="ansi-green-intense-fg ansi-bold">    136</span> ):
-<span class="ansi-green-intense-fg ansi-bold">    137</span>     (
-<span class="ansi-green-intense-fg ansi-bold">    138</span>         <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>summary_vqa_model,
-<span class="ansi-green-intense-fg ansi-bold">    139</span>         <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>summary_vqa_vis_processors,
-<span class="ansi-green-intense-fg ansi-bold">    140</span>         <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>summary_vqa_txt_processors,
-<span class="ansi-green-fg">--&gt; 141</span>     ) <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">load_vqa_model</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    142</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
-<span class="ansi-green-intense-fg ansi-bold">    143</span>     <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>summary_vqa_model <span style="color: rgb(98,98,98)">=</span> summary_vqa_model
-
-File <span class="ansi-green-fg">~/work/AMMICO/AMMICO/ammico/summary.py:232</span>, in <span class="ansi-cyan-fg">SummaryDetector.load_vqa_model</span><span class="ansi-blue-fg">(self)</span>
-<span class="ansi-green-intense-fg ansi-bold">    216</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">load_vqa_model</span>(<span style="color: rgb(0,135,0)">self</span>):
-<span class="ansi-green-intense-fg ansi-bold">    217</span> <span style="color: rgb(188,188,188)">    </span><span style="color: rgb(175,0,0)">&#34;&#34;&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">    218</span> <span style="color: rgb(175,0,0)">    Load blip_vqa model and preprocessors for visual and text inputs from lavis.models.</span>
-<span class="ansi-green-intense-fg ansi-bold">    219</span>
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    226</span>
-<span class="ansi-green-intense-fg ansi-bold">    227</span> <span style="color: rgb(175,0,0)">    &#34;&#34;&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">    228</span>     (
-<span class="ansi-green-intense-fg ansi-bold">    229</span>         summary_vqa_model,
-<span class="ansi-green-intense-fg ansi-bold">    230</span>         summary_vqa_vis_processors,
-<span class="ansi-green-intense-fg ansi-bold">    231</span>         summary_vqa_txt_processors,
-<span class="ansi-green-fg">--&gt; 232</span>     ) <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">load_model_and_preprocess</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">    233</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">name</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">blip_vqa</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    234</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">model_type</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">vqav2</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    235</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">is_eval</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(0,135,0)">True</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    236</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">device</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">summary_device</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    237</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    238</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> summary_vqa_model, summary_vqa_vis_processors, summary_vqa_txt_processors
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/__init__.py:195</span>, in <span class="ansi-cyan-fg">load_model_and_preprocess</span><span class="ansi-blue-fg">(name, model_type, is_eval, device)</span>
-<span class="ansi-green-intense-fg ansi-bold">    192</span> model_cls <span style="color: rgb(98,98,98)">=</span> registry<span style="color: rgb(98,98,98)">.</span>get_model_class(name)
-<span class="ansi-green-intense-fg ansi-bold">    194</span> <span style="color: rgb(95,135,135)"># load model</span>
-<span class="ansi-green-fg">--&gt; 195</span> model <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">model_cls</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">from_pretrained</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">model_type</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">model_type</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    197</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> is_eval:
-<span class="ansi-green-intense-fg ansi-bold">    198</span>     model<span style="color: rgb(98,98,98)">.</span>eval()
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/base_model.py:70</span>, in <span class="ansi-cyan-fg">BaseModel.from_pretrained</span><span class="ansi-blue-fg">(cls, model_type)</span>
-<span class="ansi-green-intense-fg ansi-bold">     60</span> <span style="color: rgb(175,0,0)">&#34;&#34;&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">     61</span> <span style="color: rgb(175,0,0)">Build a pretrained model from default configuration file, specified by model_type.</span>
-<span class="ansi-green-intense-fg ansi-bold">     62</span>
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">     67</span> <span style="color: rgb(175,0,0)">    - model (nn.Module): pretrained or finetuned model, depending on the configuration.</span>
-<span class="ansi-green-intense-fg ansi-bold">     68</span> <span style="color: rgb(175,0,0)">&#34;&#34;&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">     69</span> model_cfg <span style="color: rgb(98,98,98)">=</span> OmegaConf<span style="color: rgb(98,98,98)">.</span>load(<span style="color: rgb(0,135,0)">cls</span><span style="color: rgb(98,98,98)">.</span>default_config_path(model_type))<span style="color: rgb(98,98,98)">.</span>model
-<span class="ansi-green-fg">---&gt; 70</span> model <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">cls</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">from_config</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">model_cfg</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">     72</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> model
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/blip_models/blip_vqa.py:373</span>, in <span class="ansi-cyan-fg">BlipVQA.from_config</span><span class="ansi-blue-fg">(cls, cfg)</span>
-<span class="ansi-green-intense-fg ansi-bold">    364</span> max_txt_len <span style="color: rgb(98,98,98)">=</span> cfg<span style="color: rgb(98,98,98)">.</span>get(<span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">max_txt_len</span><span style="color: rgb(175,0,0)">&#34;</span>, <span style="color: rgb(98,98,98)">35</span>)
-<span class="ansi-green-intense-fg ansi-bold">    366</span> model <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">cls</span>(
-<span class="ansi-green-intense-fg ansi-bold">    367</span>     image_encoder<span style="color: rgb(98,98,98)">=</span>image_encoder,
-<span class="ansi-green-intense-fg ansi-bold">    368</span>     text_encoder<span style="color: rgb(98,98,98)">=</span>text_encoder,
-<span class="ansi-green-intense-fg ansi-bold">    369</span>     text_decoder<span style="color: rgb(98,98,98)">=</span>text_decoder,
-<span class="ansi-green-intense-fg ansi-bold">    370</span>     max_txt_len<span style="color: rgb(98,98,98)">=</span>max_txt_len,
-<span class="ansi-green-intense-fg ansi-bold">    371</span> )
-<span class="ansi-green-fg">--&gt; 373</span> <span class="ansi-yellow-bg">model</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">load_checkpoint_from_config</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">cfg</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    375</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> model
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/base_model.py:95</span>, in <span class="ansi-cyan-fg">BaseModel.load_checkpoint_from_config</span><span class="ansi-blue-fg">(self, cfg, **kwargs)</span>
-<span class="ansi-green-intense-fg ansi-bold">     91</span>     finetune_path <span style="color: rgb(98,98,98)">=</span> cfg<span style="color: rgb(98,98,98)">.</span>get(<span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">finetuned</span><span style="color: rgb(175,0,0)">&#34;</span>, <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>)
-<span class="ansi-green-intense-fg ansi-bold">     92</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">assert</span> (
-<span class="ansi-green-intense-fg ansi-bold">     93</span>         finetune_path <span class="ansi-bold" style="color: rgb(175,0,255)">is</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
-<span class="ansi-green-intense-fg ansi-bold">     94</span>     ), <span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">Found load_finetuned is True, but finetune_path is None.</span><span style="color: rgb(175,0,0)">&#34;</span>
-<span class="ansi-green-fg">---&gt; 95</span>     <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">load_checkpoint</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">url_or_filename</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">finetune_path</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">     96</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
-<span class="ansi-green-intense-fg ansi-bold">     97</span>     <span style="color: rgb(95,135,135)"># load pre-trained weights</span>
-<span class="ansi-green-intense-fg ansi-bold">     98</span>     pretrain_path <span style="color: rgb(98,98,98)">=</span> cfg<span style="color: rgb(98,98,98)">.</span>get(<span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">pretrained</span><span style="color: rgb(175,0,0)">&#34;</span>, <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>)
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/base_model.py:37</span>, in <span class="ansi-cyan-fg">BaseModel.load_checkpoint</span><span class="ansi-blue-fg">(self, url_or_filename)</span>
-<span class="ansi-green-intense-fg ansi-bold">     30</span> <span style="color: rgb(175,0,0)">&#34;&#34;&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">     31</span> <span style="color: rgb(175,0,0)">Load from a finetuned checkpoint.</span>
-<span class="ansi-green-intense-fg ansi-bold">     32</span>
-<span class="ansi-green-intense-fg ansi-bold">     33</span> <span style="color: rgb(175,0,0)">This should expect no mismatch in the model keys and the checkpoint keys.</span>
-<span class="ansi-green-intense-fg ansi-bold">     34</span> <span style="color: rgb(175,0,0)">&#34;&#34;&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">     36</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> is_url(url_or_filename):
-<span class="ansi-green-fg">---&gt; 37</span>     cached_file <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">download_cached_file</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">     38</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">url_or_filename</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">check_hash</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(0,135,0)">False</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">progress</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(0,135,0)">True</span>
-<span class="ansi-green-intense-fg ansi-bold">     39</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">     40</span>     checkpoint <span style="color: rgb(98,98,98)">=</span> torch<span style="color: rgb(98,98,98)">.</span>load(cached_file, map_location<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">cpu</span><span style="color: rgb(175,0,0)">&#34;</span>)
-<span class="ansi-green-intense-fg ansi-bold">     41</span> <span class="ansi-bold" style="color: rgb(0,135,0)">elif</span> os<span style="color: rgb(98,98,98)">.</span>path<span style="color: rgb(98,98,98)">.</span>isfile(url_or_filename):
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/common/dist_utils.py:132</span>, in <span class="ansi-cyan-fg">download_cached_file</span><span class="ansi-blue-fg">(url, check_hash, progress)</span>
-<span class="ansi-green-intense-fg ansi-bold">    129</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> cached_file
-<span class="ansi-green-intense-fg ansi-bold">    131</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> is_main_process():
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-Cell <span class="ansi-green-fg">In[22], line 1</span>
-<span class="ansi-green-fg">----&gt; 1</span> image_summary_vqa_detector <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">ammico</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">SummaryDetector</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">image_dict</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">analysis_type</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">summary_and_questions</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span>
-<span class="ansi-green-intense-fg ansi-bold">      2</span> <span class="ansi-yellow-bg">                                                    </span><span class="ansi-yellow-bg">model_type</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">base</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">      3</span> <span class="ansi-bold" style="color: rgb(0,135,0)">for</span> num, key <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> tqdm(<span style="color: rgb(0,135,0)">enumerate</span>(image_dict<span style="color: rgb(98,98,98)">.</span>keys()),total<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(0,135,0)">len</span>(image_dict)):
-<span class="ansi-green-intense-fg ansi-bold">      4</span>     image_dict[key] <span style="color: rgb(98,98,98)">=</span> image_summary_vqa_detector<span style="color: rgb(98,98,98)">.</span>analyse_image(subdict<span style="color: rgb(98,98,98)">=</span>image_dict[key],
-<span class="ansi-green-intense-fg ansi-bold">      5</span>                                                                analysis_type<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">summary_and_questions</span><span style="color: rgb(175,0,0)">&#34;</span>,
-<span class="ansi-green-intense-fg ansi-bold">      6</span>                                                                list_of_questions <span style="color: rgb(98,98,98)">=</span> list_of_questions)
-
-File <span class="ansi-green-fg">~/work/AMMICO/AMMICO/ammico/summary.py:141</span>, in <span class="ansi-cyan-fg">SummaryDetector.__init__</span><span class="ansi-blue-fg">(self, subdict, model_type, analysis_type, list_of_questions, summary_model, summary_vis_processors, summary_vqa_model, summary_vqa_vis_processors, summary_vqa_txt_processors, summary_vqa_model_new, summary_vqa_vis_processors_new, summary_vqa_txt_processors_new, device_type)</span>
-<span class="ansi-green-intense-fg ansi-bold">    127</span>     <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>summary_vis_processors <span style="color: rgb(98,98,98)">=</span> summary_vis_processors
-<span class="ansi-green-intense-fg ansi-bold">    128</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> (
-<span class="ansi-green-intense-fg ansi-bold">    129</span>     model_type <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>allowed_model_types
-<span class="ansi-green-intense-fg ansi-bold">    130</span>     <span class="ansi-bold" style="color: rgb(175,0,255)">and</span> (summary_vqa_model <span class="ansi-bold" style="color: rgb(175,0,255)">is</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>)
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    135</span>     )
-<span class="ansi-green-intense-fg ansi-bold">    136</span> ):
-<span class="ansi-green-intense-fg ansi-bold">    137</span>     (
-<span class="ansi-green-intense-fg ansi-bold">    138</span>         <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>summary_vqa_model,
-<span class="ansi-green-intense-fg ansi-bold">    139</span>         <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>summary_vqa_vis_processors,
-<span class="ansi-green-intense-fg ansi-bold">    140</span>         <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>summary_vqa_txt_processors,
-<span class="ansi-green-fg">--&gt; 141</span>     ) <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">load_vqa_model</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    142</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
-<span class="ansi-green-intense-fg ansi-bold">    143</span>     <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>summary_vqa_model <span style="color: rgb(98,98,98)">=</span> summary_vqa_model
-
-File <span class="ansi-green-fg">~/work/AMMICO/AMMICO/ammico/summary.py:232</span>, in <span class="ansi-cyan-fg">SummaryDetector.load_vqa_model</span><span class="ansi-blue-fg">(self)</span>
-<span class="ansi-green-intense-fg ansi-bold">    216</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">load_vqa_model</span>(<span style="color: rgb(0,135,0)">self</span>):
-<span class="ansi-green-intense-fg ansi-bold">    217</span> <span style="color: rgb(188,188,188)">    </span><span style="color: rgb(175,0,0)">&#34;&#34;&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">    218</span> <span style="color: rgb(175,0,0)">    Load blip_vqa model and preprocessors for visual and text inputs from lavis.models.</span>
-<span class="ansi-green-intense-fg ansi-bold">    219</span>
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    226</span>
-<span class="ansi-green-intense-fg ansi-bold">    227</span> <span style="color: rgb(175,0,0)">    &#34;&#34;&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">    228</span>     (
-<span class="ansi-green-intense-fg ansi-bold">    229</span>         summary_vqa_model,
-<span class="ansi-green-intense-fg ansi-bold">    230</span>         summary_vqa_vis_processors,
-<span class="ansi-green-intense-fg ansi-bold">    231</span>         summary_vqa_txt_processors,
-<span class="ansi-green-fg">--&gt; 232</span>     ) <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">load_model_and_preprocess</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">    233</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">name</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">blip_vqa</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    234</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">model_type</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">vqav2</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    235</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">is_eval</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(0,135,0)">True</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    236</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">device</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">summary_device</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    237</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    238</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> summary_vqa_model, summary_vqa_vis_processors, summary_vqa_txt_processors
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/__init__.py:195</span>, in <span class="ansi-cyan-fg">load_model_and_preprocess</span><span class="ansi-blue-fg">(name, model_type, is_eval, device)</span>
-<span class="ansi-green-intense-fg ansi-bold">    192</span> model_cls <span style="color: rgb(98,98,98)">=</span> registry<span style="color: rgb(98,98,98)">.</span>get_model_class(name)
-<span class="ansi-green-intense-fg ansi-bold">    194</span> <span style="color: rgb(95,135,135)"># load model</span>
-<span class="ansi-green-fg">--&gt; 195</span> model <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">model_cls</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">from_pretrained</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">model_type</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">model_type</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    197</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> is_eval:
-<span class="ansi-green-intense-fg ansi-bold">    198</span>     model<span style="color: rgb(98,98,98)">.</span>eval()
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/base_model.py:70</span>, in <span class="ansi-cyan-fg">BaseModel.from_pretrained</span><span class="ansi-blue-fg">(cls, model_type)</span>
-<span class="ansi-green-intense-fg ansi-bold">     60</span> <span style="color: rgb(175,0,0)">&#34;&#34;&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">     61</span> <span style="color: rgb(175,0,0)">Build a pretrained model from default configuration file, specified by model_type.</span>
-<span class="ansi-green-intense-fg ansi-bold">     62</span>
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">     67</span> <span style="color: rgb(175,0,0)">    - model (nn.Module): pretrained or finetuned model, depending on the configuration.</span>
-<span class="ansi-green-intense-fg ansi-bold">     68</span> <span style="color: rgb(175,0,0)">&#34;&#34;&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">     69</span> model_cfg <span style="color: rgb(98,98,98)">=</span> OmegaConf<span style="color: rgb(98,98,98)">.</span>load(<span style="color: rgb(0,135,0)">cls</span><span style="color: rgb(98,98,98)">.</span>default_config_path(model_type))<span style="color: rgb(98,98,98)">.</span>model
-<span class="ansi-green-fg">---&gt; 70</span> model <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">cls</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">from_config</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">model_cfg</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">     72</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> model
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/blip_models/blip_vqa.py:373</span>, in <span class="ansi-cyan-fg">BlipVQA.from_config</span><span class="ansi-blue-fg">(cls, cfg)</span>
-<span class="ansi-green-intense-fg ansi-bold">    364</span> max_txt_len <span style="color: rgb(98,98,98)">=</span> cfg<span style="color: rgb(98,98,98)">.</span>get(<span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">max_txt_len</span><span style="color: rgb(175,0,0)">&#34;</span>, <span style="color: rgb(98,98,98)">35</span>)
-<span class="ansi-green-intense-fg ansi-bold">    366</span> model <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">cls</span>(
-<span class="ansi-green-intense-fg ansi-bold">    367</span>     image_encoder<span style="color: rgb(98,98,98)">=</span>image_encoder,
-<span class="ansi-green-intense-fg ansi-bold">    368</span>     text_encoder<span style="color: rgb(98,98,98)">=</span>text_encoder,
-<span class="ansi-green-intense-fg ansi-bold">    369</span>     text_decoder<span style="color: rgb(98,98,98)">=</span>text_decoder,
-<span class="ansi-green-intense-fg ansi-bold">    370</span>     max_txt_len<span style="color: rgb(98,98,98)">=</span>max_txt_len,
-<span class="ansi-green-intense-fg ansi-bold">    371</span> )
-<span class="ansi-green-fg">--&gt; 373</span> <span class="ansi-yellow-bg">model</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">load_checkpoint_from_config</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">cfg</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    375</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> model
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/base_model.py:95</span>, in <span class="ansi-cyan-fg">BaseModel.load_checkpoint_from_config</span><span class="ansi-blue-fg">(self, cfg, **kwargs)</span>
-<span class="ansi-green-intense-fg ansi-bold">     91</span>     finetune_path <span style="color: rgb(98,98,98)">=</span> cfg<span style="color: rgb(98,98,98)">.</span>get(<span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">finetuned</span><span style="color: rgb(175,0,0)">&#34;</span>, <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>)
-<span class="ansi-green-intense-fg ansi-bold">     92</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">assert</span> (
-<span class="ansi-green-intense-fg ansi-bold">     93</span>         finetune_path <span class="ansi-bold" style="color: rgb(175,0,255)">is</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
-<span class="ansi-green-intense-fg ansi-bold">     94</span>     ), <span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">Found load_finetuned is True, but finetune_path is None.</span><span style="color: rgb(175,0,0)">&#34;</span>
-<span class="ansi-green-fg">---&gt; 95</span>     <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">load_checkpoint</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">url_or_filename</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">finetune_path</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">     96</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
-<span class="ansi-green-intense-fg ansi-bold">     97</span>     <span style="color: rgb(95,135,135)"># load pre-trained weights</span>
-<span class="ansi-green-intense-fg ansi-bold">     98</span>     pretrain_path <span style="color: rgb(98,98,98)">=</span> cfg<span style="color: rgb(98,98,98)">.</span>get(<span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">pretrained</span><span style="color: rgb(175,0,0)">&#34;</span>, <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>)
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/base_model.py:37</span>, in <span class="ansi-cyan-fg">BaseModel.load_checkpoint</span><span class="ansi-blue-fg">(self, url_or_filename)</span>
-<span class="ansi-green-intense-fg ansi-bold">     30</span> <span style="color: rgb(175,0,0)">&#34;&#34;&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">     31</span> <span style="color: rgb(175,0,0)">Load from a finetuned checkpoint.</span>
-<span class="ansi-green-intense-fg ansi-bold">     32</span>
-<span class="ansi-green-intense-fg ansi-bold">     33</span> <span style="color: rgb(175,0,0)">This should expect no mismatch in the model keys and the checkpoint keys.</span>
-<span class="ansi-green-intense-fg ansi-bold">     34</span> <span style="color: rgb(175,0,0)">&#34;&#34;&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">     36</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> is_url(url_or_filename):
-<span class="ansi-green-fg">---&gt; 37</span>     cached_file <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">download_cached_file</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">     38</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">url_or_filename</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">check_hash</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(0,135,0)">False</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">progress</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(0,135,0)">True</span>
-<span class="ansi-green-intense-fg ansi-bold">     39</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">     40</span>     checkpoint <span style="color: rgb(98,98,98)">=</span> torch<span style="color: rgb(98,98,98)">.</span>load(cached_file, map_location<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">cpu</span><span style="color: rgb(175,0,0)">&#34;</span>)
-<span class="ansi-green-intense-fg ansi-bold">     41</span> <span class="ansi-bold" style="color: rgb(0,135,0)">elif</span> os<span style="color: rgb(98,98,98)">.</span>path<span style="color: rgb(98,98,98)">.</span>isfile(url_or_filename):
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/common/dist_utils.py:132</span>, in <span class="ansi-cyan-fg">download_cached_file</span><span class="ansi-blue-fg">(url, check_hash, progress)</span>
-<span class="ansi-green-intense-fg ansi-bold">    129</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> cached_file
-<span class="ansi-green-intense-fg ansi-bold">    131</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> is_main_process():
-<span class="ansi-green-fg">--&gt; 132</span>     <span class="ansi-yellow-bg">timm_hub</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">download_cached_file</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">url</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">check_hash</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">progress</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    134</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> is_dist_avail_and_initialized():
-<span class="ansi-green-intense-fg ansi-bold">    135</span>     dist<span style="color: rgb(98,98,98)">.</span>barrier()
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/timm/models/hub.py:51</span>, in <span class="ansi-cyan-fg">download_cached_file</span><span class="ansi-blue-fg">(url, check_hash, progress)</span>
-<span class="ansi-green-intense-fg ansi-bold">     49</span>         r <span style="color: rgb(98,98,98)">=</span> HASH_REGEX<span style="color: rgb(98,98,98)">.</span>search(filename)  <span style="color: rgb(95,135,135)"># r is Optional[Match[str]]</span>
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@@ -17869,8 +612,8 @@ File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/pyth
 <section id="BLIP2-models">
 <h3>BLIP2 models<a class="headerlink" href="#BLIP2-models" title="Link to this heading"></a></h3>
 <p>This is very heavy models. They requare approx 60GB of RAM and they can use more than 20GB GPUs memory.</p>
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-<span class="ansi-red-fg">OSError</span>                                   Traceback (most recent call last)
-Cell <span class="ansi-green-fg">In[23], line 1</span>
-<span class="ansi-green-fg">----&gt; 1</span> obj <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">ammico</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">SummaryDetector</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">subdict</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">image_dict</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">analysis_type</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">summary_and_questions</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">model_type</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">blip2_t5_caption_coco_flant5xl</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">      2</span> <span style="color: rgb(95,135,135)"># list of the new models that can be used:</span>
-<span class="ansi-green-intense-fg ansi-bold">      3</span> <span style="color: rgb(95,135,135)"># &#34;blip2_t5_pretrain_flant5xxl&#34;,</span>
-<span class="ansi-green-intense-fg ansi-bold">      4</span> <span style="color: rgb(95,135,135)"># &#34;blip2_t5_pretrain_flant5xl&#34;,</span>
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">     14</span>
-<span class="ansi-green-intense-fg ansi-bold">     15</span> <span style="color: rgb(95,135,135)">#also you can perform all calculation on cpu if you set device_type= &#34;cpu&#34; or gpu if you set device_type= &#34;cuda&#34;</span>
-
-File <span class="ansi-green-fg">~/work/AMMICO/AMMICO/ammico/summary.py:156</span>, in <span class="ansi-cyan-fg">SummaryDetector.__init__</span><span class="ansi-blue-fg">(self, subdict, model_type, analysis_type, list_of_questions, summary_model, summary_vis_processors, summary_vqa_model, summary_vqa_vis_processors, summary_vqa_txt_processors, summary_vqa_model_new, summary_vqa_vis_processors_new, summary_vqa_txt_processors_new, device_type)</span>
-<span class="ansi-green-intense-fg ansi-bold">    145</span>     <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>summary_vqa_txt_processors <span style="color: rgb(98,98,98)">=</span> summary_vqa_txt_processors
-<span class="ansi-green-intense-fg ansi-bold">    146</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> (
-<span class="ansi-green-intense-fg ansi-bold">    147</span>     model_type <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>allowed_new_model_types
-<span class="ansi-green-intense-fg ansi-bold">    148</span>     <span class="ansi-bold" style="color: rgb(175,0,255)">and</span> (summary_vqa_model_new <span class="ansi-bold" style="color: rgb(175,0,255)">is</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>)
-<span class="ansi-green-intense-fg ansi-bold">    149</span>     <span class="ansi-bold" style="color: rgb(175,0,255)">and</span> (summary_vqa_vis_processors_new <span class="ansi-bold" style="color: rgb(175,0,255)">is</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>)
-<span class="ansi-green-intense-fg ansi-bold">    150</span>     <span class="ansi-bold" style="color: rgb(175,0,255)">and</span> (summary_vqa_txt_processors_new <span class="ansi-bold" style="color: rgb(175,0,255)">is</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>)
-<span class="ansi-green-intense-fg ansi-bold">    151</span> ):
-<span class="ansi-green-intense-fg ansi-bold">    152</span>     (
-<span class="ansi-green-intense-fg ansi-bold">    153</span>         <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>summary_vqa_model_new,
-<span class="ansi-green-intense-fg ansi-bold">    154</span>         <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>summary_vqa_vis_processors_new,
-<span class="ansi-green-intense-fg ansi-bold">    155</span>         <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>summary_vqa_txt_processors_new,
-<span class="ansi-green-fg">--&gt; 156</span>     ) <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">load_new_model</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">model_type</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">model_type</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    157</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
-<span class="ansi-green-intense-fg ansi-bold">    158</span>     <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>summary_vqa_model_new <span style="color: rgb(98,98,98)">=</span> summary_vqa_model_new
-
-File <span class="ansi-green-fg">~/work/AMMICO/AMMICO/ammico/summary.py:479</span>, in <span class="ansi-cyan-fg">SummaryDetector.load_new_model</span><span class="ansi-blue-fg">(self, model_type)</span>
-<span class="ansi-green-intense-fg ansi-bold">    455</span> <span style="color: rgb(175,0,0)">&#34;&#34;&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">    456</span> <span style="color: rgb(175,0,0)">Load new BLIP2 models.</span>
-<span class="ansi-green-intense-fg ansi-bold">    457</span>
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    464</span> <span style="color: rgb(175,0,0)">    txt_processors (dict): preprocessors for text inputs.</span>
-<span class="ansi-green-intense-fg ansi-bold">    465</span> <span style="color: rgb(175,0,0)">&#34;&#34;&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">    466</span> select_model <span style="color: rgb(98,98,98)">=</span> {
-<span class="ansi-green-intense-fg ansi-bold">    467</span>     <span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">blip2_t5_pretrain_flant5xxl</span><span style="color: rgb(175,0,0)">&#34;</span>: SummaryDetector<span style="color: rgb(98,98,98)">.</span>load_model_blip2_t5_pretrain_flant5xxl,
-<span class="ansi-green-intense-fg ansi-bold">    468</span>     <span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">blip2_t5_pretrain_flant5xl</span><span style="color: rgb(175,0,0)">&#34;</span>: SummaryDetector<span style="color: rgb(98,98,98)">.</span>load_model_blip2_t5_pretrain_flant5xl,
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    473</span>     <span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">blip2_opt_caption_coco_opt6.7b</span><span style="color: rgb(175,0,0)">&#34;</span>: SummaryDetector<span style="color: rgb(98,98,98)">.</span>load_model_base_blip2_opt_caption_coco_opt67b,
-<span class="ansi-green-intense-fg ansi-bold">    474</span> }
-<span class="ansi-green-intense-fg ansi-bold">    475</span> (
-<span class="ansi-green-intense-fg ansi-bold">    476</span>     summary_vqa_model,
-<span class="ansi-green-intense-fg ansi-bold">    477</span>     summary_vqa_vis_processors,
-<span class="ansi-green-intense-fg ansi-bold">    478</span>     summary_vqa_txt_processors,
-<span class="ansi-green-fg">--&gt; 479</span> ) <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">select_model</span><span class="ansi-yellow-bg">[</span><span class="ansi-yellow-bg">model_type</span><span class="ansi-yellow-bg">]</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    480</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> summary_vqa_model, summary_vqa_vis_processors, summary_vqa_txt_processors
-
-File <span class="ansi-green-fg">~/work/AMMICO/AMMICO/ammico/summary.py:543</span>, in <span class="ansi-cyan-fg">SummaryDetector.load_model_blip2_t5_caption_coco_flant5xl</span><span class="ansi-blue-fg">(self)</span>
-<span class="ansi-green-intense-fg ansi-bold">    528</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">load_model_blip2_t5_caption_coco_flant5xl</span>(<span style="color: rgb(0,135,0)">self</span>):
-<span class="ansi-green-intense-fg ansi-bold">    529</span> <span style="color: rgb(188,188,188)">    </span><span style="color: rgb(175,0,0)">&#34;&#34;&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">    530</span> <span style="color: rgb(175,0,0)">    Load BLIP2 model with caption_coco_flant5xl architecture.</span>
-<span class="ansi-green-intense-fg ansi-bold">    531</span>
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    537</span> <span style="color: rgb(175,0,0)">        txt_processors (dict): preprocessors for text inputs.</span>
-<span class="ansi-green-intense-fg ansi-bold">    538</span> <span style="color: rgb(175,0,0)">    &#34;&#34;&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">    539</span>     (
-<span class="ansi-green-intense-fg ansi-bold">    540</span>         summary_vqa_model,
-<span class="ansi-green-intense-fg ansi-bold">    541</span>         summary_vqa_vis_processors,
-<span class="ansi-green-intense-fg ansi-bold">    542</span>         summary_vqa_txt_processors,
-<span class="ansi-green-fg">--&gt; 543</span>     ) <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">load_model_and_preprocess</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">    544</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">name</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">blip2_t5</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    545</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">model_type</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">caption_coco_flant5xl</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    546</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">is_eval</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(0,135,0)">True</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    547</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">device</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">summary_device</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    548</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    549</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> summary_vqa_model, summary_vqa_vis_processors, summary_vqa_txt_processors
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/__init__.py:195</span>, in <span class="ansi-cyan-fg">load_model_and_preprocess</span><span class="ansi-blue-fg">(name, model_type, is_eval, device)</span>
-<span class="ansi-green-intense-fg ansi-bold">    192</span> model_cls <span style="color: rgb(98,98,98)">=</span> registry<span style="color: rgb(98,98,98)">.</span>get_model_class(name)
-<span class="ansi-green-intense-fg ansi-bold">    194</span> <span style="color: rgb(95,135,135)"># load model</span>
-<span class="ansi-green-fg">--&gt; 195</span> model <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">model_cls</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">from_pretrained</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">model_type</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">model_type</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    197</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> is_eval:
-<span class="ansi-green-intense-fg ansi-bold">    198</span>     model<span style="color: rgb(98,98,98)">.</span>eval()
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/base_model.py:70</span>, in <span class="ansi-cyan-fg">BaseModel.from_pretrained</span><span class="ansi-blue-fg">(cls, model_type)</span>
-<span class="ansi-green-intense-fg ansi-bold">     60</span> <span style="color: rgb(175,0,0)">&#34;&#34;&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">     61</span> <span style="color: rgb(175,0,0)">Build a pretrained model from default configuration file, specified by model_type.</span>
-<span class="ansi-green-intense-fg ansi-bold">     62</span>
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">     67</span> <span style="color: rgb(175,0,0)">    - model (nn.Module): pretrained or finetuned model, depending on the configuration.</span>
-<span class="ansi-green-intense-fg ansi-bold">     68</span> <span style="color: rgb(175,0,0)">&#34;&#34;&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">     69</span> model_cfg <span style="color: rgb(98,98,98)">=</span> OmegaConf<span style="color: rgb(98,98,98)">.</span>load(<span style="color: rgb(0,135,0)">cls</span><span style="color: rgb(98,98,98)">.</span>default_config_path(model_type))<span style="color: rgb(98,98,98)">.</span>model
-<span class="ansi-green-fg">---&gt; 70</span> model <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">cls</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">from_config</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">model_cfg</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">     72</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> model
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/blip2_models/blip2_t5.py:368</span>, in <span class="ansi-cyan-fg">Blip2T5.from_config</span><span class="ansi-blue-fg">(cls, cfg)</span>
-<span class="ansi-green-intense-fg ansi-bold">    364</span> max_txt_len <span style="color: rgb(98,98,98)">=</span> cfg<span style="color: rgb(98,98,98)">.</span>get(<span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">max_txt_len</span><span style="color: rgb(175,0,0)">&#34;</span>, <span style="color: rgb(98,98,98)">32</span>)
-<span class="ansi-green-intense-fg ansi-bold">    366</span> apply_lemmatizer <span style="color: rgb(98,98,98)">=</span> cfg<span style="color: rgb(98,98,98)">.</span>get(<span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">apply_lemmatizer</span><span style="color: rgb(175,0,0)">&#34;</span>, <span class="ansi-bold" style="color: rgb(0,135,0)">False</span>)
-<span class="ansi-green-fg">--&gt; 368</span> model <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">cls</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">    369</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">vit_model</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">vit_model</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    370</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">img_size</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">img_size</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    371</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">drop_path_rate</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">drop_path_rate</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    372</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">use_grad_checkpoint</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">use_grad_checkpoint</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    373</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">vit_precision</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">vit_precision</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    374</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">freeze_vit</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">freeze_vit</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    375</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">num_query_token</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">num_query_token</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    376</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">t5_model</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">t5_model</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    377</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">prompt</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">prompt</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    378</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">max_txt_len</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">max_txt_len</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    379</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">apply_lemmatizer</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">apply_lemmatizer</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    380</span> <span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    381</span> model<span style="color: rgb(98,98,98)">.</span>load_checkpoint_from_config(cfg)
-<span class="ansi-green-intense-fg ansi-bold">    383</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> model
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/blip2_models/blip2_t5.py:61</span>, in <span class="ansi-cyan-fg">Blip2T5.__init__</span><span class="ansi-blue-fg">(self, vit_model, img_size, drop_path_rate, use_grad_checkpoint, vit_precision, freeze_vit, num_query_token, t5_model, prompt, max_txt_len, apply_lemmatizer)</span>
-<span class="ansi-green-intense-fg ansi-bold">     57</span> <span style="color: rgb(0,135,0)">super</span>()<span style="color: rgb(98,98,98)">.</span><span style="color: rgb(0,0,255)">__init__</span>()
-<span class="ansi-green-intense-fg ansi-bold">     59</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>tokenizer <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>init_tokenizer()
-<span class="ansi-green-fg">---&gt; 61</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>visual_encoder, <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>ln_vision <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">init_vision_encoder</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">     62</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">vit_model</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">img_size</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">drop_path_rate</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">use_grad_checkpoint</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">vit_precision</span>
-<span class="ansi-green-intense-fg ansi-bold">     63</span> <span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">     64</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> freeze_vit:
-<span class="ansi-green-intense-fg ansi-bold">     65</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">for</span> name, param <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>visual_encoder<span style="color: rgb(98,98,98)">.</span>named_parameters():
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/blip2_models/blip2.py:72</span>, in <span class="ansi-cyan-fg">Blip2Base.init_vision_encoder</span><span class="ansi-blue-fg">(cls, model_name, img_size, drop_path_rate, use_grad_checkpoint, precision)</span>
-<span class="ansi-green-intense-fg ansi-bold">     67</span> <span class="ansi-bold" style="color: rgb(0,135,0)">assert</span> model_name <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> [
-<span class="ansi-green-intense-fg ansi-bold">     68</span>     <span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">eva_clip_g</span><span style="color: rgb(175,0,0)">&#34;</span>,
-<span class="ansi-green-intense-fg ansi-bold">     69</span>     <span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">clip_L</span><span style="color: rgb(175,0,0)">&#34;</span>,
-<span class="ansi-green-intense-fg ansi-bold">     70</span> ], <span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">vit model must be eva_clip_g or clip_L</span><span style="color: rgb(175,0,0)">&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">     71</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> model_name <span style="color: rgb(98,98,98)">==</span> <span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">eva_clip_g</span><span style="color: rgb(175,0,0)">&#34;</span>:
-<span class="ansi-green-fg">---&gt; 72</span>     visual_encoder <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">create_eva_vit_g</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">     73</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">img_size</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">drop_path_rate</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">use_grad_checkpoint</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">precision</span>
-<span class="ansi-green-intense-fg ansi-bold">     74</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">     75</span> <span class="ansi-bold" style="color: rgb(0,135,0)">elif</span> model_name <span style="color: rgb(98,98,98)">==</span> <span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">clip_L</span><span style="color: rgb(175,0,0)">&#34;</span>:
-<span class="ansi-green-intense-fg ansi-bold">     76</span>     visual_encoder <span style="color: rgb(98,98,98)">=</span> create_clip_vit_L(img_size, use_grad_checkpoint, precision)
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/eva_vit.py:430</span>, in <span class="ansi-cyan-fg">create_eva_vit_g</span><span class="ansi-blue-fg">(img_size, drop_path_rate, use_checkpoint, precision)</span>
-<span class="ansi-green-intense-fg ansi-bold">    416</span> model <span style="color: rgb(98,98,98)">=</span> VisionTransformer(
-<span class="ansi-green-intense-fg ansi-bold">    417</span>     img_size<span style="color: rgb(98,98,98)">=</span>img_size,
-<span class="ansi-green-intense-fg ansi-bold">    418</span>     patch_size<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(98,98,98)">14</span>,
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    427</span>     use_checkpoint<span style="color: rgb(98,98,98)">=</span>use_checkpoint,
-<span class="ansi-green-intense-fg ansi-bold">    428</span> )
-<span class="ansi-green-intense-fg ansi-bold">    429</span> url <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/eva_vit_g.pth</span><span style="color: rgb(175,0,0)">&#34;</span>
-<span class="ansi-green-fg">--&gt; 430</span> cached_file <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">download_cached_file</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">    431</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">url</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">check_hash</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(0,135,0)">False</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">progress</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(0,135,0)">True</span>
-<span class="ansi-green-intense-fg ansi-bold">    432</span> <span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    433</span> state_dict <span style="color: rgb(98,98,98)">=</span> torch<span style="color: rgb(98,98,98)">.</span>load(cached_file, map_location<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">cpu</span><span style="color: rgb(175,0,0)">&#34;</span>)
-<span class="ansi-green-intense-fg ansi-bold">    434</span> interpolate_pos_embed(model,state_dict)
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/common/dist_utils.py:132</span>, in <span class="ansi-cyan-fg">download_cached_file</span><span class="ansi-blue-fg">(url, check_hash, progress)</span>
-<span class="ansi-green-intense-fg ansi-bold">    129</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> cached_file
-<span class="ansi-green-intense-fg ansi-bold">    131</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> is_main_process():
-<span class="ansi-green-fg">--&gt; 132</span>     <span class="ansi-yellow-bg">timm_hub</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">download_cached_file</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">url</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">check_hash</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">progress</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    134</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> is_dist_avail_and_initialized():
-<span class="ansi-green-intense-fg ansi-bold">    135</span>     dist<span style="color: rgb(98,98,98)">.</span>barrier()
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/timm/models/hub.py:51</span>, in <span class="ansi-cyan-fg">download_cached_file</span><span class="ansi-blue-fg">(url, check_hash, progress)</span>
-<span class="ansi-green-intense-fg ansi-bold">     49</span>         r <span style="color: rgb(98,98,98)">=</span> HASH_REGEX<span style="color: rgb(98,98,98)">.</span>search(filename)  <span style="color: rgb(95,135,135)"># r is Optional[Match[str]]</span>
-<span class="ansi-green-intense-fg ansi-bold">     50</span>         hash_prefix <span style="color: rgb(98,98,98)">=</span> r<span style="color: rgb(98,98,98)">.</span>group(<span style="color: rgb(98,98,98)">1</span>) <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> r <span class="ansi-bold" style="color: rgb(0,135,0)">else</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
-<span class="ansi-green-fg">---&gt; 51</span>     <span class="ansi-yellow-bg">download_url_to_file</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">url</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">cached_file</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">hash_prefix</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">progress</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">progress</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">     52</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> cached_file
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/hub.py:636</span>, in <span class="ansi-cyan-fg">download_url_to_file</span><span class="ansi-blue-fg">(url, dst, hash_prefix, progress)</span>
-<span class="ansi-green-intense-fg ansi-bold">    634</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span style="color: rgb(0,135,0)">len</span>(buffer) <span style="color: rgb(98,98,98)">==</span> <span style="color: rgb(98,98,98)">0</span>:
-<span class="ansi-green-intense-fg ansi-bold">    635</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">break</span>
-<span class="ansi-green-fg">--&gt; 636</span> <span class="ansi-yellow-bg">f</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">write</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">buffer</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    637</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> hash_prefix <span class="ansi-bold" style="color: rgb(175,0,255)">is</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>:
-<span class="ansi-green-intense-fg ansi-bold">    638</span>     sha256<span style="color: rgb(98,98,98)">.</span>update(buffer)
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/tempfile.py:478</span>, in <span class="ansi-cyan-fg">_TemporaryFileWrapper.__getattr__.&lt;locals&gt;.func_wrapper</span><span class="ansi-blue-fg">(*args, **kwargs)</span>
-<span class="ansi-green-intense-fg ansi-bold">    476</span> <span style="color: rgb(175,0,255)">@_functools</span><span style="color: rgb(98,98,98)">.</span>wraps(func)
-<span class="ansi-green-intense-fg ansi-bold">    477</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">func_wrapper</span>(<span style="color: rgb(98,98,98)">*</span>args, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs):
-<span class="ansi-green-fg">--&gt; 478</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">func</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
-
-<span class="ansi-red-fg">OSError</span>: [Errno 28] No space left on device
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[24]:
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">image_dict</span><span class="p">:</span>
@@ -20503,189 +648,18 @@ File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/pyth
 </pre></div>
 </div>
 </div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
-<span class="ansi-red-fg">NameError</span>                                 Traceback (most recent call last)
-Cell <span class="ansi-green-fg">In[24], line 2</span>
-<span class="ansi-green-intense-fg ansi-bold">      1</span> <span class="ansi-bold" style="color: rgb(0,135,0)">for</span> key <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> image_dict:
-<span class="ansi-green-fg">----&gt; 2</span>     image_dict[key] <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">obj</span><span style="color: rgb(98,98,98)">.</span>analyse_image(subdict <span style="color: rgb(98,98,98)">=</span> image_dict[key], analysis_type<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">summary_and_questions</span><span style="color: rgb(175,0,0)">&#34;</span>)
-<span class="ansi-green-intense-fg ansi-bold">      4</span> <span style="color: rgb(95,135,135)"># analysis_type can be </span>
-<span class="ansi-green-intense-fg ansi-bold">      5</span> <span style="color: rgb(95,135,135)"># &#34;summary&#34;,</span>
-<span class="ansi-green-intense-fg ansi-bold">      6</span> <span style="color: rgb(95,135,135)"># &#34;questions&#34;,</span>
-<span class="ansi-green-intense-fg ansi-bold">      7</span> <span style="color: rgb(95,135,135)"># &#34;summary_and_questions&#34;.</span>
-
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-</pre></div></div>
-</div>
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+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">image_dict</span>
 </pre></div>
 </div>
 </div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[25]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
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-{&#39;img4&#39;: {&#39;filename&#39;: &#39;data-test/img4.png&#39;,
-  &#39;face&#39;: &#39;No&#39;,
-  &#39;multiple_faces&#39;: &#39;No&#39;,
-  &#39;no_faces&#39;: 0,
-  &#39;wears_mask&#39;: [&#39;No&#39;],
-  &#39;age&#39;: [None],
-  &#39;gender&#39;: [None],
-  &#39;race&#39;: [None],
-  &#39;emotion&#39;: [None],
-  &#39;emotion (category)&#39;: [None],
-  &#39;text&#39;: &#39;MOODOVIN XI&#39;,
-  &#39;text_language&#39;: &#39;en&#39;,
-  &#39;text_english&#39;: &#39;MOODOVIN XI&#39;,
-  &#39;text_clean&#39;: &#39;XI&#39;,
-  &#39;text_summary&#39;: &#39; MOODOVIN XI XI: Vladimir Putin, Vladimir Vladmir Zelizer, Vladimir&#39;,
-  &#39;sentiment&#39;: &#39;POSITIVE&#39;,
-  &#39;sentiment_score&#39;: 0.66,
-  &#39;entity&#39;: [&#39;MOODOVIN XI&#39;],
-  &#39;entity_type&#39;: [&#39;ORG&#39;],
-  &#39;const_image_summary&#39;: &#39;a river running through a city next to tall buildings&#39;,
-  &#39;3_non-deterministic_summary&#39;: [&#39;there is a pretty house that sits above the water&#39;,
-   &#39;there is a building with a balcony and lots of plants on the side of it&#39;,
-   &#39;several buildings with a river flowing through it&#39;]},
- &#39;img1&#39;: {&#39;filename&#39;: &#39;data-test/img1.png&#39;,
-  &#39;no_faces&#39;: 0,
-  &#39;age&#39;: [None],
-  &#39;wears_mask&#39;: [&#39;No&#39;],
-  &#39;emotion (category)&#39;: [None],
-  &#39;multiple_faces&#39;: &#39;No&#39;,
-  &#39;emotion&#39;: [None],
-  &#39;gender&#39;: [None],
-  &#39;race&#39;: [None],
-  &#39;face&#39;: &#39;No&#39;,
-  &#39;text&#39;: &#39;SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions JOHN R. TAYLOR University of Colorado&#39;,
-  &#39;text_language&#39;: &#39;en&#39;,
-  &#39;text_english&#39;: &#39;SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions JOHN R. TAYLOR University of Colorado&#39;,
-  &#39;text_clean&#39;: &#39;THEORY The Quantum Theory of Collisions JOHN R. TAYLOR University of Colorado&#39;,
-  &#39;text_summary&#39;: &#39; SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions&#39;,
-  &#39;sentiment&#39;: &#39;POSITIVE&#39;,
-  &#39;sentiment_score&#39;: 0.91,
-  &#39;entity&#39;: [&#39;Non&#39;,
-   &#39;##vist&#39;,
-   &#39;Col&#39;,
-   &#39;##N&#39;,
-   &#39;R&#39;,
-   &#39;T&#39;,
-   &#39;##AYL&#39;,
-   &#39;University of Colorado&#39;],
-  &#39;entity_type&#39;: [&#39;MISC&#39;, &#39;MISC&#39;, &#39;MISC&#39;, &#39;ORG&#39;, &#39;PER&#39;, &#39;PER&#39;, &#39;ORG&#39;, &#39;ORG&#39;],
-  &#39;const_image_summary&#39;: &#39;a close up of a piece of paper with writing on it&#39;,
-  &#39;3_non-deterministic_summary&#39;: [&#39;a book opened to the book title for a novel&#39;,
-   &#39;there are many text on this page&#39;,
-   &#39;the text in a book is a handwritten poem&#39;]},
- &#39;img2&#39;: {&#39;filename&#39;: &#39;data-test/img2.png&#39;,
-  &#39;no_faces&#39;: 0,
-  &#39;age&#39;: [None],
-  &#39;wears_mask&#39;: [&#39;No&#39;],
-  &#39;emotion (category)&#39;: [None],
-  &#39;multiple_faces&#39;: &#39;No&#39;,
-  &#39;emotion&#39;: [None],
-  &#39;gender&#39;: [None],
-  &#39;race&#39;: [None],
-  &#39;face&#39;: &#39;No&#39;,
-  &#39;text&#39;: &#39;THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J.. H. WILKINSON&#39;,
-  &#39;text_language&#39;: &#39;en&#39;,
-  &#39;text_english&#39;: &#39;THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J.. H. WILKINSON&#39;,
-  &#39;text_clean&#39;: &#39;THE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J .. H. WILKINSON&#39;,
-  &#39;text_summary&#39;: &#39; H. H. W. WILKINSON: The Algebri&#39;,
-  &#39;sentiment&#39;: &#39;NEGATIVE&#39;,
-  &#39;sentiment_score&#39;: 0.97,
-  &#39;entity&#39;: [&#39;ALGEBRAIC EIGENVAL&#39;, &#39;NVS TIO MI&#39;, &#39;J&#39;, &#39;H&#39;, &#39;WILKINSON&#39;],
-  &#39;entity_type&#39;: [&#39;MISC&#39;, &#39;ORG&#39;, &#39;ORG&#39;, &#39;ORG&#39;, &#39;ORG&#39;],
-  &#39;const_image_summary&#39;: &#39;a yellow book with green lettering on it&#39;,
-  &#39;3_non-deterministic_summary&#39;: [&#39;a book cover with green writing on a black background&#39;,
-   &#39;the title page of a book with information from its authors&#39;,
-   &#39;a book about the age - related engineering and engineering&#39;]},
- &#39;img3&#39;: {&#39;filename&#39;: &#39;data-test/img3.png&#39;,
-  &#39;no_faces&#39;: 0,
-  &#39;age&#39;: [None],
-  &#39;wears_mask&#39;: [&#39;No&#39;],
-  &#39;emotion (category)&#39;: [None],
-  &#39;multiple_faces&#39;: &#39;No&#39;,
-  &#39;emotion&#39;: [None],
-  &#39;gender&#39;: [None],
-  &#39;race&#39;: [None],
-  &#39;face&#39;: &#39;No&#39;,
-  &#39;text&#39;: &#39;m OOOO 0000 www.&#39;,
-  &#39;text_language&#39;: &#39;en&#39;,
-  &#39;text_english&#39;: &#39;m OOOO 0000 www.&#39;,
-  &#39;text_clean&#39;: &#39;m www .&#39;,
-  &#39;text_summary&#39;: &#39; www. m OOOO 0000 0000 www.m.m OOOo 0000&#39;,
-  &#39;sentiment&#39;: &#39;NEGATIVE&#39;,
-  &#39;sentiment_score&#39;: 0.62,
-  &#39;entity&#39;: [],
-  &#39;entity_type&#39;: [],
-  &#39;const_image_summary&#39;: &#39;a bus that is sitting on the side of a road&#39;,
-  &#39;3_non-deterministic_summary&#39;: [&#39;there are cars and a bus on the side of the road&#39;,
-   &#39;a bus that is sitting in the middle of a street&#39;,
-   &#39;an aerial view of an empty city street with two large buses passing by&#39;]},
- &#39;img0&#39;: {&#39;filename&#39;: &#39;data-test/img0.png&#39;,
-  &#39;no_faces&#39;: 0,
-  &#39;age&#39;: [None],
-  &#39;wears_mask&#39;: [&#39;No&#39;],
-  &#39;emotion (category)&#39;: [None],
-  &#39;multiple_faces&#39;: &#39;No&#39;,
-  &#39;emotion&#39;: [None],
-  &#39;gender&#39;: [None],
-  &#39;race&#39;: [None],
-  &#39;face&#39;: &#39;No&#39;,
-  &#39;text&#39;: &#39;Mathematische Formelsammlung für Ingenieure und Naturwissenschaftler Mit zahlreichen Abbildungen und Rechenbeispielen und einer ausführlichen Integraltafel 3., verbesserte Auflage&#39;,
-  &#39;text_language&#39;: &#39;de&#39;,
-  &#39;text_english&#39;: &#39;Mathematical formula collection for engineers and scientists With numerous illustrations and calculation examples and a detailed integral table 3rd, improved edition&#39;,
-  &#39;text_clean&#39;: &#39;Mathematical formula collection for engineers and scientists With numerous illustrations and calculation examples and a detailed integral table 3rd , improved edition&#39;,
-  &#39;text_summary&#39;: &#39; Mathematical formula collection for engineers and scientists . Includes numerous illustrations and calculation examples . Includes&#39;,
-  &#39;sentiment&#39;: &#39;POSITIVE&#39;,
-  &#39;sentiment_score&#39;: 1.0,
-  &#39;entity&#39;: [],
-  &#39;entity_type&#39;: [],
-  &#39;const_image_summary&#39;: &#39;a close up of an open book with writing on it&#39;,
-  &#39;3_non-deterministic_summary&#39;: [&#39;a close up of a book with many languages&#39;,
-   &#39;a book that is opened up in german&#39;,
-   &#39;book about mathemarche formulals and their meaning&#39;]},
- &#39;img5&#39;: {&#39;filename&#39;: &#39;data-test/img5.png&#39;,
-  &#39;no_faces&#39;: 1,
-  &#39;age&#39;: [26],
-  &#39;wears_mask&#39;: [&#39;No&#39;],
-  &#39;emotion (category)&#39;: [&#39;Negative&#39;],
-  &#39;multiple_faces&#39;: &#39;No&#39;,
-  &#39;emotion&#39;: [&#39;sad&#39;],
-  &#39;gender&#39;: [&#39;Man&#39;],
-  &#39;race&#39;: [None],
-  &#39;face&#39;: &#39;Yes&#39;,
-  &#39;text&#39;: None,
-  &#39;text_language&#39;: &#39;en&#39;,
-  &#39;text_english&#39;: &#39;&#39;,
-  &#39;text_clean&#39;: &#39;&#39;,
-  &#39;text_summary&#39;: &#39; CNN.com will feature iReporter photos in a weekly Travel Snapshots gallery .&#39;,
-  &#39;sentiment&#39;: &#39;POSITIVE&#39;,
-  &#39;sentiment_score&#39;: 0.75,
-  &#39;entity&#39;: [],
-  &#39;entity_type&#39;: [],
-  &#39;const_image_summary&#39;: &#39;a person running on a beach near a rock formation&#39;,
-  &#39;3_non-deterministic_summary&#39;: [&#39;a woman is running down the beach next to some rocks&#39;,
-   &#39;a woman running along the beach by the ocean&#39;,
-   &#39;there is a person running on the beach next to the ocean&#39;]}}
-</pre></div></div>
-</div>
 <p>You can also pass a list of questions to this cell if <code class="docutils literal notranslate"><span class="pre">analysis_type=&quot;summary_and_questions&quot;</span></code> or <code class="docutils literal notranslate"><span class="pre">analysis_type=&quot;questions&quot;</span></code>. But the format of questions has changed in new models.</p>
 <p>Here is an example of a list of questions:</p>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[26]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">list_of_questions</span> <span class="o">=</span> <span class="p">[</span>
@@ -20695,8 +669,8 @@ Cell <span class="ansi-green-fg">In[24], line 2</span>
 </pre></div>
 </div>
 </div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[27]:
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">image_dict</span><span class="p">:</span>
@@ -20704,24 +678,10 @@ Cell <span class="ansi-green-fg">In[24], line 2</span>
 </pre></div>
 </div>
 </div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
-<span class="ansi-red-fg">NameError</span>                                 Traceback (most recent call last)
-Cell <span class="ansi-green-fg">In[27], line 2</span>
-<span class="ansi-green-intense-fg ansi-bold">      1</span> <span class="ansi-bold" style="color: rgb(0,135,0)">for</span> key <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> image_dict:
-<span class="ansi-green-fg">----&gt; 2</span>     image_dict[key] <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">obj</span><span style="color: rgb(98,98,98)">.</span>analyse_image(subdict <span style="color: rgb(98,98,98)">=</span> image_dict[key], analysis_type<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">questions</span><span style="color: rgb(175,0,0)">&#34;</span>, list_of_questions<span style="color: rgb(98,98,98)">=</span>list_of_questions)
-
-<span class="ansi-red-fg">NameError</span>: name &#39;obj&#39; is not defined
-</pre></div></div>
-</div>
 <p>You can also pass a question with previous answers as context into this model and pass in questions like this one to get a more accurate answer:</p>
 <p>You can combine as many questions as you want in a single query as a list.</p>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[28]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">list_of_questions</span> <span class="o">=</span> <span class="p">[</span>
@@ -20731,8 +691,8 @@ Cell <span class="ansi-green-fg">In[27], line 2</span>
 </pre></div>
 </div>
 </div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[29]:
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">image_dict</span><span class="p">:</span>
@@ -20740,184 +700,17 @@ Cell <span class="ansi-green-fg">In[27], line 2</span>
 </pre></div>
 </div>
 </div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
-<span class="ansi-red-fg">NameError</span>                                 Traceback (most recent call last)
-Cell <span class="ansi-green-fg">In[29], line 2</span>
-<span class="ansi-green-intense-fg ansi-bold">      1</span> <span class="ansi-bold" style="color: rgb(0,135,0)">for</span> key <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> image_dict:
-<span class="ansi-green-fg">----&gt; 2</span>     image_dict[key] <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">obj</span><span style="color: rgb(98,98,98)">.</span>analyse_image(subdict <span style="color: rgb(98,98,98)">=</span> image_dict[key], analysis_type<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">questions</span><span style="color: rgb(175,0,0)">&#34;</span>, list_of_questions<span style="color: rgb(98,98,98)">=</span>list_of_questions)
-
-<span class="ansi-red-fg">NameError</span>: name &#39;obj&#39; is not defined
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[30]:
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">image_dict</span>
 </pre></div>
 </div>
 </div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[30]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-{&#39;img4&#39;: {&#39;filename&#39;: &#39;data-test/img4.png&#39;,
-  &#39;face&#39;: &#39;No&#39;,
-  &#39;multiple_faces&#39;: &#39;No&#39;,
-  &#39;no_faces&#39;: 0,
-  &#39;wears_mask&#39;: [&#39;No&#39;],
-  &#39;age&#39;: [None],
-  &#39;gender&#39;: [None],
-  &#39;race&#39;: [None],
-  &#39;emotion&#39;: [None],
-  &#39;emotion (category)&#39;: [None],
-  &#39;text&#39;: &#39;MOODOVIN XI&#39;,
-  &#39;text_language&#39;: &#39;en&#39;,
-  &#39;text_english&#39;: &#39;MOODOVIN XI&#39;,
-  &#39;text_clean&#39;: &#39;XI&#39;,
-  &#39;text_summary&#39;: &#39; MOODOVIN XI XI: Vladimir Putin, Vladimir Vladmir Zelizer, Vladimir&#39;,
-  &#39;sentiment&#39;: &#39;POSITIVE&#39;,
-  &#39;sentiment_score&#39;: 0.66,
-  &#39;entity&#39;: [&#39;MOODOVIN XI&#39;],
-  &#39;entity_type&#39;: [&#39;ORG&#39;],
-  &#39;const_image_summary&#39;: &#39;a river running through a city next to tall buildings&#39;,
-  &#39;3_non-deterministic_summary&#39;: [&#39;there is a pretty house that sits above the water&#39;,
-   &#39;there is a building with a balcony and lots of plants on the side of it&#39;,
-   &#39;several buildings with a river flowing through it&#39;]},
- &#39;img1&#39;: {&#39;filename&#39;: &#39;data-test/img1.png&#39;,
-  &#39;no_faces&#39;: 0,
-  &#39;age&#39;: [None],
-  &#39;wears_mask&#39;: [&#39;No&#39;],
-  &#39;emotion (category)&#39;: [None],
-  &#39;multiple_faces&#39;: &#39;No&#39;,
-  &#39;emotion&#39;: [None],
-  &#39;gender&#39;: [None],
-  &#39;race&#39;: [None],
-  &#39;face&#39;: &#39;No&#39;,
-  &#39;text&#39;: &#39;SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions JOHN R. TAYLOR University of Colorado&#39;,
-  &#39;text_language&#39;: &#39;en&#39;,
-  &#39;text_english&#39;: &#39;SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions JOHN R. TAYLOR University of Colorado&#39;,
-  &#39;text_clean&#39;: &#39;THEORY The Quantum Theory of Collisions JOHN R. TAYLOR University of Colorado&#39;,
-  &#39;text_summary&#39;: &#39; SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions&#39;,
-  &#39;sentiment&#39;: &#39;POSITIVE&#39;,
-  &#39;sentiment_score&#39;: 0.91,
-  &#39;entity&#39;: [&#39;Non&#39;,
-   &#39;##vist&#39;,
-   &#39;Col&#39;,
-   &#39;##N&#39;,
-   &#39;R&#39;,
-   &#39;T&#39;,
-   &#39;##AYL&#39;,
-   &#39;University of Colorado&#39;],
-  &#39;entity_type&#39;: [&#39;MISC&#39;, &#39;MISC&#39;, &#39;MISC&#39;, &#39;ORG&#39;, &#39;PER&#39;, &#39;PER&#39;, &#39;ORG&#39;, &#39;ORG&#39;],
-  &#39;const_image_summary&#39;: &#39;a close up of a piece of paper with writing on it&#39;,
-  &#39;3_non-deterministic_summary&#39;: [&#39;a book opened to the book title for a novel&#39;,
-   &#39;there are many text on this page&#39;,
-   &#39;the text in a book is a handwritten poem&#39;]},
- &#39;img2&#39;: {&#39;filename&#39;: &#39;data-test/img2.png&#39;,
-  &#39;no_faces&#39;: 0,
-  &#39;age&#39;: [None],
-  &#39;wears_mask&#39;: [&#39;No&#39;],
-  &#39;emotion (category)&#39;: [None],
-  &#39;multiple_faces&#39;: &#39;No&#39;,
-  &#39;emotion&#39;: [None],
-  &#39;gender&#39;: [None],
-  &#39;race&#39;: [None],
-  &#39;face&#39;: &#39;No&#39;,
-  &#39;text&#39;: &#39;THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J.. H. WILKINSON&#39;,
-  &#39;text_language&#39;: &#39;en&#39;,
-  &#39;text_english&#39;: &#39;THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J.. H. WILKINSON&#39;,
-  &#39;text_clean&#39;: &#39;THE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J .. H. WILKINSON&#39;,
-  &#39;text_summary&#39;: &#39; H. H. W. WILKINSON: The Algebri&#39;,
-  &#39;sentiment&#39;: &#39;NEGATIVE&#39;,
-  &#39;sentiment_score&#39;: 0.97,
-  &#39;entity&#39;: [&#39;ALGEBRAIC EIGENVAL&#39;, &#39;NVS TIO MI&#39;, &#39;J&#39;, &#39;H&#39;, &#39;WILKINSON&#39;],
-  &#39;entity_type&#39;: [&#39;MISC&#39;, &#39;ORG&#39;, &#39;ORG&#39;, &#39;ORG&#39;, &#39;ORG&#39;],
-  &#39;const_image_summary&#39;: &#39;a yellow book with green lettering on it&#39;,
-  &#39;3_non-deterministic_summary&#39;: [&#39;a book cover with green writing on a black background&#39;,
-   &#39;the title page of a book with information from its authors&#39;,
-   &#39;a book about the age - related engineering and engineering&#39;]},
- &#39;img3&#39;: {&#39;filename&#39;: &#39;data-test/img3.png&#39;,
-  &#39;no_faces&#39;: 0,
-  &#39;age&#39;: [None],
-  &#39;wears_mask&#39;: [&#39;No&#39;],
-  &#39;emotion (category)&#39;: [None],
-  &#39;multiple_faces&#39;: &#39;No&#39;,
-  &#39;emotion&#39;: [None],
-  &#39;gender&#39;: [None],
-  &#39;race&#39;: [None],
-  &#39;face&#39;: &#39;No&#39;,
-  &#39;text&#39;: &#39;m OOOO 0000 www.&#39;,
-  &#39;text_language&#39;: &#39;en&#39;,
-  &#39;text_english&#39;: &#39;m OOOO 0000 www.&#39;,
-  &#39;text_clean&#39;: &#39;m www .&#39;,
-  &#39;text_summary&#39;: &#39; www. m OOOO 0000 0000 www.m.m OOOo 0000&#39;,
-  &#39;sentiment&#39;: &#39;NEGATIVE&#39;,
-  &#39;sentiment_score&#39;: 0.62,
-  &#39;entity&#39;: [],
-  &#39;entity_type&#39;: [],
-  &#39;const_image_summary&#39;: &#39;a bus that is sitting on the side of a road&#39;,
-  &#39;3_non-deterministic_summary&#39;: [&#39;there are cars and a bus on the side of the road&#39;,
-   &#39;a bus that is sitting in the middle of a street&#39;,
-   &#39;an aerial view of an empty city street with two large buses passing by&#39;]},
- &#39;img0&#39;: {&#39;filename&#39;: &#39;data-test/img0.png&#39;,
-  &#39;no_faces&#39;: 0,
-  &#39;age&#39;: [None],
-  &#39;wears_mask&#39;: [&#39;No&#39;],
-  &#39;emotion (category)&#39;: [None],
-  &#39;multiple_faces&#39;: &#39;No&#39;,
-  &#39;emotion&#39;: [None],
-  &#39;gender&#39;: [None],
-  &#39;race&#39;: [None],
-  &#39;face&#39;: &#39;No&#39;,
-  &#39;text&#39;: &#39;Mathematische Formelsammlung für Ingenieure und Naturwissenschaftler Mit zahlreichen Abbildungen und Rechenbeispielen und einer ausführlichen Integraltafel 3., verbesserte Auflage&#39;,
-  &#39;text_language&#39;: &#39;de&#39;,
-  &#39;text_english&#39;: &#39;Mathematical formula collection for engineers and scientists With numerous illustrations and calculation examples and a detailed integral table 3rd, improved edition&#39;,
-  &#39;text_clean&#39;: &#39;Mathematical formula collection for engineers and scientists With numerous illustrations and calculation examples and a detailed integral table 3rd , improved edition&#39;,
-  &#39;text_summary&#39;: &#39; Mathematical formula collection for engineers and scientists . Includes numerous illustrations and calculation examples . Includes&#39;,
-  &#39;sentiment&#39;: &#39;POSITIVE&#39;,
-  &#39;sentiment_score&#39;: 1.0,
-  &#39;entity&#39;: [],
-  &#39;entity_type&#39;: [],
-  &#39;const_image_summary&#39;: &#39;a close up of an open book with writing on it&#39;,
-  &#39;3_non-deterministic_summary&#39;: [&#39;a close up of a book with many languages&#39;,
-   &#39;a book that is opened up in german&#39;,
-   &#39;book about mathemarche formulals and their meaning&#39;]},
- &#39;img5&#39;: {&#39;filename&#39;: &#39;data-test/img5.png&#39;,
-  &#39;no_faces&#39;: 1,
-  &#39;age&#39;: [26],
-  &#39;wears_mask&#39;: [&#39;No&#39;],
-  &#39;emotion (category)&#39;: [&#39;Negative&#39;],
-  &#39;multiple_faces&#39;: &#39;No&#39;,
-  &#39;emotion&#39;: [&#39;sad&#39;],
-  &#39;gender&#39;: [&#39;Man&#39;],
-  &#39;race&#39;: [None],
-  &#39;face&#39;: &#39;Yes&#39;,
-  &#39;text&#39;: None,
-  &#39;text_language&#39;: &#39;en&#39;,
-  &#39;text_english&#39;: &#39;&#39;,
-  &#39;text_clean&#39;: &#39;&#39;,
-  &#39;text_summary&#39;: &#39; CNN.com will feature iReporter photos in a weekly Travel Snapshots gallery .&#39;,
-  &#39;sentiment&#39;: &#39;POSITIVE&#39;,
-  &#39;sentiment_score&#39;: 0.75,
-  &#39;entity&#39;: [],
-  &#39;entity_type&#39;: [],
-  &#39;const_image_summary&#39;: &#39;a person running on a beach near a rock formation&#39;,
-  &#39;3_non-deterministic_summary&#39;: [&#39;a woman is running down the beach next to some rocks&#39;,
-   &#39;a woman running along the beach by the ocean&#39;,
-   &#39;there is a person running on the beach next to the ocean&#39;]}}
-</pre></div></div>
-</div>
 <p>You can also ask sequential questions if you pass the argument <code class="docutils literal notranslate"><span class="pre">cosequential_questions=True</span></code>. This means that the answers to previous questions will be passed as context to the next question. However, this method will work a bit slower, because for each image the answers to the questions will not be calculated simultaneously, but sequentially.</p>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[31]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">list_of_questions</span> <span class="o">=</span> <span class="p">[</span>
@@ -20927,8 +720,8 @@ Cell <span class="ansi-green-fg">In[29], line 2</span>
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 </section>
 <section id="Detection-of-faces-and-facial-expression-analysis">
 <h2>Detection of faces and facial expression analysis<a class="headerlink" href="#Detection-of-faces-and-facial-expression-analysis" title="Link to this heading"></a></h2>
 <p>Faces and facial expressions are detected and analyzed using the <code class="docutils literal notranslate"><span class="pre">EmotionDetector</span></code> class from the <code class="docutils literal notranslate"><span class="pre">faces</span></code> module. Initially, it is detected if faces are present on the image using RetinaFace, followed by analysis if face masks are worn (Face-Mask-Detection). The detection of age, gender, race, and emotions is carried out with deepface.</p>
-<p><img alt="5b7a985b84a54ab2900643706d06813c" class="no-scaled-link" src="../_images/emotion_detector.png" style="width: 800px;" /></p>
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 <p>Depending on the features found on the image, the face detection module returns a different analysis content: If no faces are found on the image, all further steps are skipped and the result <code class="docutils literal notranslate"><span class="pre">&quot;face&quot;:</span> <span class="pre">&quot;No&quot;,</span> <span class="pre">&quot;multiple_faces&quot;:</span> <span class="pre">&quot;No&quot;,</span> <span class="pre">&quot;no_faces&quot;:</span> <span class="pre">0,</span> <span class="pre">&quot;wears_mask&quot;:</span> <span class="pre">[&quot;No&quot;],</span> <span class="pre">&quot;age&quot;:</span> <span class="pre">[None],</span> <span class="pre">&quot;gender&quot;:</span> <span class="pre">[None],</span> <span class="pre">&quot;race&quot;:</span> <span class="pre">[None],</span> <span class="pre">&quot;emotion&quot;:</span> <span class="pre">[None],</span> <span class="pre">&quot;emotion</span> <span class="pre">(category)&quot;:</span> <span class="pre">[None]</span></code> is returned. If one or several faces are found, up to three faces are analyzed if they are partially concealed by a face mask. If
 yes, only age and gender are detected; if no, also race, emotion, and dominant emotion are detected. In case of the latter, the output could look like this: <code class="docutils literal notranslate"><span class="pre">&quot;face&quot;:</span> <span class="pre">&quot;Yes&quot;,</span> <span class="pre">&quot;multiple_faces&quot;:</span> <span class="pre">&quot;Yes&quot;,</span> <span class="pre">&quot;no_faces&quot;:</span> <span class="pre">2,</span> <span class="pre">&quot;wears_mask&quot;:</span> <span class="pre">[&quot;No&quot;,</span> <span class="pre">&quot;No&quot;],</span> <span class="pre">&quot;age&quot;:</span> <span class="pre">[27,</span> <span class="pre">28],</span> <span class="pre">&quot;gender&quot;:</span> <span class="pre">[&quot;Man&quot;,</span> <span class="pre">&quot;Man&quot;],</span> <span class="pre">&quot;race&quot;:</span> <span class="pre">[&quot;asian&quot;,</span> <span class="pre">None],</span> <span class="pre">&quot;emotion&quot;:</span> <span class="pre">[&quot;angry&quot;,</span> <span class="pre">&quot;neutral&quot;],</span> <span class="pre">&quot;emotion</span> <span class="pre">(category)&quot;:</span> <span class="pre">[&quot;Negative&quot;,</span> <span class="pre">&quot;Neutral&quot;]</span></code>, where for the two faces that are detected (given by <code class="docutils literal notranslate"><span class="pre">no_faces</span></code>), some of the values are returned as a list
 with the first item for the first (largest) face and the second item for the second (smaller) face (for example, <code class="docutils literal notranslate"><span class="pre">&quot;emotion&quot;</span></code> returns a list <code class="docutils literal notranslate"><span class="pre">[&quot;angry&quot;,</span> <span class="pre">&quot;neutral&quot;]</span></code> signifying the first face expressing anger, and the second face having a neutral expression).</p>
@@ -21125,8 +751,8 @@ default is set to 50%, so that a confidence above 0.5 results in an emotion bein
 <p>From the seven facial expressions, an overall dominating emotion category is identified: negative, positive, or neutral emotion. These are defined with the facial expressions angry, disgust, fear and sad for the negative category, happy for the positive category, and surprise and neutral for the neutral category.</p>
 <p>A similar threshold as for the emotion recognition is set for the race detection, <code class="docutils literal notranslate"><span class="pre">race_threshold</span></code>, with the default set to 50% so that a confidence for the race above 0.5 only will return a value in the analysis.</p>
 <p>Summarizing, the face detection is carried out using the following method call and keywords, where <code class="docutils literal notranslate"><span class="pre">emotion_threshold</span></code> and <code class="docutils literal notranslate"><span class="pre">race_threshold</span></code> are optional:</p>
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@@ -21365,7 +816,7 @@ default is set to 50%, so that a confidence above 0.5 results in an emotion bein
 <h3>Indexing and extracting features from images in selected folder<a class="headerlink" href="#Indexing-and-extracting-features-from-images-in-selected-folder" title="Link to this heading"></a></h3>
 <p>First you need to select a model. You can choose one of the following models: - <a class="reference external" href="https://github.com/salesforce/BLIP">blip</a> - <a class="reference external" href="https://huggingface.co/docs/transformers/main/model_doc/blip-2">blip2</a> - <a class="reference external" href="https://github.com/salesforce/ALBEF">albef</a> - <a class="reference external" href="https://github.com/openai/CLIP/blob/main/model-card.md">clip_base</a> - <a class="reference external" href="https://github.com/mlfoundations/open_clip">clip_vitl14</a> - <a class="reference external" href="https://github.com/mlfoundations/open_clip">clip_vitl14_336</a></p>
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-<span class="ansi-green-fg">   (...)</span>
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-<span class="ansi-green-intense-fg ansi-bold">     54</span> <span style="color: rgb(175,0,0)">    &#34;&#34;&#34;</span>
-<span class="ansi-green-fg">---&gt; 55</span>     model, vis_processors, txt_processors <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">load_model_and_preprocess</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">     56</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">name</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">blip_feature_extractor</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">     57</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">model_type</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">base</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">     58</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">is_eval</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(0,135,0)">True</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">     59</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">device</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">device</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">     60</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">     61</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> model, vis_processors, txt_processors
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/__init__.py:195</span>, in <span class="ansi-cyan-fg">load_model_and_preprocess</span><span class="ansi-blue-fg">(name, model_type, is_eval, device)</span>
-<span class="ansi-green-intense-fg ansi-bold">    192</span> model_cls <span style="color: rgb(98,98,98)">=</span> registry<span style="color: rgb(98,98,98)">.</span>get_model_class(name)
-<span class="ansi-green-intense-fg ansi-bold">    194</span> <span style="color: rgb(95,135,135)"># load model</span>
-<span class="ansi-green-fg">--&gt; 195</span> model <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">model_cls</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">from_pretrained</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">model_type</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">model_type</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    197</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> is_eval:
-<span class="ansi-green-intense-fg ansi-bold">    198</span>     model<span style="color: rgb(98,98,98)">.</span>eval()
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/base_model.py:70</span>, in <span class="ansi-cyan-fg">BaseModel.from_pretrained</span><span class="ansi-blue-fg">(cls, model_type)</span>
-<span class="ansi-green-intense-fg ansi-bold">     60</span> <span style="color: rgb(175,0,0)">&#34;&#34;&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">     61</span> <span style="color: rgb(175,0,0)">Build a pretrained model from default configuration file, specified by model_type.</span>
-<span class="ansi-green-intense-fg ansi-bold">     62</span>
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">     67</span> <span style="color: rgb(175,0,0)">    - model (nn.Module): pretrained or finetuned model, depending on the configuration.</span>
-<span class="ansi-green-intense-fg ansi-bold">     68</span> <span style="color: rgb(175,0,0)">&#34;&#34;&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">     69</span> model_cfg <span style="color: rgb(98,98,98)">=</span> OmegaConf<span style="color: rgb(98,98,98)">.</span>load(<span style="color: rgb(0,135,0)">cls</span><span style="color: rgb(98,98,98)">.</span>default_config_path(model_type))<span style="color: rgb(98,98,98)">.</span>model
-<span class="ansi-green-fg">---&gt; 70</span> model <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">cls</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">from_config</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">model_cfg</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">     72</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> model
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/blip_models/blip_feature_extractor.py:208</span>, in <span class="ansi-cyan-fg">BlipFeatureExtractor.from_config</span><span class="ansi-blue-fg">(cls, cfg)</span>
-<span class="ansi-green-intense-fg ansi-bold">    206</span> pretrain_path <span style="color: rgb(98,98,98)">=</span> cfg<span style="color: rgb(98,98,98)">.</span>get(<span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">pretrained</span><span style="color: rgb(175,0,0)">&#34;</span>, <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>)
-<span class="ansi-green-intense-fg ansi-bold">    207</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> pretrain_path <span class="ansi-bold" style="color: rgb(175,0,255)">is</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>:
-<span class="ansi-green-fg">--&gt; 208</span>     msg <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">model</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">load_from_pretrained</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">url_or_filename</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">pretrain_path</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    209</span> <span class="ansi-bold" style="color: rgb(0,135,0)">else</span>:
-<span class="ansi-green-intense-fg ansi-bold">    210</span>     warnings<span style="color: rgb(98,98,98)">.</span>warn(<span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">No pretrained weights are loaded.</span><span style="color: rgb(175,0,0)">&#34;</span>)
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/blip_models/blip.py:30</span>, in <span class="ansi-cyan-fg">BlipBase.load_from_pretrained</span><span class="ansi-blue-fg">(self, url_or_filename)</span>
-<span class="ansi-green-intense-fg ansi-bold">     28</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">load_from_pretrained</span>(<span style="color: rgb(0,135,0)">self</span>, url_or_filename):
-<span class="ansi-green-intense-fg ansi-bold">     29</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> is_url(url_or_filename):
-<span class="ansi-green-fg">---&gt; 30</span>         cached_file <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">download_cached_file</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">     31</span> <span class="ansi-yellow-bg">            </span><span class="ansi-yellow-bg">url_or_filename</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">check_hash</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(0,135,0)">False</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">progress</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(0,135,0)">True</span>
-<span class="ansi-green-intense-fg ansi-bold">     32</span> <span class="ansi-yellow-bg">        </span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">     33</span>         checkpoint <span style="color: rgb(98,98,98)">=</span> torch<span style="color: rgb(98,98,98)">.</span>load(cached_file, map_location<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">cpu</span><span style="color: rgb(175,0,0)">&#34;</span>)
-<span class="ansi-green-intense-fg ansi-bold">     34</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">elif</span> os<span style="color: rgb(98,98,98)">.</span>path<span style="color: rgb(98,98,98)">.</span>isfile(url_or_filename):
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/common/dist_utils.py:132</span>, in <span class="ansi-cyan-fg">download_cached_file</span><span class="ansi-blue-fg">(url, check_hash, progress)</span>
-<span class="ansi-green-intense-fg ansi-bold">    129</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> cached_file
-<span class="ansi-green-intense-fg ansi-bold">    131</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> is_main_process():
-<span class="ansi-green-fg">--&gt; 132</span>     <span class="ansi-yellow-bg">timm_hub</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">download_cached_file</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">url</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">check_hash</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">progress</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    134</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> is_dist_avail_and_initialized():
-<span class="ansi-green-intense-fg ansi-bold">    135</span>     dist<span style="color: rgb(98,98,98)">.</span>barrier()
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/timm/models/hub.py:51</span>, in <span class="ansi-cyan-fg">download_cached_file</span><span class="ansi-blue-fg">(url, check_hash, progress)</span>
-<span class="ansi-green-intense-fg ansi-bold">     49</span>         r <span style="color: rgb(98,98,98)">=</span> HASH_REGEX<span style="color: rgb(98,98,98)">.</span>search(filename)  <span style="color: rgb(95,135,135)"># r is Optional[Match[str]]</span>
-<span class="ansi-green-intense-fg ansi-bold">     50</span>         hash_prefix <span style="color: rgb(98,98,98)">=</span> r<span style="color: rgb(98,98,98)">.</span>group(<span style="color: rgb(98,98,98)">1</span>) <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> r <span class="ansi-bold" style="color: rgb(0,135,0)">else</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>
-<span class="ansi-green-fg">---&gt; 51</span>     <span class="ansi-yellow-bg">download_url_to_file</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">url</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">cached_file</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">hash_prefix</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">progress</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">progress</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">     52</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> cached_file
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/hub.py:636</span>, in <span class="ansi-cyan-fg">download_url_to_file</span><span class="ansi-blue-fg">(url, dst, hash_prefix, progress)</span>
-<span class="ansi-green-intense-fg ansi-bold">    634</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span style="color: rgb(0,135,0)">len</span>(buffer) <span style="color: rgb(98,98,98)">==</span> <span style="color: rgb(98,98,98)">0</span>:
-<span class="ansi-green-intense-fg ansi-bold">    635</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">break</span>
-<span class="ansi-green-fg">--&gt; 636</span> <span class="ansi-yellow-bg">f</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">write</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">buffer</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    637</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> hash_prefix <span class="ansi-bold" style="color: rgb(175,0,255)">is</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>:
-<span class="ansi-green-intense-fg ansi-bold">    638</span>     sha256<span style="color: rgb(98,98,98)">.</span>update(buffer)
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/tempfile.py:478</span>, in <span class="ansi-cyan-fg">_TemporaryFileWrapper.__getattr__.&lt;locals&gt;.func_wrapper</span><span class="ansi-blue-fg">(*args, **kwargs)</span>
-<span class="ansi-green-intense-fg ansi-bold">    476</span> <span style="color: rgb(175,0,255)">@_functools</span><span style="color: rgb(98,98,98)">.</span>wraps(func)
-<span class="ansi-green-intense-fg ansi-bold">    477</span> <span class="ansi-bold" style="color: rgb(0,135,0)">def</span> <span style="color: rgb(0,0,255)">func_wrapper</span>(<span style="color: rgb(98,98,98)">*</span>args, <span style="color: rgb(98,98,98)">*</span><span style="color: rgb(98,98,98)">*</span>kwargs):
-<span class="ansi-green-fg">--&gt; 478</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">func</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
-
-<span class="ansi-red-fg">OSError</span>: [Errno 28] No space left on device
-</pre></div></div>
-</div>
 <p>The images are then processed and stored in a numerical representation, a tensor. These tensors do not change for the same image and same model - so if you run this analysis once, and save the tensors giving a path with the keyword <code class="docutils literal notranslate"><span class="pre">path_to_save_tensors</span></code>, a file with filename <code class="docutils literal notranslate"><span class="pre">.&lt;Number_of_images&gt;_&lt;model_name&gt;_saved_features_image.pt</span></code> will be placed there.</p>
 <p>This can save you time if you want to analyse same images with the same model but different questions. To run using the saved tensors, execute the below code giving the path and name of the tensor file.</p>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[38]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># (</span>
@@ -23350,7 +881,7 @@ File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/pyth
 <h3>Formulate your search queries<a class="headerlink" href="#Formulate-your-search-queries" title="Link to this heading"></a></h3>
 <p>Next, you need to form search queries. You can search either by image or by text. You can search for a single query, or you can search for several queries at once, the computational time should not be much different. The format of the queries is as follows:</p>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[39]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">importlib_resources</span>                                                                       <span class="c1"># only require for image query example</span>
@@ -23367,8 +898,8 @@ File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/pyth
 </div>
 <p>You can filter your results in 3 different ways: - <code class="docutils literal notranslate"><span class="pre">filter_number_of_images</span></code> limits the number of images found. That is, if the parameter <code class="docutils literal notranslate"><span class="pre">filter_number_of_images</span> <span class="pre">=</span> <span class="pre">10</span></code>, then the first 10 images that best match the query will be shown. The other images ranks will be set to <code class="docutils literal notranslate"><span class="pre">None</span></code> and the similarity value to <code class="docutils literal notranslate"><span class="pre">0</span></code>. - <code class="docutils literal notranslate"><span class="pre">filter_val_limit</span></code> limits the output of images with a similarity value not bigger than <code class="docutils literal notranslate"><span class="pre">filter_val_limit</span></code>. That is, if the parameter <code class="docutils literal notranslate"><span class="pre">filter_val_limit</span> <span class="pre">=</span> <span class="pre">0.2</span></code>, all images
 with similarity less than 0.2 will be discarded. - <code class="docutils literal notranslate"><span class="pre">filter_rel_error</span></code> (percentage) limits the output of images with a similarity value not bigger than <code class="docutils literal notranslate"><span class="pre">100</span> <span class="pre">*</span> <span class="pre">abs(current_simularity_value</span> <span class="pre">-</span> <span class="pre">best_simularity_value_in_current_search)/best_simularity_value_in_current_search</span> <span class="pre">&lt;</span> <span class="pre">filter_rel_error</span></code>. That is, if we set filter_rel_error = 30, it means that if the top1 image have 0.5 similarity value, we discard all image with similarity less than 0.35.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[40]:
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">similarity</span><span class="p">,</span> <span class="n">sorted_lists</span> <span class="o">=</span> <span class="n">my_obj</span><span class="o">.</span><span class="n">multimodal_search</span><span class="p">(</span>
@@ -23384,235 +915,34 @@ with similarity less than 0.2 will be discarded. - <code class="docutils literal
 </pre></div>
 </div>
 </div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
-<span class="ansi-red-fg">NameError</span>                                 Traceback (most recent call last)
-Cell <span class="ansi-green-fg">In[40], line 2</span>
-<span class="ansi-green-intense-fg ansi-bold">      1</span> similarity, sorted_lists <span style="color: rgb(98,98,98)">=</span> my_obj<span style="color: rgb(98,98,98)">.</span>multimodal_search(
-<span class="ansi-green-fg">----&gt; 2</span>     <span class="ansi-yellow-bg">model</span>,
-<span class="ansi-green-intense-fg ansi-bold">      3</span>     vis_processors,
-<span class="ansi-green-intense-fg ansi-bold">      4</span>     txt_processors,
-<span class="ansi-green-intense-fg ansi-bold">      5</span>     model_type,
-<span class="ansi-green-intense-fg ansi-bold">      6</span>     image_keys,
-<span class="ansi-green-intense-fg ansi-bold">      7</span>     features_image_stacked,
-<span class="ansi-green-intense-fg ansi-bold">      8</span>     search_query,
-<span class="ansi-green-intense-fg ansi-bold">      9</span>     filter_number_of_images<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(98,98,98)">20</span>,
-<span class="ansi-green-intense-fg ansi-bold">     10</span> )
-
-<span class="ansi-red-fg">NameError</span>: name &#39;model&#39; is not defined
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[41]:
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
 </div>
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-&#39;--------------------------------------------------&#39;
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-&#39;Results:&#39;
-</pre></div></div>
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-<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
-<span class="ansi-red-fg">KeyError</span>                                  Traceback (most recent call last)
-Cell <span class="ansi-green-fg">In[45], line 1</span>
-<span class="ansi-green-fg">----&gt; 1</span> <span class="ansi-yellow-bg">my_obj</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">show_results</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">      2</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">search_query</span><span class="ansi-yellow-bg">[</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">3</span><span class="ansi-yellow-bg">]</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(95,135,135)"># you can change the index to see the results for other queries</span>
-<span class="ansi-green-intense-fg ansi-bold">      3</span> <span class="ansi-yellow-bg">)</span>
-
-File <span class="ansi-green-fg">~/work/AMMICO/AMMICO/ammico/multimodal_search.py:970</span>, in <span class="ansi-cyan-fg">MultimodalSearch.show_results</span><span class="ansi-blue-fg">(self, query, itm, image_gradcam_with_itm)</span>
-<span class="ansi-green-intense-fg ansi-bold">    967</span>     current_querry_val <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">list</span>(query<span style="color: rgb(98,98,98)">.</span>values())[<span style="color: rgb(98,98,98)">0</span>]
-<span class="ansi-green-intense-fg ansi-bold">    968</span>     current_querry_rank <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">rank </span><span style="color: rgb(175,0,0)">&#34;</span> <span style="color: rgb(98,98,98)">+</span> <span style="color: rgb(0,135,0)">list</span>(query<span style="color: rgb(98,98,98)">.</span>values())[<span style="color: rgb(98,98,98)">0</span>]
-<span class="ansi-green-fg">--&gt; 970</span> <span class="ansi-bold" style="color: rgb(0,135,0)">for</span> s <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> <span class="ansi-yellow-bg" style="color: rgb(0,135,0)">sorted</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">    971</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">subdict</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">items</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">)</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">key</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(0,135,0)">lambda</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">t</span><span class="ansi-yellow-bg">:</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">t</span><span class="ansi-yellow-bg">[</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">1</span><span class="ansi-yellow-bg">]</span><span class="ansi-yellow-bg">[</span><span class="ansi-yellow-bg">current_querry_val</span><span class="ansi-yellow-bg">]</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg">reverse</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg ansi-bold" style="color: rgb(0,135,0)">True</span>
-<span class="ansi-green-intense-fg ansi-bold">    972</span> <span class="ansi-yellow-bg">)</span>:
-<span class="ansi-green-intense-fg ansi-bold">    973</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> s[<span style="color: rgb(98,98,98)">1</span>][current_querry_rank] <span class="ansi-bold" style="color: rgb(175,0,255)">is</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>:
-<span class="ansi-green-intense-fg ansi-bold">    974</span>         <span class="ansi-bold" style="color: rgb(0,135,0)">break</span>
-
-File <span class="ansi-green-fg">~/work/AMMICO/AMMICO/ammico/multimodal_search.py:971</span>, in <span class="ansi-cyan-fg">MultimodalSearch.show_results.&lt;locals&gt;.&lt;lambda&gt;</span><span class="ansi-blue-fg">(t)</span>
-<span class="ansi-green-intense-fg ansi-bold">    967</span>     current_querry_val <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">list</span>(query<span style="color: rgb(98,98,98)">.</span>values())[<span style="color: rgb(98,98,98)">0</span>]
-<span class="ansi-green-intense-fg ansi-bold">    968</span>     current_querry_rank <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">rank </span><span style="color: rgb(175,0,0)">&#34;</span> <span style="color: rgb(98,98,98)">+</span> <span style="color: rgb(0,135,0)">list</span>(query<span style="color: rgb(98,98,98)">.</span>values())[<span style="color: rgb(98,98,98)">0</span>]
-<span class="ansi-green-intense-fg ansi-bold">    970</span> <span class="ansi-bold" style="color: rgb(0,135,0)">for</span> s <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> <span style="color: rgb(0,135,0)">sorted</span>(
-<span class="ansi-green-fg">--&gt; 971</span>     <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>subdict<span style="color: rgb(98,98,98)">.</span>items(), key<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">lambda</span> t: <span class="ansi-yellow-bg">t</span><span class="ansi-yellow-bg">[</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">1</span><span class="ansi-yellow-bg">]</span><span class="ansi-yellow-bg">[</span><span class="ansi-yellow-bg">current_querry_val</span><span class="ansi-yellow-bg">]</span>, reverse<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">True</span>
-<span class="ansi-green-intense-fg ansi-bold">    972</span> ):
-<span class="ansi-green-intense-fg ansi-bold">    973</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> s[<span style="color: rgb(98,98,98)">1</span>][current_querry_rank] <span class="ansi-bold" style="color: rgb(175,0,255)">is</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>:
-<span class="ansi-green-intense-fg ansi-bold">    974</span>         <span class="ansi-bold" style="color: rgb(0,135,0)">break</span>
-
-<span class="ansi-red-fg">KeyError</span>: &#39;/home/runner/work/AMMICO/AMMICO/ammico/data/test-crop-image.png&#39;
-</pre></div></div>
-</div>
 </section>
 <section id="Improve-the-search-results">
 <h3>Improve the search results<a class="headerlink" href="#Improve-the-search-results" title="Link to this heading"></a></h3>
 <p>For even better results, a slightly different approach has been prepared that can improve search results. It is quite resource-intensive, so it is applied after the main algorithm has found the most relevant images. This approach works only with text queries and it skips image queries. Among the parameters you can choose 3 models: <code class="docutils literal notranslate"><span class="pre">&quot;blip_base&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;blip_large&quot;</span></code>, <code class="docutils literal notranslate"><span class="pre">&quot;blip2_coco&quot;</span></code>. If you get an <code class="docutils literal notranslate"><span class="pre">Out</span> <span class="pre">of</span> <span class="pre">Memory</span></code> error, try reducing the batch_size value (minimum = 1), which is the number of
 images being processed simultaneously. With the parameter <code class="docutils literal notranslate"><span class="pre">need_grad_cam</span> <span class="pre">=</span> <span class="pre">True/False</span></code> you can enable the calculation of the heat map of each image to be processed and save them in <code class="docutils literal notranslate"><span class="pre">image_gradcam_with_itm</span></code>. Thus the <code class="docutils literal notranslate"><span class="pre">image_text_match_reordering()</span></code> function calculates new similarity values and new ranks for each image. The resulting values are added to the general dictionary.</p>
 <div class="nbinput nblast docutils container">
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 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">itm_model</span> <span class="o">=</span> <span class="s2">&quot;blip_base&quot;</span>
@@ -23767,8 +976,8 @@ images being processed simultaneously. With the parameter <code class="docutils
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 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">itm_scores</span><span class="p">,</span> <span class="n">image_gradcam_with_itm</span> <span class="o">=</span> <span class="n">my_obj</span><span class="o">.</span><span class="n">image_text_match_reordering</span><span class="p">(</span>
@@ -23782,29 +991,9 @@ images being processed simultaneously. With the parameter <code class="docutils
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-<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
-<span class="ansi-red-fg">NameError</span>                                 Traceback (most recent call last)
-Cell <span class="ansi-green-fg">In[47], line 4</span>
-<span class="ansi-green-intense-fg ansi-bold">      1</span> itm_scores, image_gradcam_with_itm <span style="color: rgb(98,98,98)">=</span> my_obj<span style="color: rgb(98,98,98)">.</span>image_text_match_reordering(
-<span class="ansi-green-intense-fg ansi-bold">      2</span>     search_query,
-<span class="ansi-green-intense-fg ansi-bold">      3</span>     itm_model,
-<span class="ansi-green-fg">----&gt; 4</span>     <span class="ansi-yellow-bg">image_keys</span>,
-<span class="ansi-green-intense-fg ansi-bold">      5</span>     sorted_lists,
-<span class="ansi-green-intense-fg ansi-bold">      6</span>     batch_size<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(98,98,98)">1</span>,
-<span class="ansi-green-intense-fg ansi-bold">      7</span>     need_grad_cam<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">True</span>,
-<span class="ansi-green-intense-fg ansi-bold">      8</span> )
-
-<span class="ansi-red-fg">NameError</span>: name &#39;image_keys&#39; is not defined
-</pre></div></div>
-</div>
 <p>Then using the same output function you can add the <code class="docutils literal notranslate"><span class="pre">itm=True</span></code> argument to output the new image order. Remember that for images querys, an error will be thrown with <code class="docutils literal notranslate"><span class="pre">itm=True</span></code> argument. You can also add the <code class="docutils literal notranslate"><span class="pre">image_gradcam_with_itm</span></code> along with <code class="docutils literal notranslate"><span class="pre">itm=True</span></code> argument to output the heat maps of the calculated images.</p>
-<div class="nbinput docutils container">
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 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">my_obj</span><span class="o">.</span><span class="n">show_results</span><span class="p">(</span>
@@ -23813,27 +1002,12 @@ Cell <span class="ansi-green-fg">In[47], line 4</span>
 </pre></div>
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-<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
-<span class="ansi-red-fg">NameError</span>                                 Traceback (most recent call last)
-Cell <span class="ansi-green-fg">In[48], line 2</span>
-<span class="ansi-green-intense-fg ansi-bold">      1</span> my_obj<span style="color: rgb(98,98,98)">.</span>show_results(
-<span class="ansi-green-fg">----&gt; 2</span>     search_query[<span style="color: rgb(98,98,98)">0</span>], itm<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">True</span>, image_gradcam_with_itm<span style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">image_gradcam_with_itm</span>
-<span class="ansi-green-intense-fg ansi-bold">      3</span> )
-
-<span class="ansi-red-fg">NameError</span>: name &#39;image_gradcam_with_itm&#39; is not defined
-</pre></div></div>
-</div>
 </section>
 <section id="Save-search-results-to-csv">
 <h3>Save search results to csv<a class="headerlink" href="#Save-search-results-to-csv" title="Link to this heading"></a></h3>
 <p>Convert the dictionary of dictionarys into a dictionary with lists:</p>
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 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">outdict</span> <span class="o">=</span> <span class="n">ammico</span><span class="o">.</span><span class="n">append_data_to_dict</span><span class="p">(</span><span class="n">image_dict</span><span class="p">)</span>
@@ -23841,64 +1015,24 @@ Cell <span class="ansi-green-fg">In[48], line 2</span>
 </pre></div>
 </div>
 </div>
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-<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
-<span class="ansi-red-fg">AttributeError</span>                            Traceback (most recent call last)
-Cell <span class="ansi-green-fg">In[49], line 1</span>
-<span class="ansi-green-fg">----&gt; 1</span> outdict <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">ammico</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">append_data_to_dict</span>(image_dict)
-<span class="ansi-green-intense-fg ansi-bold">      2</span> df <span style="color: rgb(98,98,98)">=</span> ammico<span style="color: rgb(98,98,98)">.</span>dump_df(outdict)
-
-<span class="ansi-red-fg">AttributeError</span>: module &#39;ammico&#39; has no attribute &#39;append_data_to_dict&#39;
-</pre></div></div>
-</div>
 <p>Check the dataframe:</p>
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 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
 </pre></div>
 </div>
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-<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
-<span class="ansi-red-fg">NameError</span>                                 Traceback (most recent call last)
-Cell <span class="ansi-green-fg">In[50], line 1</span>
-<span class="ansi-green-fg">----&gt; 1</span> <span class="ansi-yellow-bg">df</span><span style="color: rgb(98,98,98)">.</span>head(<span style="color: rgb(98,98,98)">10</span>)
-
-<span class="ansi-red-fg">NameError</span>: name &#39;df&#39; is not defined
-</pre></div></div>
-</div>
 <p>Write the csv file:</p>
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 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s2">&quot;/content/drive/MyDrive/misinformation-data/data_out.csv&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
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-<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
-<span class="ansi-red-fg">NameError</span>                                 Traceback (most recent call last)
-Cell <span class="ansi-green-fg">In[51], line 1</span>
-<span class="ansi-green-fg">----&gt; 1</span> <span class="ansi-yellow-bg">df</span><span style="color: rgb(98,98,98)">.</span>to_csv(<span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">/content/drive/MyDrive/misinformation-data/data_out.csv</span><span style="color: rgb(175,0,0)">&#34;</span>)
-
-<span class="ansi-red-fg">NameError</span>: name &#39;df&#39; is not defined
-</pre></div></div>
-</div>
 </section>
 </section>
 <section id="Color-analysis-of-pictures">
@@ -23906,8 +1040,8 @@ Cell <span class="ansi-green-fg">In[51], line 1</span>
 <p>This module shows primary color analysis of color image using K-Means algorithm. The output are N primary colors and their corresponding percentage.</p>
 <p>To check the analysis, you can inspect the analyzed elements here. Loading the results takes a moment, so please be patient. If you are sure of what you are doing, you can skip this and directly export a csv file in the step below. Here, we display the color detection results provided by <code class="docutils literal notranslate"><span class="pre">colorgram</span></code> and <code class="docutils literal notranslate"><span class="pre">colour</span></code> libraries. Click on the tabs to see the results in the right sidebar. You may need to increment the <code class="docutils literal notranslate"><span class="pre">port</span></code> number if you are already running several notebook instances on the same
 server.</p>
-<div class="nbinput docutils container">
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 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">analysis_explorer</span> <span class="o">=</span> <span class="n">ammico</span><span class="o">.</span><span class="n">AnalysisExplorer</span><span class="p">(</span><span class="n">image_dict</span><span class="p">)</span>
@@ -23915,22 +1049,9 @@ server.</p>
 </pre></div>
 </div>
 </div>
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-></iframe></div>
-</div>
 <p>Instead of inspecting each of the images, you can also directly carry out the analysis and export the result into a csv. This may take a while depending on how many images you have loaded.</p>
 <div class="nbinput nblast docutils container">
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 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">image_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
@@ -23940,7 +1061,7 @@ server.</p>
 </div>
 <p>These steps are required to convert the dictionary of dictionarys into a dictionary with lists, that can be converted into a pandas dataframe and exported to a csv file.</p>
 <div class="nbinput nblast docutils container">
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 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">ammico</span><span class="o">.</span><span class="n">get_dataframe</span><span class="p">(</span><span class="n">image_dict</span><span class="p">)</span>
@@ -23948,315 +1069,23 @@ server.</p>
 </div>
 </div>
 <p>Check the dataframe:</p>
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 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[55]:
-</pre></div>
-</div>
-<div class="output_area rendered_html docutils container">
-<div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>filename</th>
-      <th>face</th>
-      <th>multiple_faces</th>
-      <th>no_faces</th>
-      <th>wears_mask</th>
-      <th>age</th>
-      <th>gender</th>
-      <th>race</th>
-      <th>emotion</th>
-      <th>emotion (category)</th>
-      <th>...</th>
-      <th>blue</th>
-      <th>yellow</th>
-      <th>cyan</th>
-      <th>orange</th>
-      <th>purple</th>
-      <th>pink</th>
-      <th>brown</th>
-      <th>grey</th>
-      <th>white</th>
-      <th>black</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>data-test/img4.png</td>
-      <td>No</td>
-      <td>No</td>
-      <td>0</td>
-      <td>[No]</td>
-      <td>[None]</td>
-      <td>[None]</td>
-      <td>[None]</td>
-      <td>[None]</td>
-      <td>[None]</td>
-      <td>...</td>
-      <td>0.16</td>
-      <td>0.00</td>
-      <td>0</td>
-      <td>0</td>
-      <td>0.00</td>
-      <td>0</td>
-      <td>0.10</td>
-      <td>0.42</td>
-      <td>0.05</td>
-      <td>0.21</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>data-test/img1.png</td>
-      <td>No</td>
-      <td>No</td>
-      <td>0</td>
-      <td>[No]</td>
-      <td>[None]</td>
-      <td>[None]</td>
-      <td>[None]</td>
-      <td>[None]</td>
-      <td>[None]</td>
-      <td>...</td>
-      <td>0.00</td>
-      <td>0.00</td>
-      <td>0</td>
-      <td>0</td>
-      <td>0.00</td>
-      <td>0</td>
-      <td>0.00</td>
-      <td>0.96</td>
-      <td>0.00</td>
-      <td>0.04</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>data-test/img2.png</td>
-      <td>No</td>
-      <td>No</td>
-      <td>0</td>
-      <td>[No]</td>
-      <td>[None]</td>
-      <td>[None]</td>
-      <td>[None]</td>
-      <td>[None]</td>
-      <td>[None]</td>
-      <td>...</td>
-      <td>0.00</td>
-      <td>0.75</td>
-      <td>0</td>
-      <td>0</td>
-      <td>0.00</td>
-      <td>0</td>
-      <td>0.04</td>
-      <td>0.15</td>
-      <td>0.00</td>
-      <td>0.02</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>data-test/img3.png</td>
-      <td>No</td>
-      <td>No</td>
-      <td>0</td>
-      <td>[No]</td>
-      <td>[None]</td>
-      <td>[None]</td>
-      <td>[None]</td>
-      <td>[None]</td>
-      <td>[None]</td>
-      <td>...</td>
-      <td>0.00</td>
-      <td>0.00</td>
-      <td>0</td>
-      <td>0</td>
-      <td>0.02</td>
-      <td>0</td>
-      <td>0.06</td>
-      <td>0.92</td>
-      <td>0.01</td>
-      <td>0.00</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>data-test/img0.png</td>
-      <td>No</td>
-      <td>No</td>
-      <td>0</td>
-      <td>[No]</td>
-      <td>[None]</td>
-      <td>[None]</td>
-      <td>[None]</td>
-      <td>[None]</td>
-      <td>[None]</td>
-      <td>...</td>
-      <td>0.00</td>
-      <td>0.00</td>
-      <td>0</td>
-      <td>0</td>
-      <td>0.00</td>
-      <td>0</td>
-      <td>0.00</td>
-      <td>0.98</td>
-      <td>0.00</td>
-      <td>0.02</td>
-    </tr>
-    <tr>
-      <th>5</th>
-      <td>data-test/img5.png</td>
-      <td>Yes</td>
-      <td>No</td>
-      <td>1</td>
-      <td>[No]</td>
-      <td>[26]</td>
-      <td>[Man]</td>
-      <td>[None]</td>
-      <td>[sad]</td>
-      <td>[Negative]</td>
-      <td>...</td>
-      <td>0.12</td>
-      <td>0.00</td>
-      <td>0</td>
-      <td>0</td>
-      <td>0.00</td>
-      <td>0</td>
-      <td>0.02</td>
-      <td>0.50</td>
-      <td>0.00</td>
-      <td>0.00</td>
-    </tr>
-  </tbody>
-</table>
-<p>6 rows × 33 columns</p>
-</div></div>
-</div>
 <p>Write the csv file - here you should provide a file path and file name for the csv file to be written.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[56]:
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s2">&quot;/content/drive/MyDrive/misinformation-data/data_out.csv&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
-<span class="ansi-red-fg">OSError</span>                                   Traceback (most recent call last)
-Cell <span class="ansi-green-fg">In[56], line 1</span>
-<span class="ansi-green-fg">----&gt; 1</span> <span class="ansi-yellow-bg">df</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">to_csv</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">/content/drive/MyDrive/misinformation-data/data_out.csv</span><span class="ansi-yellow-bg" style="color: rgb(175,0,0)">&#34;</span><span class="ansi-yellow-bg">)</span>
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/util/_decorators.py:333</span>, in <span class="ansi-cyan-fg">deprecate_nonkeyword_arguments.&lt;locals&gt;.decorate.&lt;locals&gt;.wrapper</span><span class="ansi-blue-fg">(*args, **kwargs)</span>
-<span class="ansi-green-intense-fg ansi-bold">    327</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span style="color: rgb(0,135,0)">len</span>(args) <span style="color: rgb(98,98,98)">&gt;</span> num_allow_args:
-<span class="ansi-green-intense-fg ansi-bold">    328</span>     warnings<span style="color: rgb(98,98,98)">.</span>warn(
-<span class="ansi-green-intense-fg ansi-bold">    329</span>         msg<span style="color: rgb(98,98,98)">.</span>format(arguments<span style="color: rgb(98,98,98)">=</span>_format_argument_list(allow_args)),
-<span class="ansi-green-intense-fg ansi-bold">    330</span>         <span class="ansi-bold" style="color: rgb(215,95,95)">FutureWarning</span>,
-<span class="ansi-green-intense-fg ansi-bold">    331</span>         stacklevel<span style="color: rgb(98,98,98)">=</span>find_stack_level(),
-<span class="ansi-green-intense-fg ansi-bold">    332</span>     )
-<span class="ansi-green-fg">--&gt; 333</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">func</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">args</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg"> </span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">*</span><span class="ansi-yellow-bg">kwargs</span><span class="ansi-yellow-bg">)</span>
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/core/generic.py:3961</span>, in <span class="ansi-cyan-fg">NDFrame.to_csv</span><span class="ansi-blue-fg">(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, lineterminator, chunksize, date_format, doublequote, escapechar, decimal, errors, storage_options)</span>
-<span class="ansi-green-intense-fg ansi-bold">   3950</span> df <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(0,135,0)">self</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span style="color: rgb(0,135,0)">isinstance</span>(<span style="color: rgb(0,135,0)">self</span>, ABCDataFrame) <span class="ansi-bold" style="color: rgb(0,135,0)">else</span> <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>to_frame()
-<span class="ansi-green-intense-fg ansi-bold">   3952</span> formatter <span style="color: rgb(98,98,98)">=</span> DataFrameFormatter(
-<span class="ansi-green-intense-fg ansi-bold">   3953</span>     frame<span style="color: rgb(98,98,98)">=</span>df,
-<span class="ansi-green-intense-fg ansi-bold">   3954</span>     header<span style="color: rgb(98,98,98)">=</span>header,
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">   3958</span>     decimal<span style="color: rgb(98,98,98)">=</span>decimal,
-<span class="ansi-green-intense-fg ansi-bold">   3959</span> )
-<span class="ansi-green-fg">-&gt; 3961</span> <span class="ansi-bold" style="color: rgb(0,135,0)">return</span> <span class="ansi-yellow-bg">DataFrameRenderer</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">formatter</span><span class="ansi-yellow-bg">)</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">to_csv</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">   3962</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">path_or_buf</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3963</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">lineterminator</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">lineterminator</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3964</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">sep</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">sep</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3965</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">encoding</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">encoding</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3966</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">errors</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">errors</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3967</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">compression</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">compression</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3968</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">quoting</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">quoting</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3969</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">columns</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">columns</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3970</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">index_label</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">index_label</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3971</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">mode</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">mode</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3972</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">chunksize</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">chunksize</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3973</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">quotechar</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">quotechar</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3974</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">date_format</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">date_format</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3975</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">doublequote</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">doublequote</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3976</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">escapechar</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">escapechar</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3977</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">storage_options</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">storage_options</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">   3978</span> <span class="ansi-yellow-bg">)</span>
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/io/formats/format.py:1014</span>, in <span class="ansi-cyan-fg">DataFrameRenderer.to_csv</span><span class="ansi-blue-fg">(self, path_or_buf, encoding, sep, columns, index_label, mode, compression, quoting, quotechar, lineterminator, chunksize, date_format, doublequote, escapechar, errors, storage_options)</span>
-<span class="ansi-green-intense-fg ansi-bold">    993</span>     created_buffer <span style="color: rgb(98,98,98)">=</span> <span class="ansi-bold" style="color: rgb(0,135,0)">False</span>
-<span class="ansi-green-intense-fg ansi-bold">    995</span> csv_formatter <span style="color: rgb(98,98,98)">=</span> CSVFormatter(
-<span class="ansi-green-intense-fg ansi-bold">    996</span>     path_or_buf<span style="color: rgb(98,98,98)">=</span>path_or_buf,
-<span class="ansi-green-intense-fg ansi-bold">    997</span>     lineterminator<span style="color: rgb(98,98,98)">=</span>lineterminator,
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">   1012</span>     formatter<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>fmt,
-<span class="ansi-green-intense-fg ansi-bold">   1013</span> )
-<span class="ansi-green-fg">-&gt; 1014</span> <span class="ansi-yellow-bg">csv_formatter</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">save</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">   1016</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> created_buffer:
-<span class="ansi-green-intense-fg ansi-bold">   1017</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">assert</span> <span style="color: rgb(0,135,0)">isinstance</span>(path_or_buf, StringIO)
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/io/formats/csvs.py:251</span>, in <span class="ansi-cyan-fg">CSVFormatter.save</span><span class="ansi-blue-fg">(self)</span>
-<span class="ansi-green-intense-fg ansi-bold">    247</span> <span style="color: rgb(175,0,0)">&#34;&#34;&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">    248</span> <span style="color: rgb(175,0,0)">Create the writer &amp; save.</span>
-<span class="ansi-green-intense-fg ansi-bold">    249</span> <span style="color: rgb(175,0,0)">&#34;&#34;&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">    250</span> <span style="color: rgb(95,135,135)"># apply compression and byte/text conversion</span>
-<span class="ansi-green-fg">--&gt; 251</span> <span class="ansi-bold" style="color: rgb(0,135,0)">with</span> <span class="ansi-yellow-bg">get_handle</span><span class="ansi-yellow-bg">(</span>
-<span class="ansi-green-intense-fg ansi-bold">    252</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">filepath_or_buffer</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    253</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">mode</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    254</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">encoding</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">encoding</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    255</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">errors</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">errors</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    256</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">compression</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">compression</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    257</span> <span class="ansi-yellow-bg">    </span><span class="ansi-yellow-bg">storage_options</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">self</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">storage_options</span><span class="ansi-yellow-bg">,</span>
-<span class="ansi-green-intense-fg ansi-bold">    258</span> <span class="ansi-yellow-bg">)</span> <span class="ansi-bold" style="color: rgb(0,135,0)">as</span> handles:
-<span class="ansi-green-intense-fg ansi-bold">    259</span>     <span style="color: rgb(95,135,135)"># Note: self.encoding is irrelevant here</span>
-<span class="ansi-green-intense-fg ansi-bold">    260</span>     <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>writer <span style="color: rgb(98,98,98)">=</span> csvlib<span style="color: rgb(98,98,98)">.</span>writer(
-<span class="ansi-green-intense-fg ansi-bold">    261</span>         handles<span style="color: rgb(98,98,98)">.</span>handle,
-<span class="ansi-green-intense-fg ansi-bold">    262</span>         lineterminator<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>lineterminator,
-<span class="ansi-green-fg">   (...)</span>
-<span class="ansi-green-intense-fg ansi-bold">    267</span>         quotechar<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>quotechar,
-<span class="ansi-green-intense-fg ansi-bold">    268</span>     )
-<span class="ansi-green-intense-fg ansi-bold">    270</span>     <span style="color: rgb(0,135,0)">self</span><span style="color: rgb(98,98,98)">.</span>_save()
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/io/common.py:749</span>, in <span class="ansi-cyan-fg">get_handle</span><span class="ansi-blue-fg">(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)</span>
-<span class="ansi-green-intense-fg ansi-bold">    747</span> <span style="color: rgb(95,135,135)"># Only for write methods</span>
-<span class="ansi-green-intense-fg ansi-bold">    748</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">r</span><span style="color: rgb(175,0,0)">&#34;</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> <span class="ansi-bold" style="color: rgb(175,0,255)">in</span> mode <span class="ansi-bold" style="color: rgb(175,0,255)">and</span> is_path:
-<span class="ansi-green-fg">--&gt; 749</span>     <span class="ansi-yellow-bg">check_parent_directory</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg" style="color: rgb(0,135,0)">str</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">handle</span><span class="ansi-yellow-bg">)</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">    751</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> compression:
-<span class="ansi-green-intense-fg ansi-bold">    752</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> compression <span style="color: rgb(98,98,98)">!=</span> <span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">zstd</span><span style="color: rgb(175,0,0)">&#34;</span>:
-<span class="ansi-green-intense-fg ansi-bold">    753</span>         <span style="color: rgb(95,135,135)"># compression libraries do not like an explicit text-mode</span>
-
-File <span class="ansi-green-fg">/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/io/common.py:616</span>, in <span class="ansi-cyan-fg">check_parent_directory</span><span class="ansi-blue-fg">(path)</span>
-<span class="ansi-green-intense-fg ansi-bold">    614</span> parent <span style="color: rgb(98,98,98)">=</span> Path(path)<span style="color: rgb(98,98,98)">.</span>parent
-<span class="ansi-green-intense-fg ansi-bold">    615</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> parent<span style="color: rgb(98,98,98)">.</span>is_dir():
-<span class="ansi-green-fg">--&gt; 616</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">raise</span> <span class="ansi-bold" style="color: rgb(215,95,95)">OSError</span>(<span style="color: rgb(175,0,0)">rf</span><span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">Cannot save file into a non-existent directory: </span><span style="color: rgb(175,0,0)">&#39;</span><span class="ansi-bold" style="color: rgb(175,95,135)">{</span>parent<span class="ansi-bold" style="color: rgb(175,95,135)">}</span><span style="color: rgb(175,0,0)">&#39;</span><span style="color: rgb(175,0,0)">&#34;</span>)
-
-<span class="ansi-red-fg">OSError</span>: Cannot save file into a non-existent directory: &#39;/content/drive/MyDrive/misinformation-data&#39;
-</pre></div></div>
-</div>
 </section>
 <section id="Further-detector-modules">
 <h2>Further detector modules<a class="headerlink" href="#Further-detector-modules" title="Link to this heading"></a></h2>
diff --git a/build/html/notebooks/DemoNotebook_ammico.ipynb b/build/html/notebooks/DemoNotebook_ammico.ipynb
index 55ae8da..9dbe482 100644
--- a/build/html/notebooks/DemoNotebook_ammico.ipynb
+++ b/build/html/notebooks/DemoNotebook_ammico.ipynb
@@ -14,15 +14,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:42:44.420408Z",
-     "iopub.status.busy": "2024-02-19T08:42:44.420216Z",
-     "iopub.status.idle": "2024-02-19T08:42:44.428568Z",
-     "shell.execute_reply": "2024-02-19T08:42:44.428037Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# if running on google colab\n",
@@ -52,257 +45,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:42:44.430935Z",
-     "iopub.status.busy": "2024-02-19T08:42:44.430571Z",
-     "iopub.status.idle": "2024-02-19T08:42:51.757352Z",
-     "shell.execute_reply": "2024-02-19T08:42:51.756689Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Downloading readme:   0%|          | 0.00/21.0 [00:00<?, ?B/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Downloading readme: 100%|██████████| 21.0/21.0 [00:00<00:00, 178kB/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
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-     "name": "stderr",
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-      "\r",
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-     ]
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-      "\r",
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-     ]
-    },
-    {
-     "name": "stderr",
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-      "\r",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
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-      "\n"
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-     ]
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-      "\n"
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-      "Downloading data: 100%|██████████| 43.4k/43.4k [00:00<00:00, 153kB/s]"
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-      "\n"
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-      "\n"
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-      "Downloading data: 100%|██████████| 1.33M/1.33M [00:00<00:00, 3.27MB/s]"
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-      "\r",
-      "Downloading data: 100%|██████████| 1.33M/1.33M [00:00<00:00, 3.26MB/s]"
-     ]
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-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
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-    {
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-     "text": [
-      "\r",
-      "Downloading data: 100%|██████████| 687k/687k [00:00<00:00, 1.89MB/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Downloading data: 100%|██████████| 687k/687k [00:00<00:00, 1.89MB/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Generating train split: 0 examples [00:00, ? examples/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "Generating train split: 6 examples [00:00, 423.54 examples/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from datasets import load_dataset\n",
     "from pathlib import Path\n",
@@ -320,15 +65,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:42:51.760070Z",
-     "iopub.status.busy": "2024-02-19T08:42:51.759727Z",
-     "iopub.status.idle": "2024-02-19T08:42:52.642174Z",
-     "shell.execute_reply": "2024-02-19T08:42:52.641528Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "data_path = \"./data-test\"\n",
@@ -349,15 +87,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:42:52.645250Z",
-     "iopub.status.busy": "2024-02-19T08:42:52.644756Z",
-     "iopub.status.idle": "2024-02-19T08:43:01.940227Z",
-     "shell.execute_reply": "2024-02-19T08:43:01.939551Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
@@ -395,15 +126,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:43:01.943248Z",
-     "iopub.status.busy": "2024-02-19T08:43:01.942735Z",
-     "iopub.status.idle": "2024-02-19T08:43:01.945881Z",
-     "shell.execute_reply": "2024-02-19T08:43:01.945338Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = \"/content/drive/MyDrive/misinformation-data/misinformation-campaign-981aa55a3b13.json\""
@@ -431,15 +155,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:43:01.948209Z",
-     "iopub.status.busy": "2024-02-19T08:43:01.947847Z",
-     "iopub.status.idle": "2024-02-19T08:43:01.952312Z",
-     "shell.execute_reply": "2024-02-19T08:43:01.951836Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "image_dict = ammico.find_files(\n",
@@ -460,38 +177,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:43:01.954766Z",
-     "iopub.status.busy": "2024-02-19T08:43:01.954396Z",
-     "iopub.status.idle": "2024-02-19T08:43:01.978861Z",
-     "shell.execute_reply": "2024-02-19T08:43:01.977627Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"100%\"\n",
-       "            height=\"650\"\n",
-       "            src=\"http://127.0.0.1:8055/\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x7f966243a8e0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "analysis_explorer = ammico.AnalysisExplorer(image_dict)\n",
     "analysis_explorer.run_server(port=8055)"
@@ -514,15 +202,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:43:01.981825Z",
-     "iopub.status.busy": "2024-02-19T08:43:01.981158Z",
-     "iopub.status.idle": "2024-02-19T08:43:01.984935Z",
-     "shell.execute_reply": "2024-02-19T08:43:01.983983Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# dump file name\n",
@@ -540,221 +221,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:43:01.988248Z",
-     "iopub.status.busy": "2024-02-19T08:43:01.987632Z",
-     "iopub.status.idle": "2024-02-19T08:44:04.645259Z",
-     "shell.execute_reply": "2024-02-19T08:44:04.644561Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "  0%|          | 0/6 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Downloading data from 'https://github.com/serengil/deepface_models/releases/download/v1.0/retinaface.h5' to file '/home/runner/.cache/pooch/3be32af6e4183fa0156bc33bda371147-retinaface.h5'.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 17%|█▋        | 1/6 [00:08<00:41,  8.32s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found missing key(s) {'no_faces', 'age', 'wears_mask', 'emotion (category)', 'multiple_faces', 'emotion', 'gender', 'race', 'face'} in subdict img1 - setting to None.\n",
-      "Found missing key(s) {'no_faces', 'age', 'wears_mask', 'emotion (category)', 'multiple_faces', 'emotion', 'gender', 'race', 'face'} in subdict img2 - setting to None.\n",
-      "Found missing key(s) {'no_faces', 'age', 'wears_mask', 'emotion (category)', 'multiple_faces', 'emotion', 'gender', 'race', 'face'} in subdict img3 - setting to None.\n",
-      "Found missing key(s) {'no_faces', 'age', 'wears_mask', 'emotion (category)', 'multiple_faces', 'emotion', 'gender', 'race', 'face'} in subdict img0 - setting to None.\n",
-      "Found missing key(s) {'no_faces', 'age', 'wears_mask', 'emotion (category)', 'multiple_faces', 'emotion', 'gender', 'race', 'face'} in subdict img5 - setting to None.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 33%|███▎      | 2/6 [00:10<00:19,  4.89s/it]"
-     ]
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+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for num, key in tqdm(enumerate(image_dict.keys()), total=len(image_dict)):    # loop through all images\n",
     "    image_dict[key] = ammico.EmotionDetector(image_dict[key]).analyse_image() # analyse image with EmotionDetector and update dict\n",
@@ -773,4980 +242,9 @@
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-   "metadata": {
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+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for num, key in tqdm(enumerate(image_dict.keys()), total=len(image_dict)):  # loop through all images\n",
     "    image_dict[key] = ammico.TextDetector(image_dict[key], analyse_text=True).analyse_image() # analyse image with EmotionDetector and update dict\n",
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+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# clear memory on cuda first? Faces seems to always not release GPU\n",
     "# initialize the models\n",
@@ -7146,168 +289,9 @@
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "100%|██████████| 6/6 [02:33<00:00, 25.54s/it]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# initialize the models\n",
     "image_summary_detector = ammico.SummaryDetector(subdict = image_dict, analysis_type=\"summary\", model_type=\"base\")\n",
@@ -7339,15 +323,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:50:22.307625Z",
-     "iopub.status.busy": "2024-02-19T08:50:22.307231Z",
-     "iopub.status.idle": "2024-02-19T08:50:22.310969Z",
-     "shell.execute_reply": "2024-02-19T08:50:22.310432Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "image_df = ammico.get_dataframe(image_dict)"
@@ -7362,192 +339,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:50:22.313326Z",
-     "iopub.status.busy": "2024-02-19T08:50:22.312953Z",
-     "iopub.status.idle": "2024-02-19T08:50:22.351063Z",
-     "shell.execute_reply": "2024-02-19T08:50:22.350534Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>filename</th>\n",
-       "      <th>face</th>\n",
-       "      <th>multiple_faces</th>\n",
-       "      <th>no_faces</th>\n",
-       "      <th>wears_mask</th>\n",
-       "      <th>age</th>\n",
-       "      <th>gender</th>\n",
-       "      <th>race</th>\n",
-       "      <th>emotion</th>\n",
-       "      <th>emotion (category)</th>\n",
-       "      <th>...</th>\n",
-       "      <th>text_language</th>\n",
-       "      <th>text_english</th>\n",
-       "      <th>text_clean</th>\n",
-       "      <th>text_summary</th>\n",
-       "      <th>sentiment</th>\n",
-       "      <th>sentiment_score</th>\n",
-       "      <th>entity</th>\n",
-       "      <th>entity_type</th>\n",
-       "      <th>const_image_summary</th>\n",
-       "      <th>3_non-deterministic_summary</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>data-test/img4.png</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>0</td>\n",
-       "      <td>[No]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>...</td>\n",
-       "      <td>en</td>\n",
-       "      <td>MOODOVIN XI</td>\n",
-       "      <td>XI</td>\n",
-       "      <td>MOODOVIN XI XI: Vladimir Putin, Vladimir Vlad...</td>\n",
-       "      <td>POSITIVE</td>\n",
-       "      <td>0.66</td>\n",
-       "      <td>[MOODOVIN XI]</td>\n",
-       "      <td>[ORG]</td>\n",
-       "      <td>a river running through a city next to tall bu...</td>\n",
-       "      <td>[buildings near a waterway with small boats pa...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>data-test/img1.png</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>0</td>\n",
-       "      <td>[No]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>...</td>\n",
-       "      <td>en</td>\n",
-       "      <td>SCATTERING THEORY The Quantum Theory of Nonrel...</td>\n",
-       "      <td>THEORY The Quantum Theory of Collisions JOHN R...</td>\n",
-       "      <td>SCATTERING THEORY The Quantum Theory of Nonre...</td>\n",
-       "      <td>POSITIVE</td>\n",
-       "      <td>0.91</td>\n",
-       "      <td>[Non, ##vist, Col, ##N, R, T, ##AYL, Universit...</td>\n",
-       "      <td>[MISC, MISC, MISC, ORG, PER, PER, ORG, ORG]</td>\n",
-       "      <td>a close up of a piece of paper with writing on it</td>\n",
-       "      <td>[a white paper with some black writing on it, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>data-test/img2.png</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>0</td>\n",
-       "      <td>[No]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>...</td>\n",
-       "      <td>en</td>\n",
-       "      <td>THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO M...</td>\n",
-       "      <td>THE PROBLEM DOM NVS TIO MINA Monographs on Num...</td>\n",
-       "      <td>H. H. W. WILKINSON: The Algebri</td>\n",
-       "      <td>NEGATIVE</td>\n",
-       "      <td>0.97</td>\n",
-       "      <td>[ALGEBRAIC EIGENVAL, NVS TIO MI, J, H, WILKINSON]</td>\n",
-       "      <td>[MISC, ORG, ORG, ORG, ORG]</td>\n",
-       "      <td>a yellow book with green lettering on it</td>\n",
-       "      <td>[an old book with a picture of the slogan of t...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3 rows × 21 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             filename face multiple_faces  no_faces wears_mask     age  \\\n",
-       "0  data-test/img4.png   No             No         0       [No]  [None]   \n",
-       "1  data-test/img1.png   No             No         0       [No]  [None]   \n",
-       "2  data-test/img2.png   No             No         0       [No]  [None]   \n",
-       "\n",
-       "   gender    race emotion emotion (category)  ... text_language  \\\n",
-       "0  [None]  [None]  [None]             [None]  ...            en   \n",
-       "1  [None]  [None]  [None]             [None]  ...            en   \n",
-       "2  [None]  [None]  [None]             [None]  ...            en   \n",
-       "\n",
-       "                                        text_english  \\\n",
-       "0                                        MOODOVIN XI   \n",
-       "1  SCATTERING THEORY The Quantum Theory of Nonrel...   \n",
-       "2  THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO M...   \n",
-       "\n",
-       "                                          text_clean  \\\n",
-       "0                                                 XI   \n",
-       "1  THEORY The Quantum Theory of Collisions JOHN R...   \n",
-       "2  THE PROBLEM DOM NVS TIO MINA Monographs on Num...   \n",
-       "\n",
-       "                                        text_summary sentiment  \\\n",
-       "0   MOODOVIN XI XI: Vladimir Putin, Vladimir Vlad...  POSITIVE   \n",
-       "1   SCATTERING THEORY The Quantum Theory of Nonre...  POSITIVE   \n",
-       "2                    H. H. W. WILKINSON: The Algebri  NEGATIVE   \n",
-       "\n",
-       "  sentiment_score                                             entity  \\\n",
-       "0            0.66                                      [MOODOVIN XI]   \n",
-       "1            0.91  [Non, ##vist, Col, ##N, R, T, ##AYL, Universit...   \n",
-       "2            0.97  [ALGEBRAIC EIGENVAL, NVS TIO MI, J, H, WILKINSON]   \n",
-       "\n",
-       "                                   entity_type  \\\n",
-       "0                                        [ORG]   \n",
-       "1  [MISC, MISC, MISC, ORG, PER, PER, ORG, ORG]   \n",
-       "2                   [MISC, ORG, ORG, ORG, ORG]   \n",
-       "\n",
-       "                                 const_image_summary  \\\n",
-       "0  a river running through a city next to tall bu...   \n",
-       "1  a close up of a piece of paper with writing on it   \n",
-       "2           a yellow book with green lettering on it   \n",
-       "\n",
-       "                         3_non-deterministic_summary  \n",
-       "0  [buildings near a waterway with small boats pa...  \n",
-       "1  [a white paper with some black writing on it, ...  \n",
-       "2  [an old book with a picture of the slogan of t...  \n",
-       "\n",
-       "[3 rows x 21 columns]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "image_df.head(3)"
    ]
@@ -7561,34 +355,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:50:22.391547Z",
-     "iopub.status.busy": "2024-02-19T08:50:22.391161Z",
-     "iopub.status.idle": "2024-02-19T08:50:25.022235Z",
-     "shell.execute_reply": "2024-02-19T08:50:25.021403Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "OSError",
-     "evalue": "Cannot save file into a non-existent directory: '/content/drive/MyDrive/misinformation-data'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mimage_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/content/drive/MyDrive/misinformation-data/data_out.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/util/_decorators.py:333\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    327\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m    328\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    329\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m    330\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    331\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m    332\u001b[0m     )\n\u001b[0;32m--> 333\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/core/generic.py:3961\u001b[0m, in \u001b[0;36mNDFrame.to_csv\u001b[0;34m(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, lineterminator, chunksize, date_format, doublequote, escapechar, decimal, errors, storage_options)\u001b[0m\n\u001b[1;32m   3950\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m, ABCDataFrame) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mto_frame()\n\u001b[1;32m   3952\u001b[0m formatter \u001b[38;5;241m=\u001b[39m DataFrameFormatter(\n\u001b[1;32m   3953\u001b[0m     frame\u001b[38;5;241m=\u001b[39mdf,\n\u001b[1;32m   3954\u001b[0m     header\u001b[38;5;241m=\u001b[39mheader,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3958\u001b[0m     decimal\u001b[38;5;241m=\u001b[39mdecimal,\n\u001b[1;32m   3959\u001b[0m )\n\u001b[0;32m-> 3961\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mDataFrameRenderer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mformatter\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_csv\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3962\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath_or_buf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3963\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlineterminator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlineterminator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3964\u001b[0m \u001b[43m    \u001b[49m\u001b[43msep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msep\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3965\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3966\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3967\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3968\u001b[0m \u001b[43m    \u001b[49m\u001b[43mquoting\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquoting\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3969\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3970\u001b[0m \u001b[43m    \u001b[49m\u001b[43mindex_label\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex_label\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3971\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3972\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunksize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunksize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3973\u001b[0m \u001b[43m    \u001b[49m\u001b[43mquotechar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquotechar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3974\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdate_format\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdate_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3975\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdoublequote\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdoublequote\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3976\u001b[0m \u001b[43m    \u001b[49m\u001b[43mescapechar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mescapechar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3977\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3978\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/io/formats/format.py:1014\u001b[0m, in \u001b[0;36mDataFrameRenderer.to_csv\u001b[0;34m(self, path_or_buf, encoding, sep, columns, index_label, mode, compression, quoting, quotechar, lineterminator, chunksize, date_format, doublequote, escapechar, errors, storage_options)\u001b[0m\n\u001b[1;32m    993\u001b[0m     created_buffer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    995\u001b[0m csv_formatter \u001b[38;5;241m=\u001b[39m CSVFormatter(\n\u001b[1;32m    996\u001b[0m     path_or_buf\u001b[38;5;241m=\u001b[39mpath_or_buf,\n\u001b[1;32m    997\u001b[0m     lineterminator\u001b[38;5;241m=\u001b[39mlineterminator,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1012\u001b[0m     formatter\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfmt,\n\u001b[1;32m   1013\u001b[0m )\n\u001b[0;32m-> 1014\u001b[0m \u001b[43mcsv_formatter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1016\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m created_buffer:\n\u001b[1;32m   1017\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(path_or_buf, StringIO)\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/io/formats/csvs.py:251\u001b[0m, in \u001b[0;36mCSVFormatter.save\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    247\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    248\u001b[0m \u001b[38;5;124;03mCreate the writer & save.\u001b[39;00m\n\u001b[1;32m    249\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    250\u001b[0m \u001b[38;5;66;03m# apply compression and byte/text conversion\u001b[39;00m\n\u001b[0;32m--> 251\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    252\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    253\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    254\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    255\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    256\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    257\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    258\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m handles:\n\u001b[1;32m    259\u001b[0m     \u001b[38;5;66;03m# Note: self.encoding is irrelevant here\u001b[39;00m\n\u001b[1;32m    260\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwriter \u001b[38;5;241m=\u001b[39m csvlib\u001b[38;5;241m.\u001b[39mwriter(\n\u001b[1;32m    261\u001b[0m         handles\u001b[38;5;241m.\u001b[39mhandle,\n\u001b[1;32m    262\u001b[0m         lineterminator\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlineterminator,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    267\u001b[0m         quotechar\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mquotechar,\n\u001b[1;32m    268\u001b[0m     )\n\u001b[1;32m    270\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_save()\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/io/common.py:749\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    747\u001b[0m \u001b[38;5;66;03m# Only for write methods\u001b[39;00m\n\u001b[1;32m    748\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode \u001b[38;5;129;01mand\u001b[39;00m is_path:\n\u001b[0;32m--> 749\u001b[0m     \u001b[43mcheck_parent_directory\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    751\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m compression:\n\u001b[1;32m    752\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m compression \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mzstd\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    753\u001b[0m         \u001b[38;5;66;03m# compression libraries do not like an explicit text-mode\u001b[39;00m\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/io/common.py:616\u001b[0m, in \u001b[0;36mcheck_parent_directory\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m    614\u001b[0m parent \u001b[38;5;241m=\u001b[39m Path(path)\u001b[38;5;241m.\u001b[39mparent\n\u001b[1;32m    615\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m parent\u001b[38;5;241m.\u001b[39mis_dir():\n\u001b[0;32m--> 616\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(\u001b[38;5;124mrf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot save file into a non-existent directory: \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparent\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "\u001b[0;31mOSError\u001b[0m: Cannot save file into a non-existent directory: '/content/drive/MyDrive/misinformation-data'"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "image_df.to_csv(\"/content/drive/MyDrive/misinformation-data/data_out.csv\")"
    ]
@@ -7611,15 +380,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:50:25.032719Z",
-     "iopub.status.busy": "2024-02-19T08:50:25.032386Z",
-     "iopub.status.idle": "2024-02-19T08:50:25.035336Z",
-     "shell.execute_reply": "2024-02-19T08:50:25.034770Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = \"/content/drive/MyDrive/misinformation-data/misinformation-campaign-981aa55a3b13.json\"\n"
@@ -7636,88 +398,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:50:25.037648Z",
-     "iopub.status.busy": "2024-02-19T08:50:25.037344Z",
-     "iopub.status.idle": "2024-02-19T08:51:21.184249Z",
-     "shell.execute_reply": "2024-02-19T08:51:21.183549Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "  0%|          | 0/6 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 17%|█▋        | 1/6 [00:09<00:47,  9.51s/it]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 33%|███▎      | 2/6 [00:18<00:36,  9.10s/it]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 50%|█████     | 3/6 [00:27<00:27,  9.07s/it]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 67%|██████▋   | 4/6 [00:37<00:18,  9.49s/it]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 83%|████████▎ | 5/6 [00:46<00:09,  9.38s/it]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "100%|██████████| 6/6 [00:56<00:00,  9.41s/it]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "100%|██████████| 6/6 [00:56<00:00,  9.36s/it]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for num, key in tqdm(enumerate(image_dict.keys()), total=len(image_dict)):  # loop through all images\n",
     "    image_dict[key] = ammico.TextDetector(image_dict[key],                  # analyse image with TextDetector and update dict\n",
@@ -7793,15 +476,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:51:21.186991Z",
-     "iopub.status.busy": "2024-02-19T08:51:21.186599Z",
-     "iopub.status.idle": "2024-02-19T08:51:27.937584Z",
-     "shell.execute_reply": "2024-02-19T08:51:27.936965Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "image_summary_detector = ammico.SummaryDetector(image_dict, analysis_type=\"summary\", model_type=\"base\")"
@@ -7809,88 +485,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:51:27.945516Z",
-     "iopub.status.busy": "2024-02-19T08:51:27.945076Z",
-     "iopub.status.idle": "2024-02-19T08:52:37.388418Z",
-     "shell.execute_reply": "2024-02-19T08:52:37.387691Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "  0%|          | 0/6 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 17%|█▋        | 1/6 [00:13<01:06, 13.34s/it]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 33%|███▎      | 2/6 [00:24<00:48, 12.03s/it]"
-     ]
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+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for num, key in tqdm(enumerate(image_dict.keys()),total=len(image_dict)):\n",
     "    image_dict[key] = image_summary_detector.analyse_image(subdict = image_dict[key], analysis_type=\"summary\")\n",
@@ -7909,15 +506,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:52:37.396555Z",
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-     "shell.execute_reply": "2024-02-19T08:52:37.398848Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "list_of_questions = [\n",
@@ -7936,654 +526,9 @@
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-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[21], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m image_summary_vqa_detector \u001b[38;5;241m=\u001b[39m \u001b[43mammico\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSummaryDetector\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43manalysis_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mquestions\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m                                                    \u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvqa\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m num, key \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28menumerate\u001b[39m(image_dict\u001b[38;5;241m.\u001b[39mkeys()),total\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(image_dict)):\n\u001b[1;32m      5\u001b[0m     image_dict[key] \u001b[38;5;241m=\u001b[39m image_summary_vqa_detector\u001b[38;5;241m.\u001b[39manalyse_image(subdict\u001b[38;5;241m=\u001b[39mimage_dict[key], \n\u001b[1;32m      6\u001b[0m                                                                analysis_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquestions\u001b[39m\u001b[38;5;124m\"\u001b[39m, \n\u001b[1;32m      7\u001b[0m                                                                list_of_questions \u001b[38;5;241m=\u001b[39m list_of_questions)\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/summary.py:141\u001b[0m, in \u001b[0;36mSummaryDetector.__init__\u001b[0;34m(self, subdict, model_type, analysis_type, list_of_questions, summary_model, summary_vis_processors, summary_vqa_model, summary_vqa_vis_processors, summary_vqa_txt_processors, summary_vqa_model_new, summary_vqa_vis_processors_new, summary_vqa_txt_processors_new, device_type)\u001b[0m\n\u001b[1;32m    127\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vis_processors \u001b[38;5;241m=\u001b[39m summary_vis_processors\n\u001b[1;32m    128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m    129\u001b[0m     model_type \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mallowed_model_types\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (summary_vqa_model \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    135\u001b[0m     )\n\u001b[1;32m    136\u001b[0m ):\n\u001b[1;32m    137\u001b[0m     (\n\u001b[1;32m    138\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_model,\n\u001b[1;32m    139\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_vis_processors,\n\u001b[1;32m    140\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_txt_processors,\n\u001b[0;32m--> 141\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_vqa_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    142\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    143\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_model \u001b[38;5;241m=\u001b[39m summary_vqa_model\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/summary.py:232\u001b[0m, in \u001b[0;36mSummaryDetector.load_vqa_model\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    216\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_vqa_model\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    217\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    218\u001b[0m \u001b[38;5;124;03m    Load blip_vqa model and preprocessors for visual and text inputs from lavis.models.\u001b[39;00m\n\u001b[1;32m    219\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    226\u001b[0m \n\u001b[1;32m    227\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m    228\u001b[0m     (\n\u001b[1;32m    229\u001b[0m         summary_vqa_model,\n\u001b[1;32m    230\u001b[0m         summary_vqa_vis_processors,\n\u001b[1;32m    231\u001b[0m         summary_vqa_txt_processors,\n\u001b[0;32m--> 232\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[43mload_model_and_preprocess\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    233\u001b[0m \u001b[43m        \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mblip_vqa\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    234\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvqav2\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    235\u001b[0m \u001b[43m        \u001b[49m\u001b[43mis_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    236\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msummary_device\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    237\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    238\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m summary_vqa_model, summary_vqa_vis_processors, summary_vqa_txt_processors\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/__init__.py:195\u001b[0m, in \u001b[0;36mload_model_and_preprocess\u001b[0;34m(name, model_type, is_eval, device)\u001b[0m\n\u001b[1;32m    192\u001b[0m model_cls \u001b[38;5;241m=\u001b[39m registry\u001b[38;5;241m.\u001b[39mget_model_class(name)\n\u001b[1;32m    194\u001b[0m \u001b[38;5;66;03m# load model\u001b[39;00m\n\u001b[0;32m--> 195\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_cls\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    197\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_eval:\n\u001b[1;32m    198\u001b[0m     model\u001b[38;5;241m.\u001b[39meval()\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/base_model.py:70\u001b[0m, in \u001b[0;36mBaseModel.from_pretrained\u001b[0;34m(cls, model_type)\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     61\u001b[0m \u001b[38;5;124;03mBuild a pretrained model from default configuration file, specified by model_type.\u001b[39;00m\n\u001b[1;32m     62\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[38;5;124;03m    - model (nn.Module): pretrained or finetuned model, depending on the configuration.\u001b[39;00m\n\u001b[1;32m     68\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     69\u001b[0m model_cfg \u001b[38;5;241m=\u001b[39m OmegaConf\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mdefault_config_path(model_type))\u001b[38;5;241m.\u001b[39mmodel\n\u001b[0;32m---> 70\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_cfg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/blip_models/blip_vqa.py:373\u001b[0m, in \u001b[0;36mBlipVQA.from_config\u001b[0;34m(cls, cfg)\u001b[0m\n\u001b[1;32m    364\u001b[0m max_txt_len \u001b[38;5;241m=\u001b[39m cfg\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax_txt_len\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m35\u001b[39m)\n\u001b[1;32m    366\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(\n\u001b[1;32m    367\u001b[0m     image_encoder\u001b[38;5;241m=\u001b[39mimage_encoder,\n\u001b[1;32m    368\u001b[0m     text_encoder\u001b[38;5;241m=\u001b[39mtext_encoder,\n\u001b[1;32m    369\u001b[0m     text_decoder\u001b[38;5;241m=\u001b[39mtext_decoder,\n\u001b[1;32m    370\u001b[0m     max_txt_len\u001b[38;5;241m=\u001b[39mmax_txt_len,\n\u001b[1;32m    371\u001b[0m )\n\u001b[0;32m--> 373\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_checkpoint_from_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcfg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    375\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/base_model.py:95\u001b[0m, in \u001b[0;36mBaseModel.load_checkpoint_from_config\u001b[0;34m(self, cfg, **kwargs)\u001b[0m\n\u001b[1;32m     91\u001b[0m     finetune_path \u001b[38;5;241m=\u001b[39m cfg\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfinetuned\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m     92\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m (\n\u001b[1;32m     93\u001b[0m         finetune_path \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     94\u001b[0m     ), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound load_finetuned is True, but finetune_path is None.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 95\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_checkpoint\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl_or_filename\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfinetune_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     96\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     97\u001b[0m     \u001b[38;5;66;03m# load pre-trained weights\u001b[39;00m\n\u001b[1;32m     98\u001b[0m     pretrain_path \u001b[38;5;241m=\u001b[39m cfg\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpretrained\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/base_model.py:37\u001b[0m, in \u001b[0;36mBaseModel.load_checkpoint\u001b[0;34m(self, url_or_filename)\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     31\u001b[0m \u001b[38;5;124;03mLoad from a finetuned checkpoint.\u001b[39;00m\n\u001b[1;32m     32\u001b[0m \n\u001b[1;32m     33\u001b[0m \u001b[38;5;124;03mThis should expect no mismatch in the model keys and the checkpoint keys.\u001b[39;00m\n\u001b[1;32m     34\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     36\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_url(url_or_filename):\n\u001b[0;32m---> 37\u001b[0m     cached_file \u001b[38;5;241m=\u001b[39m \u001b[43mdownload_cached_file\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     38\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl_or_filename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_hash\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m     39\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     40\u001b[0m     checkpoint \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mload(cached_file, map_location\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     41\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misfile(url_or_filename):\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/common/dist_utils.py:132\u001b[0m, in \u001b[0;36mdownload_cached_file\u001b[0;34m(url, check_hash, progress)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cached_file\n\u001b[1;32m    131\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_main_process():\n\u001b[0;32m--> 132\u001b[0m     \u001b[43mtimm_hub\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_cached_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_hash\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    134\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_dist_avail_and_initialized():\n\u001b[1;32m    135\u001b[0m     dist\u001b[38;5;241m.\u001b[39mbarrier()\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/timm/models/hub.py:51\u001b[0m, in \u001b[0;36mdownload_cached_file\u001b[0;34m(url, check_hash, progress)\u001b[0m\n\u001b[1;32m     49\u001b[0m         r \u001b[38;5;241m=\u001b[39m HASH_REGEX\u001b[38;5;241m.\u001b[39msearch(filename)  \u001b[38;5;66;03m# r is Optional[Match[str]]\u001b[39;00m\n\u001b[1;32m     50\u001b[0m         hash_prefix \u001b[38;5;241m=\u001b[39m r\u001b[38;5;241m.\u001b[39mgroup(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m r \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 51\u001b[0m     \u001b[43mdownload_url_to_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcached_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhash_prefix\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprogress\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cached_file\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/hub.py:636\u001b[0m, in \u001b[0;36mdownload_url_to_file\u001b[0;34m(url, dst, hash_prefix, progress)\u001b[0m\n\u001b[1;32m    634\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(buffer) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    635\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m--> 636\u001b[0m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    637\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m hash_prefix \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    638\u001b[0m     sha256\u001b[38;5;241m.\u001b[39mupdate(buffer)\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/tempfile.py:478\u001b[0m, in \u001b[0;36m_TemporaryFileWrapper.__getattr__.<locals>.func_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    476\u001b[0m \u001b[38;5;129m@_functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m    477\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfunc_wrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 478\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[0;31mOSError\u001b[0m: [Errno 28] No space left on device"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "image_summary_vqa_detector = ammico.SummaryDetector(image_dict, analysis_type=\"questions\", \n",
     "                                                    model_type=\"vqa\")\n",
@@ -8606,462 +551,9 @@
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-    {
-     "ename": "OSError",
-     "evalue": "[Errno 28] No space left on device",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[22], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m image_summary_vqa_detector \u001b[38;5;241m=\u001b[39m \u001b[43mammico\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSummaryDetector\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43manalysis_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msummary_and_questions\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m                                                    \u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbase\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m num, key \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28menumerate\u001b[39m(image_dict\u001b[38;5;241m.\u001b[39mkeys()),total\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(image_dict)):\n\u001b[1;32m      4\u001b[0m     image_dict[key] \u001b[38;5;241m=\u001b[39m image_summary_vqa_detector\u001b[38;5;241m.\u001b[39manalyse_image(subdict\u001b[38;5;241m=\u001b[39mimage_dict[key], \n\u001b[1;32m      5\u001b[0m                                                                analysis_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msummary_and_questions\u001b[39m\u001b[38;5;124m\"\u001b[39m, \n\u001b[1;32m      6\u001b[0m                                                                list_of_questions \u001b[38;5;241m=\u001b[39m list_of_questions)\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/summary.py:141\u001b[0m, in \u001b[0;36mSummaryDetector.__init__\u001b[0;34m(self, subdict, model_type, analysis_type, list_of_questions, summary_model, summary_vis_processors, summary_vqa_model, summary_vqa_vis_processors, summary_vqa_txt_processors, summary_vqa_model_new, summary_vqa_vis_processors_new, summary_vqa_txt_processors_new, device_type)\u001b[0m\n\u001b[1;32m    127\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vis_processors \u001b[38;5;241m=\u001b[39m summary_vis_processors\n\u001b[1;32m    128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m    129\u001b[0m     model_type \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mallowed_model_types\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (summary_vqa_model \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    135\u001b[0m     )\n\u001b[1;32m    136\u001b[0m ):\n\u001b[1;32m    137\u001b[0m     (\n\u001b[1;32m    138\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_model,\n\u001b[1;32m    139\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_vis_processors,\n\u001b[1;32m    140\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_txt_processors,\n\u001b[0;32m--> 141\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_vqa_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    142\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    143\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_model \u001b[38;5;241m=\u001b[39m summary_vqa_model\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/summary.py:232\u001b[0m, in \u001b[0;36mSummaryDetector.load_vqa_model\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    216\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_vqa_model\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    217\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    218\u001b[0m \u001b[38;5;124;03m    Load blip_vqa model and preprocessors for visual and text inputs from lavis.models.\u001b[39;00m\n\u001b[1;32m    219\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    226\u001b[0m \n\u001b[1;32m    227\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m    228\u001b[0m     (\n\u001b[1;32m    229\u001b[0m         summary_vqa_model,\n\u001b[1;32m    230\u001b[0m         summary_vqa_vis_processors,\n\u001b[1;32m    231\u001b[0m         summary_vqa_txt_processors,\n\u001b[0;32m--> 232\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[43mload_model_and_preprocess\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    233\u001b[0m \u001b[43m        \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mblip_vqa\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    234\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvqav2\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    235\u001b[0m \u001b[43m        \u001b[49m\u001b[43mis_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    236\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msummary_device\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    237\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    238\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m summary_vqa_model, summary_vqa_vis_processors, summary_vqa_txt_processors\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/__init__.py:195\u001b[0m, in \u001b[0;36mload_model_and_preprocess\u001b[0;34m(name, model_type, is_eval, device)\u001b[0m\n\u001b[1;32m    192\u001b[0m model_cls \u001b[38;5;241m=\u001b[39m registry\u001b[38;5;241m.\u001b[39mget_model_class(name)\n\u001b[1;32m    194\u001b[0m \u001b[38;5;66;03m# load model\u001b[39;00m\n\u001b[0;32m--> 195\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_cls\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    197\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_eval:\n\u001b[1;32m    198\u001b[0m     model\u001b[38;5;241m.\u001b[39meval()\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/base_model.py:70\u001b[0m, in \u001b[0;36mBaseModel.from_pretrained\u001b[0;34m(cls, model_type)\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     61\u001b[0m \u001b[38;5;124;03mBuild a pretrained model from default configuration file, specified by model_type.\u001b[39;00m\n\u001b[1;32m     62\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[38;5;124;03m    - model (nn.Module): pretrained or finetuned model, depending on the configuration.\u001b[39;00m\n\u001b[1;32m     68\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     69\u001b[0m model_cfg \u001b[38;5;241m=\u001b[39m OmegaConf\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mdefault_config_path(model_type))\u001b[38;5;241m.\u001b[39mmodel\n\u001b[0;32m---> 70\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_cfg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/blip_models/blip_vqa.py:373\u001b[0m, in \u001b[0;36mBlipVQA.from_config\u001b[0;34m(cls, cfg)\u001b[0m\n\u001b[1;32m    364\u001b[0m max_txt_len \u001b[38;5;241m=\u001b[39m cfg\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax_txt_len\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m35\u001b[39m)\n\u001b[1;32m    366\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(\n\u001b[1;32m    367\u001b[0m     image_encoder\u001b[38;5;241m=\u001b[39mimage_encoder,\n\u001b[1;32m    368\u001b[0m     text_encoder\u001b[38;5;241m=\u001b[39mtext_encoder,\n\u001b[1;32m    369\u001b[0m     text_decoder\u001b[38;5;241m=\u001b[39mtext_decoder,\n\u001b[1;32m    370\u001b[0m     max_txt_len\u001b[38;5;241m=\u001b[39mmax_txt_len,\n\u001b[1;32m    371\u001b[0m )\n\u001b[0;32m--> 373\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_checkpoint_from_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcfg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    375\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/base_model.py:95\u001b[0m, in \u001b[0;36mBaseModel.load_checkpoint_from_config\u001b[0;34m(self, cfg, **kwargs)\u001b[0m\n\u001b[1;32m     91\u001b[0m     finetune_path \u001b[38;5;241m=\u001b[39m cfg\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfinetuned\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m     92\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m (\n\u001b[1;32m     93\u001b[0m         finetune_path \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     94\u001b[0m     ), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound load_finetuned is True, but finetune_path is None.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 95\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_checkpoint\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl_or_filename\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfinetune_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     96\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     97\u001b[0m     \u001b[38;5;66;03m# load pre-trained weights\u001b[39;00m\n\u001b[1;32m     98\u001b[0m     pretrain_path \u001b[38;5;241m=\u001b[39m cfg\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpretrained\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/base_model.py:37\u001b[0m, in \u001b[0;36mBaseModel.load_checkpoint\u001b[0;34m(self, url_or_filename)\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     31\u001b[0m \u001b[38;5;124;03mLoad from a finetuned checkpoint.\u001b[39;00m\n\u001b[1;32m     32\u001b[0m \n\u001b[1;32m     33\u001b[0m \u001b[38;5;124;03mThis should expect no mismatch in the model keys and the checkpoint keys.\u001b[39;00m\n\u001b[1;32m     34\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     36\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_url(url_or_filename):\n\u001b[0;32m---> 37\u001b[0m     cached_file \u001b[38;5;241m=\u001b[39m \u001b[43mdownload_cached_file\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     38\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl_or_filename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_hash\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m     39\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     40\u001b[0m     checkpoint \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mload(cached_file, map_location\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     41\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misfile(url_or_filename):\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/common/dist_utils.py:132\u001b[0m, in \u001b[0;36mdownload_cached_file\u001b[0;34m(url, check_hash, progress)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cached_file\n\u001b[1;32m    131\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_main_process():\n\u001b[0;32m--> 132\u001b[0m     \u001b[43mtimm_hub\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_cached_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_hash\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    134\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_dist_avail_and_initialized():\n\u001b[1;32m    135\u001b[0m     dist\u001b[38;5;241m.\u001b[39mbarrier()\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/timm/models/hub.py:51\u001b[0m, in \u001b[0;36mdownload_cached_file\u001b[0;34m(url, check_hash, progress)\u001b[0m\n\u001b[1;32m     49\u001b[0m         r \u001b[38;5;241m=\u001b[39m HASH_REGEX\u001b[38;5;241m.\u001b[39msearch(filename)  \u001b[38;5;66;03m# r is Optional[Match[str]]\u001b[39;00m\n\u001b[1;32m     50\u001b[0m         hash_prefix \u001b[38;5;241m=\u001b[39m r\u001b[38;5;241m.\u001b[39mgroup(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m r \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 51\u001b[0m     \u001b[43mdownload_url_to_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcached_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhash_prefix\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprogress\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cached_file\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/hub.py:636\u001b[0m, in \u001b[0;36mdownload_url_to_file\u001b[0;34m(url, dst, hash_prefix, progress)\u001b[0m\n\u001b[1;32m    634\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(buffer) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    635\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m--> 636\u001b[0m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    637\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m hash_prefix \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    638\u001b[0m     sha256\u001b[38;5;241m.\u001b[39mupdate(buffer)\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/tempfile.py:478\u001b[0m, in \u001b[0;36m_TemporaryFileWrapper.__getattr__.<locals>.func_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    476\u001b[0m \u001b[38;5;129m@_functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m    477\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfunc_wrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 478\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[0;31mOSError\u001b[0m: [Errno 28] No space left on device"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "image_summary_vqa_detector = ammico.SummaryDetector(image_dict, analysis_type=\"summary_and_questions\", \n",
     "                                                    model_type=\"base\")\n",
@@ -9097,792 +589,9 @@
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-    {
-     "ename": "OSError",
-     "evalue": "[Errno 28] No space left on device",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[23], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m obj \u001b[38;5;241m=\u001b[39m \u001b[43mammico\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSummaryDetector\u001b[49m\u001b[43m(\u001b[49m\u001b[43msubdict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mimage_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43manalysis_type\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msummary_and_questions\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mblip2_t5_caption_coco_flant5xl\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m# list of the new models that can be used:\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# \"blip2_t5_pretrain_flant5xxl\",\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# \"blip2_t5_pretrain_flant5xl\",\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     14\u001b[0m \n\u001b[1;32m     15\u001b[0m \u001b[38;5;66;03m#also you can perform all calculation on cpu if you set device_type= \"cpu\" or gpu if you set device_type= \"cuda\"\u001b[39;00m\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/summary.py:156\u001b[0m, in \u001b[0;36mSummaryDetector.__init__\u001b[0;34m(self, subdict, model_type, analysis_type, list_of_questions, summary_model, summary_vis_processors, summary_vqa_model, summary_vqa_vis_processors, summary_vqa_txt_processors, summary_vqa_model_new, summary_vqa_vis_processors_new, summary_vqa_txt_processors_new, device_type)\u001b[0m\n\u001b[1;32m    145\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_txt_processors \u001b[38;5;241m=\u001b[39m summary_vqa_txt_processors\n\u001b[1;32m    146\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m    147\u001b[0m     model_type \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mallowed_new_model_types\n\u001b[1;32m    148\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (summary_vqa_model_new \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    149\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (summary_vqa_vis_processors_new \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    150\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (summary_vqa_txt_processors_new \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    151\u001b[0m ):\n\u001b[1;32m    152\u001b[0m     (\n\u001b[1;32m    153\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_model_new,\n\u001b[1;32m    154\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_vis_processors_new,\n\u001b[1;32m    155\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_txt_processors_new,\n\u001b[0;32m--> 156\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_new_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    157\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    158\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msummary_vqa_model_new \u001b[38;5;241m=\u001b[39m summary_vqa_model_new\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/summary.py:479\u001b[0m, in \u001b[0;36mSummaryDetector.load_new_model\u001b[0;34m(self, model_type)\u001b[0m\n\u001b[1;32m    455\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    456\u001b[0m \u001b[38;5;124;03mLoad new BLIP2 models.\u001b[39;00m\n\u001b[1;32m    457\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    464\u001b[0m \u001b[38;5;124;03m    txt_processors (dict): preprocessors for text inputs.\u001b[39;00m\n\u001b[1;32m    465\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    466\u001b[0m select_model \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    467\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mblip2_t5_pretrain_flant5xxl\u001b[39m\u001b[38;5;124m\"\u001b[39m: SummaryDetector\u001b[38;5;241m.\u001b[39mload_model_blip2_t5_pretrain_flant5xxl,\n\u001b[1;32m    468\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mblip2_t5_pretrain_flant5xl\u001b[39m\u001b[38;5;124m\"\u001b[39m: SummaryDetector\u001b[38;5;241m.\u001b[39mload_model_blip2_t5_pretrain_flant5xl,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    473\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mblip2_opt_caption_coco_opt6.7b\u001b[39m\u001b[38;5;124m\"\u001b[39m: SummaryDetector\u001b[38;5;241m.\u001b[39mload_model_base_blip2_opt_caption_coco_opt67b,\n\u001b[1;32m    474\u001b[0m }\n\u001b[1;32m    475\u001b[0m (\n\u001b[1;32m    476\u001b[0m     summary_vqa_model,\n\u001b[1;32m    477\u001b[0m     summary_vqa_vis_processors,\n\u001b[1;32m    478\u001b[0m     summary_vqa_txt_processors,\n\u001b[0;32m--> 479\u001b[0m ) \u001b[38;5;241m=\u001b[39m \u001b[43mselect_model\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    480\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m summary_vqa_model, summary_vqa_vis_processors, summary_vqa_txt_processors\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/summary.py:543\u001b[0m, in \u001b[0;36mSummaryDetector.load_model_blip2_t5_caption_coco_flant5xl\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    528\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_model_blip2_t5_caption_coco_flant5xl\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    529\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    530\u001b[0m \u001b[38;5;124;03m    Load BLIP2 model with caption_coco_flant5xl architecture.\u001b[39;00m\n\u001b[1;32m    531\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    537\u001b[0m \u001b[38;5;124;03m        txt_processors (dict): preprocessors for text inputs.\u001b[39;00m\n\u001b[1;32m    538\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m    539\u001b[0m     (\n\u001b[1;32m    540\u001b[0m         summary_vqa_model,\n\u001b[1;32m    541\u001b[0m         summary_vqa_vis_processors,\n\u001b[1;32m    542\u001b[0m         summary_vqa_txt_processors,\n\u001b[0;32m--> 543\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[43mload_model_and_preprocess\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    544\u001b[0m \u001b[43m        \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mblip2_t5\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    545\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcaption_coco_flant5xl\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    546\u001b[0m \u001b[43m        \u001b[49m\u001b[43mis_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    547\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msummary_device\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    548\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    549\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m summary_vqa_model, summary_vqa_vis_processors, summary_vqa_txt_processors\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/__init__.py:195\u001b[0m, in \u001b[0;36mload_model_and_preprocess\u001b[0;34m(name, model_type, is_eval, device)\u001b[0m\n\u001b[1;32m    192\u001b[0m model_cls \u001b[38;5;241m=\u001b[39m registry\u001b[38;5;241m.\u001b[39mget_model_class(name)\n\u001b[1;32m    194\u001b[0m \u001b[38;5;66;03m# load model\u001b[39;00m\n\u001b[0;32m--> 195\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_cls\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    197\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_eval:\n\u001b[1;32m    198\u001b[0m     model\u001b[38;5;241m.\u001b[39meval()\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/base_model.py:70\u001b[0m, in \u001b[0;36mBaseModel.from_pretrained\u001b[0;34m(cls, model_type)\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     61\u001b[0m \u001b[38;5;124;03mBuild a pretrained model from default configuration file, specified by model_type.\u001b[39;00m\n\u001b[1;32m     62\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[38;5;124;03m    - model (nn.Module): pretrained or finetuned model, depending on the configuration.\u001b[39;00m\n\u001b[1;32m     68\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     69\u001b[0m model_cfg \u001b[38;5;241m=\u001b[39m OmegaConf\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mdefault_config_path(model_type))\u001b[38;5;241m.\u001b[39mmodel\n\u001b[0;32m---> 70\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_cfg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/blip2_models/blip2_t5.py:368\u001b[0m, in \u001b[0;36mBlip2T5.from_config\u001b[0;34m(cls, cfg)\u001b[0m\n\u001b[1;32m    364\u001b[0m max_txt_len \u001b[38;5;241m=\u001b[39m cfg\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax_txt_len\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m32\u001b[39m)\n\u001b[1;32m    366\u001b[0m apply_lemmatizer \u001b[38;5;241m=\u001b[39m cfg\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply_lemmatizer\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m--> 368\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m    369\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvit_model\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvit_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    370\u001b[0m \u001b[43m    \u001b[49m\u001b[43mimg_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mimg_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    371\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdrop_path_rate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrop_path_rate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    372\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_grad_checkpoint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_grad_checkpoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    373\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvit_precision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvit_precision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    374\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfreeze_vit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfreeze_vit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    375\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnum_query_token\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_query_token\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    376\u001b[0m \u001b[43m    \u001b[49m\u001b[43mt5_model\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mt5_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    377\u001b[0m \u001b[43m    \u001b[49m\u001b[43mprompt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprompt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    378\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_txt_len\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_txt_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    379\u001b[0m \u001b[43m    \u001b[49m\u001b[43mapply_lemmatizer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mapply_lemmatizer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    380\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    381\u001b[0m model\u001b[38;5;241m.\u001b[39mload_checkpoint_from_config(cfg)\n\u001b[1;32m    383\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/blip2_models/blip2_t5.py:61\u001b[0m, in \u001b[0;36mBlip2T5.__init__\u001b[0;34m(self, vit_model, img_size, drop_path_rate, use_grad_checkpoint, vit_precision, freeze_vit, num_query_token, t5_model, prompt, max_txt_len, apply_lemmatizer)\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m()\n\u001b[1;32m     59\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtokenizer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minit_tokenizer()\n\u001b[0;32m---> 61\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvisual_encoder, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mln_vision \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minit_vision_encoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     62\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvit_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mimg_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdrop_path_rate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_grad_checkpoint\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvit_precision\u001b[49m\n\u001b[1;32m     63\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     64\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m freeze_vit:\n\u001b[1;32m     65\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m name, param \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvisual_encoder\u001b[38;5;241m.\u001b[39mnamed_parameters():\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/blip2_models/blip2.py:72\u001b[0m, in \u001b[0;36mBlip2Base.init_vision_encoder\u001b[0;34m(cls, model_name, img_size, drop_path_rate, use_grad_checkpoint, precision)\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m model_name \u001b[38;5;129;01min\u001b[39;00m [\n\u001b[1;32m     68\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meva_clip_g\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclip_L\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     70\u001b[0m ], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvit model must be eva_clip_g or clip_L\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m model_name \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meva_clip_g\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m---> 72\u001b[0m     visual_encoder \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_eva_vit_g\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[43m        \u001b[49m\u001b[43mimg_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdrop_path_rate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_grad_checkpoint\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprecision\u001b[49m\n\u001b[1;32m     74\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     75\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m model_name \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclip_L\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m     76\u001b[0m     visual_encoder \u001b[38;5;241m=\u001b[39m create_clip_vit_L(img_size, use_grad_checkpoint, precision)\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/eva_vit.py:430\u001b[0m, in \u001b[0;36mcreate_eva_vit_g\u001b[0;34m(img_size, drop_path_rate, use_checkpoint, precision)\u001b[0m\n\u001b[1;32m    416\u001b[0m model \u001b[38;5;241m=\u001b[39m VisionTransformer(\n\u001b[1;32m    417\u001b[0m     img_size\u001b[38;5;241m=\u001b[39mimg_size,\n\u001b[1;32m    418\u001b[0m     patch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m14\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    427\u001b[0m     use_checkpoint\u001b[38;5;241m=\u001b[39muse_checkpoint,\n\u001b[1;32m    428\u001b[0m )  \n\u001b[1;32m    429\u001b[0m url \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/eva_vit_g.pth\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 430\u001b[0m cached_file \u001b[38;5;241m=\u001b[39m \u001b[43mdownload_cached_file\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    431\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_hash\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m    432\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    433\u001b[0m state_dict \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mload(cached_file, map_location\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m)    \n\u001b[1;32m    434\u001b[0m interpolate_pos_embed(model,state_dict)\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/common/dist_utils.py:132\u001b[0m, in \u001b[0;36mdownload_cached_file\u001b[0;34m(url, check_hash, progress)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cached_file\n\u001b[1;32m    131\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_main_process():\n\u001b[0;32m--> 132\u001b[0m     \u001b[43mtimm_hub\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_cached_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_hash\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    134\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_dist_avail_and_initialized():\n\u001b[1;32m    135\u001b[0m     dist\u001b[38;5;241m.\u001b[39mbarrier()\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/timm/models/hub.py:51\u001b[0m, in \u001b[0;36mdownload_cached_file\u001b[0;34m(url, check_hash, progress)\u001b[0m\n\u001b[1;32m     49\u001b[0m         r \u001b[38;5;241m=\u001b[39m HASH_REGEX\u001b[38;5;241m.\u001b[39msearch(filename)  \u001b[38;5;66;03m# r is Optional[Match[str]]\u001b[39;00m\n\u001b[1;32m     50\u001b[0m         hash_prefix \u001b[38;5;241m=\u001b[39m r\u001b[38;5;241m.\u001b[39mgroup(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m r \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 51\u001b[0m     \u001b[43mdownload_url_to_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcached_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhash_prefix\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprogress\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cached_file\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/hub.py:636\u001b[0m, in \u001b[0;36mdownload_url_to_file\u001b[0;34m(url, dst, hash_prefix, progress)\u001b[0m\n\u001b[1;32m    634\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(buffer) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    635\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m--> 636\u001b[0m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    637\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m hash_prefix \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    638\u001b[0m     sha256\u001b[38;5;241m.\u001b[39mupdate(buffer)\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/tempfile.py:478\u001b[0m, in \u001b[0;36m_TemporaryFileWrapper.__getattr__.<locals>.func_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    476\u001b[0m \u001b[38;5;129m@_functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m    477\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfunc_wrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 478\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[0;31mOSError\u001b[0m: [Errno 28] No space left on device"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "obj = ammico.SummaryDetector(subdict=image_dict, analysis_type = \"summary_and_questions\", model_type = \"blip2_t5_caption_coco_flant5xl\")\n",
     "# list of the new models that can be used:\n",
@@ -9903,28 +612,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:53:49.445792Z",
-     "iopub.status.busy": "2024-02-19T08:53:49.445585Z",
-     "iopub.status.idle": "2024-02-19T08:53:49.470611Z",
-     "shell.execute_reply": "2024-02-19T08:53:49.470071Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'obj' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[24], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m image_dict:\n\u001b[0;32m----> 2\u001b[0m     image_dict[key] \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241m.\u001b[39manalyse_image(subdict \u001b[38;5;241m=\u001b[39m image_dict[key], analysis_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msummary_and_questions\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# analysis_type can be \u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m# \"summary\",\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m# \"questions\",\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# \"summary_and_questions\".\u001b[39;00m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'obj' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for key in image_dict:\n",
     "    image_dict[key] = obj.analyse_image(subdict = image_dict[key], analysis_type=\"summary_and_questions\")\n",
@@ -9937,171 +627,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:53:49.473138Z",
-     "iopub.status.busy": "2024-02-19T08:53:49.472942Z",
-     "iopub.status.idle": "2024-02-19T08:53:49.480775Z",
-     "shell.execute_reply": "2024-02-19T08:53:49.480208Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'img4': {'filename': 'data-test/img4.png',\n",
-       "  'face': 'No',\n",
-       "  'multiple_faces': 'No',\n",
-       "  'no_faces': 0,\n",
-       "  'wears_mask': ['No'],\n",
-       "  'age': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'emotion': [None],\n",
-       "  'emotion (category)': [None],\n",
-       "  'text': 'MOODOVIN XI',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'MOODOVIN XI',\n",
-       "  'text_clean': 'XI',\n",
-       "  'text_summary': ' MOODOVIN XI XI: Vladimir Putin, Vladimir Vladmir Zelizer, Vladimir',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 0.66,\n",
-       "  'entity': ['MOODOVIN XI'],\n",
-       "  'entity_type': ['ORG'],\n",
-       "  'const_image_summary': 'a river running through a city next to tall buildings',\n",
-       "  '3_non-deterministic_summary': ['there is a pretty house that sits above the water',\n",
-       "   'there is a building with a balcony and lots of plants on the side of it',\n",
-       "   'several buildings with a river flowing through it']},\n",
-       " 'img1': {'filename': 'data-test/img1.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions JOHN R. TAYLOR University of Colorado',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions JOHN R. TAYLOR University of Colorado',\n",
-       "  'text_clean': 'THEORY The Quantum Theory of Collisions JOHN R. TAYLOR University of Colorado',\n",
-       "  'text_summary': ' SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 0.91,\n",
-       "  'entity': ['Non',\n",
-       "   '##vist',\n",
-       "   'Col',\n",
-       "   '##N',\n",
-       "   'R',\n",
-       "   'T',\n",
-       "   '##AYL',\n",
-       "   'University of Colorado'],\n",
-       "  'entity_type': ['MISC', 'MISC', 'MISC', 'ORG', 'PER', 'PER', 'ORG', 'ORG'],\n",
-       "  'const_image_summary': 'a close up of a piece of paper with writing on it',\n",
-       "  '3_non-deterministic_summary': ['a book opened to the book title for a novel',\n",
-       "   'there are many text on this page',\n",
-       "   'the text in a book is a handwritten poem']},\n",
-       " 'img2': {'filename': 'data-test/img2.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J.. H. WILKINSON',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J.. H. WILKINSON',\n",
-       "  'text_clean': 'THE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J .. H. WILKINSON',\n",
-       "  'text_summary': ' H. H. W. WILKINSON: The Algebri',\n",
-       "  'sentiment': 'NEGATIVE',\n",
-       "  'sentiment_score': 0.97,\n",
-       "  'entity': ['ALGEBRAIC EIGENVAL', 'NVS TIO MI', 'J', 'H', 'WILKINSON'],\n",
-       "  'entity_type': ['MISC', 'ORG', 'ORG', 'ORG', 'ORG'],\n",
-       "  'const_image_summary': 'a yellow book with green lettering on it',\n",
-       "  '3_non-deterministic_summary': ['a book cover with green writing on a black background',\n",
-       "   'the title page of a book with information from its authors',\n",
-       "   'a book about the age - related engineering and engineering']},\n",
-       " 'img3': {'filename': 'data-test/img3.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'm OOOO 0000 www.',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'm OOOO 0000 www.',\n",
-       "  'text_clean': 'm www .',\n",
-       "  'text_summary': ' www. m OOOO 0000 0000 www.m.m OOOo 0000',\n",
-       "  'sentiment': 'NEGATIVE',\n",
-       "  'sentiment_score': 0.62,\n",
-       "  'entity': [],\n",
-       "  'entity_type': [],\n",
-       "  'const_image_summary': 'a bus that is sitting on the side of a road',\n",
-       "  '3_non-deterministic_summary': ['there are cars and a bus on the side of the road',\n",
-       "   'a bus that is sitting in the middle of a street',\n",
-       "   'an aerial view of an empty city street with two large buses passing by']},\n",
-       " 'img0': {'filename': 'data-test/img0.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'Mathematische Formelsammlung für Ingenieure und Naturwissenschaftler Mit zahlreichen Abbildungen und Rechenbeispielen und einer ausführlichen Integraltafel 3., verbesserte Auflage',\n",
-       "  'text_language': 'de',\n",
-       "  'text_english': 'Mathematical formula collection for engineers and scientists With numerous illustrations and calculation examples and a detailed integral table 3rd, improved edition',\n",
-       "  'text_clean': 'Mathematical formula collection for engineers and scientists With numerous illustrations and calculation examples and a detailed integral table 3rd , improved edition',\n",
-       "  'text_summary': ' Mathematical formula collection for engineers and scientists . Includes numerous illustrations and calculation examples . Includes',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 1.0,\n",
-       "  'entity': [],\n",
-       "  'entity_type': [],\n",
-       "  'const_image_summary': 'a close up of an open book with writing on it',\n",
-       "  '3_non-deterministic_summary': ['a close up of a book with many languages',\n",
-       "   'a book that is opened up in german',\n",
-       "   'book about mathemarche formulals and their meaning']},\n",
-       " 'img5': {'filename': 'data-test/img5.png',\n",
-       "  'no_faces': 1,\n",
-       "  'age': [26],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': ['Negative'],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': ['sad'],\n",
-       "  'gender': ['Man'],\n",
-       "  'race': [None],\n",
-       "  'face': 'Yes',\n",
-       "  'text': None,\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': '',\n",
-       "  'text_clean': '',\n",
-       "  'text_summary': ' CNN.com will feature iReporter photos in a weekly Travel Snapshots gallery .',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 0.75,\n",
-       "  'entity': [],\n",
-       "  'entity_type': [],\n",
-       "  'const_image_summary': 'a person running on a beach near a rock formation',\n",
-       "  '3_non-deterministic_summary': ['a woman is running down the beach next to some rocks',\n",
-       "   'a woman running along the beach by the ocean',\n",
-       "   'there is a person running on the beach next to the ocean']}}"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "image_dict"
    ]
@@ -10117,15 +645,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:53:49.483664Z",
-     "iopub.status.busy": "2024-02-19T08:53:49.483466Z",
-     "iopub.status.idle": "2024-02-19T08:53:49.486232Z",
-     "shell.execute_reply": "2024-02-19T08:53:49.485670Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "list_of_questions = [\n",
@@ -10136,28 +657,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:53:49.488981Z",
-     "iopub.status.busy": "2024-02-19T08:53:49.488787Z",
-     "iopub.status.idle": "2024-02-19T08:53:49.509942Z",
-     "shell.execute_reply": "2024-02-19T08:53:49.509438Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'obj' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[27], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m image_dict:\n\u001b[0;32m----> 2\u001b[0m     image_dict[key] \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241m.\u001b[39manalyse_image(subdict \u001b[38;5;241m=\u001b[39m image_dict[key], analysis_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquestions\u001b[39m\u001b[38;5;124m\"\u001b[39m, list_of_questions\u001b[38;5;241m=\u001b[39mlist_of_questions)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'obj' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for key in image_dict:\n",
     "    image_dict[key] = obj.analyse_image(subdict = image_dict[key], analysis_type=\"questions\", list_of_questions=list_of_questions)"
@@ -10174,15 +676,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:53:49.513195Z",
-     "iopub.status.busy": "2024-02-19T08:53:49.512999Z",
-     "iopub.status.idle": "2024-02-19T08:53:49.515828Z",
-     "shell.execute_reply": "2024-02-19T08:53:49.515286Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "list_of_questions = [\n",
@@ -10193,28 +688,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:53:49.520071Z",
-     "iopub.status.busy": "2024-02-19T08:53:49.519734Z",
-     "iopub.status.idle": "2024-02-19T08:53:49.540462Z",
-     "shell.execute_reply": "2024-02-19T08:53:49.539940Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'obj' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[29], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m image_dict:\n\u001b[0;32m----> 2\u001b[0m     image_dict[key] \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241m.\u001b[39manalyse_image(subdict \u001b[38;5;241m=\u001b[39m image_dict[key], analysis_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquestions\u001b[39m\u001b[38;5;124m\"\u001b[39m, list_of_questions\u001b[38;5;241m=\u001b[39mlist_of_questions)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'obj' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for key in image_dict:\n",
     "    image_dict[key] = obj.analyse_image(subdict = image_dict[key], analysis_type=\"questions\", list_of_questions=list_of_questions)"
@@ -10222,171 +698,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:53:49.544909Z",
-     "iopub.status.busy": "2024-02-19T08:53:49.544431Z",
-     "iopub.status.idle": "2024-02-19T08:53:49.551819Z",
-     "shell.execute_reply": "2024-02-19T08:53:49.551270Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'img4': {'filename': 'data-test/img4.png',\n",
-       "  'face': 'No',\n",
-       "  'multiple_faces': 'No',\n",
-       "  'no_faces': 0,\n",
-       "  'wears_mask': ['No'],\n",
-       "  'age': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'emotion': [None],\n",
-       "  'emotion (category)': [None],\n",
-       "  'text': 'MOODOVIN XI',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'MOODOVIN XI',\n",
-       "  'text_clean': 'XI',\n",
-       "  'text_summary': ' MOODOVIN XI XI: Vladimir Putin, Vladimir Vladmir Zelizer, Vladimir',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 0.66,\n",
-       "  'entity': ['MOODOVIN XI'],\n",
-       "  'entity_type': ['ORG'],\n",
-       "  'const_image_summary': 'a river running through a city next to tall buildings',\n",
-       "  '3_non-deterministic_summary': ['there is a pretty house that sits above the water',\n",
-       "   'there is a building with a balcony and lots of plants on the side of it',\n",
-       "   'several buildings with a river flowing through it']},\n",
-       " 'img1': {'filename': 'data-test/img1.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions JOHN R. TAYLOR University of Colorado',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions JOHN R. TAYLOR University of Colorado',\n",
-       "  'text_clean': 'THEORY The Quantum Theory of Collisions JOHN R. TAYLOR University of Colorado',\n",
-       "  'text_summary': ' SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 0.91,\n",
-       "  'entity': ['Non',\n",
-       "   '##vist',\n",
-       "   'Col',\n",
-       "   '##N',\n",
-       "   'R',\n",
-       "   'T',\n",
-       "   '##AYL',\n",
-       "   'University of Colorado'],\n",
-       "  'entity_type': ['MISC', 'MISC', 'MISC', 'ORG', 'PER', 'PER', 'ORG', 'ORG'],\n",
-       "  'const_image_summary': 'a close up of a piece of paper with writing on it',\n",
-       "  '3_non-deterministic_summary': ['a book opened to the book title for a novel',\n",
-       "   'there are many text on this page',\n",
-       "   'the text in a book is a handwritten poem']},\n",
-       " 'img2': {'filename': 'data-test/img2.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J.. H. WILKINSON',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J.. H. WILKINSON',\n",
-       "  'text_clean': 'THE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J .. H. WILKINSON',\n",
-       "  'text_summary': ' H. H. W. WILKINSON: The Algebri',\n",
-       "  'sentiment': 'NEGATIVE',\n",
-       "  'sentiment_score': 0.97,\n",
-       "  'entity': ['ALGEBRAIC EIGENVAL', 'NVS TIO MI', 'J', 'H', 'WILKINSON'],\n",
-       "  'entity_type': ['MISC', 'ORG', 'ORG', 'ORG', 'ORG'],\n",
-       "  'const_image_summary': 'a yellow book with green lettering on it',\n",
-       "  '3_non-deterministic_summary': ['a book cover with green writing on a black background',\n",
-       "   'the title page of a book with information from its authors',\n",
-       "   'a book about the age - related engineering and engineering']},\n",
-       " 'img3': {'filename': 'data-test/img3.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'm OOOO 0000 www.',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'm OOOO 0000 www.',\n",
-       "  'text_clean': 'm www .',\n",
-       "  'text_summary': ' www. m OOOO 0000 0000 www.m.m OOOo 0000',\n",
-       "  'sentiment': 'NEGATIVE',\n",
-       "  'sentiment_score': 0.62,\n",
-       "  'entity': [],\n",
-       "  'entity_type': [],\n",
-       "  'const_image_summary': 'a bus that is sitting on the side of a road',\n",
-       "  '3_non-deterministic_summary': ['there are cars and a bus on the side of the road',\n",
-       "   'a bus that is sitting in the middle of a street',\n",
-       "   'an aerial view of an empty city street with two large buses passing by']},\n",
-       " 'img0': {'filename': 'data-test/img0.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'Mathematische Formelsammlung für Ingenieure und Naturwissenschaftler Mit zahlreichen Abbildungen und Rechenbeispielen und einer ausführlichen Integraltafel 3., verbesserte Auflage',\n",
-       "  'text_language': 'de',\n",
-       "  'text_english': 'Mathematical formula collection for engineers and scientists With numerous illustrations and calculation examples and a detailed integral table 3rd, improved edition',\n",
-       "  'text_clean': 'Mathematical formula collection for engineers and scientists With numerous illustrations and calculation examples and a detailed integral table 3rd , improved edition',\n",
-       "  'text_summary': ' Mathematical formula collection for engineers and scientists . Includes numerous illustrations and calculation examples . Includes',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 1.0,\n",
-       "  'entity': [],\n",
-       "  'entity_type': [],\n",
-       "  'const_image_summary': 'a close up of an open book with writing on it',\n",
-       "  '3_non-deterministic_summary': ['a close up of a book with many languages',\n",
-       "   'a book that is opened up in german',\n",
-       "   'book about mathemarche formulals and their meaning']},\n",
-       " 'img5': {'filename': 'data-test/img5.png',\n",
-       "  'no_faces': 1,\n",
-       "  'age': [26],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': ['Negative'],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': ['sad'],\n",
-       "  'gender': ['Man'],\n",
-       "  'race': [None],\n",
-       "  'face': 'Yes',\n",
-       "  'text': None,\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': '',\n",
-       "  'text_clean': '',\n",
-       "  'text_summary': ' CNN.com will feature iReporter photos in a weekly Travel Snapshots gallery .',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 0.75,\n",
-       "  'entity': [],\n",
-       "  'entity_type': [],\n",
-       "  'const_image_summary': 'a person running on a beach near a rock formation',\n",
-       "  '3_non-deterministic_summary': ['a woman is running down the beach next to some rocks',\n",
-       "   'a woman running along the beach by the ocean',\n",
-       "   'there is a person running on the beach next to the ocean']}}"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "image_dict"
    ]
@@ -10400,15 +714,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:53:49.554718Z",
-     "iopub.status.busy": "2024-02-19T08:53:49.554288Z",
-     "iopub.status.idle": "2024-02-19T08:53:49.557147Z",
-     "shell.execute_reply": "2024-02-19T08:53:49.556606Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "list_of_questions = [\n",
@@ -10419,28 +726,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:53:49.559854Z",
-     "iopub.status.busy": "2024-02-19T08:53:49.559416Z",
-     "iopub.status.idle": "2024-02-19T08:53:49.581420Z",
-     "shell.execute_reply": "2024-02-19T08:53:49.580718Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'obj' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[32], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m image_dict:\n\u001b[0;32m----> 2\u001b[0m     image_dict[key] \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241m.\u001b[39manalyse_image(subdict \u001b[38;5;241m=\u001b[39m image_dict[key], analysis_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquestions\u001b[39m\u001b[38;5;124m\"\u001b[39m, list_of_questions\u001b[38;5;241m=\u001b[39mlist_of_questions, consequential_questions\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'obj' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for key in image_dict:\n",
     "    image_dict[key] = obj.analyse_image(subdict = image_dict[key], analysis_type=\"questions\", list_of_questions=list_of_questions, consequential_questions=True)"
@@ -10448,171 +736,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:53:49.584997Z",
-     "iopub.status.busy": "2024-02-19T08:53:49.584485Z",
-     "iopub.status.idle": "2024-02-19T08:53:49.592760Z",
-     "shell.execute_reply": "2024-02-19T08:53:49.592039Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'img4': {'filename': 'data-test/img4.png',\n",
-       "  'face': 'No',\n",
-       "  'multiple_faces': 'No',\n",
-       "  'no_faces': 0,\n",
-       "  'wears_mask': ['No'],\n",
-       "  'age': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'emotion': [None],\n",
-       "  'emotion (category)': [None],\n",
-       "  'text': 'MOODOVIN XI',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'MOODOVIN XI',\n",
-       "  'text_clean': 'XI',\n",
-       "  'text_summary': ' MOODOVIN XI XI: Vladimir Putin, Vladimir Vladmir Zelizer, Vladimir',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 0.66,\n",
-       "  'entity': ['MOODOVIN XI'],\n",
-       "  'entity_type': ['ORG'],\n",
-       "  'const_image_summary': 'a river running through a city next to tall buildings',\n",
-       "  '3_non-deterministic_summary': ['there is a pretty house that sits above the water',\n",
-       "   'there is a building with a balcony and lots of plants on the side of it',\n",
-       "   'several buildings with a river flowing through it']},\n",
-       " 'img1': {'filename': 'data-test/img1.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions JOHN R. TAYLOR University of Colorado',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions JOHN R. TAYLOR University of Colorado',\n",
-       "  'text_clean': 'THEORY The Quantum Theory of Collisions JOHN R. TAYLOR University of Colorado',\n",
-       "  'text_summary': ' SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 0.91,\n",
-       "  'entity': ['Non',\n",
-       "   '##vist',\n",
-       "   'Col',\n",
-       "   '##N',\n",
-       "   'R',\n",
-       "   'T',\n",
-       "   '##AYL',\n",
-       "   'University of Colorado'],\n",
-       "  'entity_type': ['MISC', 'MISC', 'MISC', 'ORG', 'PER', 'PER', 'ORG', 'ORG'],\n",
-       "  'const_image_summary': 'a close up of a piece of paper with writing on it',\n",
-       "  '3_non-deterministic_summary': ['a book opened to the book title for a novel',\n",
-       "   'there are many text on this page',\n",
-       "   'the text in a book is a handwritten poem']},\n",
-       " 'img2': {'filename': 'data-test/img2.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J.. H. WILKINSON',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J.. H. WILKINSON',\n",
-       "  'text_clean': 'THE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J .. H. WILKINSON',\n",
-       "  'text_summary': ' H. H. W. WILKINSON: The Algebri',\n",
-       "  'sentiment': 'NEGATIVE',\n",
-       "  'sentiment_score': 0.97,\n",
-       "  'entity': ['ALGEBRAIC EIGENVAL', 'NVS TIO MI', 'J', 'H', 'WILKINSON'],\n",
-       "  'entity_type': ['MISC', 'ORG', 'ORG', 'ORG', 'ORG'],\n",
-       "  'const_image_summary': 'a yellow book with green lettering on it',\n",
-       "  '3_non-deterministic_summary': ['a book cover with green writing on a black background',\n",
-       "   'the title page of a book with information from its authors',\n",
-       "   'a book about the age - related engineering and engineering']},\n",
-       " 'img3': {'filename': 'data-test/img3.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'm OOOO 0000 www.',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'm OOOO 0000 www.',\n",
-       "  'text_clean': 'm www .',\n",
-       "  'text_summary': ' www. m OOOO 0000 0000 www.m.m OOOo 0000',\n",
-       "  'sentiment': 'NEGATIVE',\n",
-       "  'sentiment_score': 0.62,\n",
-       "  'entity': [],\n",
-       "  'entity_type': [],\n",
-       "  'const_image_summary': 'a bus that is sitting on the side of a road',\n",
-       "  '3_non-deterministic_summary': ['there are cars and a bus on the side of the road',\n",
-       "   'a bus that is sitting in the middle of a street',\n",
-       "   'an aerial view of an empty city street with two large buses passing by']},\n",
-       " 'img0': {'filename': 'data-test/img0.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'Mathematische Formelsammlung für Ingenieure und Naturwissenschaftler Mit zahlreichen Abbildungen und Rechenbeispielen und einer ausführlichen Integraltafel 3., verbesserte Auflage',\n",
-       "  'text_language': 'de',\n",
-       "  'text_english': 'Mathematical formula collection for engineers and scientists With numerous illustrations and calculation examples and a detailed integral table 3rd, improved edition',\n",
-       "  'text_clean': 'Mathematical formula collection for engineers and scientists With numerous illustrations and calculation examples and a detailed integral table 3rd , improved edition',\n",
-       "  'text_summary': ' Mathematical formula collection for engineers and scientists . Includes numerous illustrations and calculation examples . Includes',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 1.0,\n",
-       "  'entity': [],\n",
-       "  'entity_type': [],\n",
-       "  'const_image_summary': 'a close up of an open book with writing on it',\n",
-       "  '3_non-deterministic_summary': ['a close up of a book with many languages',\n",
-       "   'a book that is opened up in german',\n",
-       "   'book about mathemarche formulals and their meaning']},\n",
-       " 'img5': {'filename': 'data-test/img5.png',\n",
-       "  'no_faces': 1,\n",
-       "  'age': [26],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': ['Negative'],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': ['sad'],\n",
-       "  'gender': ['Man'],\n",
-       "  'race': [None],\n",
-       "  'face': 'Yes',\n",
-       "  'text': None,\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': '',\n",
-       "  'text_clean': '',\n",
-       "  'text_summary': ' CNN.com will feature iReporter photos in a weekly Travel Snapshots gallery .',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 0.75,\n",
-       "  'entity': [],\n",
-       "  'entity_type': [],\n",
-       "  'const_image_summary': 'a person running on a beach near a rock formation',\n",
-       "  '3_non-deterministic_summary': ['a woman is running down the beach next to some rocks',\n",
-       "   'a woman running along the beach by the ocean',\n",
-       "   'there is a person running on the beach next to the ocean']}}"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "image_dict"
    ]
@@ -10640,97 +766,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:53:49.596180Z",
-     "iopub.status.busy": "2024-02-19T08:53:49.595807Z",
-     "iopub.status.idle": "2024-02-19T08:54:11.388417Z",
-     "shell.execute_reply": "2024-02-19T08:54:11.387832Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "1/1 [==============================] - ETA: 0s"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "1/1 [==============================] - 1s 535ms/step\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "1/1 [==============================] - ETA: 0s"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "1/1 [==============================] - 0s 343ms/step\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "1/1 [==============================] - ETA: 0s"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "1/1 [==============================] - 0s 226ms/step\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "1/1 [==============================] - ETA: 0s"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "1/1 [==============================] - 0s 233ms/step\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "1/1 [==============================] - ETA: 0s"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for key in image_dict.keys():\n",
     "    image_dict[key] = ammico.EmotionDetector(image_dict[key], emotion_threshold=50, race_threshold=50).analyse_image()"
@@ -10793,15 +831,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:11.393676Z",
-     "iopub.status.busy": "2024-02-19T08:54:11.393305Z",
-     "iopub.status.idle": "2024-02-19T08:54:11.396416Z",
-     "shell.execute_reply": "2024-02-19T08:54:11.395833Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "model_type = \"blip\"\n",
@@ -10821,15 +852,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:11.399454Z",
-     "iopub.status.busy": "2024-02-19T08:54:11.399132Z",
-     "iopub.status.idle": "2024-02-19T08:54:11.402161Z",
-     "shell.execute_reply": "2024-02-19T08:54:11.401648Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "my_obj = ammico.MultimodalSearch(image_dict)"
@@ -10837,597 +861,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:11.404913Z",
-     "iopub.status.busy": "2024-02-19T08:54:11.404591Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.394925Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.394163Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
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-      "\r",
-      "  0%|          | 0.00/1.97G [00:00<?, ?B/s]"
-     ]
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-    {
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-      "\r",
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-      "\n"
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-    {
-     "ename": "OSError",
-     "evalue": "[Errno 28] No space left on device",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[37], line 8\u001b[0m\n\u001b[1;32m      1\u001b[0m (\n\u001b[1;32m      2\u001b[0m     model,\n\u001b[1;32m      3\u001b[0m     vis_processors,\n\u001b[1;32m      4\u001b[0m     txt_processors,\n\u001b[1;32m      5\u001b[0m     image_keys,\n\u001b[1;32m      6\u001b[0m     image_names,\n\u001b[1;32m      7\u001b[0m     features_image_stacked,\n\u001b[0;32m----> 8\u001b[0m ) \u001b[38;5;241m=\u001b[39m \u001b[43mmy_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparsing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath_to_save_tensors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/content/drive/MyDrive/misinformation-data/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     11\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/multimodal_search.py:363\u001b[0m, in \u001b[0;36mMultimodalSearch.parsing_images\u001b[0;34m(self, model_type, path_to_save_tensors, path_to_load_tensors)\u001b[0m\n\u001b[1;32m    349\u001b[0m select_extract_image_features \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    350\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mblip2\u001b[39m\u001b[38;5;124m\"\u001b[39m: MultimodalSearch\u001b[38;5;241m.\u001b[39mextract_image_features_blip2,\n\u001b[1;32m    351\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mblip\u001b[39m\u001b[38;5;124m\"\u001b[39m: MultimodalSearch\u001b[38;5;241m.\u001b[39mextract_image_features_basic,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    355\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclip_vitl14_336\u001b[39m\u001b[38;5;124m\"\u001b[39m: MultimodalSearch\u001b[38;5;241m.\u001b[39mextract_image_features_clip,\n\u001b[1;32m    356\u001b[0m }\n\u001b[1;32m    358\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m model_type \u001b[38;5;129;01min\u001b[39;00m select_model\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[1;32m    359\u001b[0m     (\n\u001b[1;32m    360\u001b[0m         model,\n\u001b[1;32m    361\u001b[0m         vis_processors,\n\u001b[1;32m    362\u001b[0m         txt_processors,\n\u001b[0;32m--> 363\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[43mselect_model\u001b[49m\u001b[43m[\u001b[49m\n\u001b[1;32m    364\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel_type\u001b[49m\n\u001b[1;32m    365\u001b[0m \u001b[43m    \u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mMultimodalSearch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmultimodal_device\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    366\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    367\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mSyntaxError\u001b[39;00m(\n\u001b[1;32m    368\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease, use one of the following models: blip2, blip, albef, clip_base, clip_vitl14, clip_vitl14_336\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    369\u001b[0m     )\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/multimodal_search.py:55\u001b[0m, in \u001b[0;36mMultimodalSearch.load_feature_extractor_model_blip\u001b[0;34m(self, device)\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_feature_extractor_model_blip\u001b[39m(\u001b[38;5;28mself\u001b[39m, device: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m     44\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     45\u001b[0m \u001b[38;5;124;03m    Load base blip_feature_extractor model and preprocessors for visual and text inputs from lavis.models.\u001b[39;00m\n\u001b[1;32m     46\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[38;5;124;03m        txt_processors (dict): preprocessors for text inputs.\u001b[39;00m\n\u001b[1;32m     54\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m---> 55\u001b[0m     model, vis_processors, txt_processors \u001b[38;5;241m=\u001b[39m \u001b[43mload_model_and_preprocess\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     56\u001b[0m \u001b[43m        \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mblip_feature_extractor\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     57\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbase\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     58\u001b[0m \u001b[43m        \u001b[49m\u001b[43mis_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     59\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     60\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     61\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m model, vis_processors, txt_processors\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/__init__.py:195\u001b[0m, in \u001b[0;36mload_model_and_preprocess\u001b[0;34m(name, model_type, is_eval, device)\u001b[0m\n\u001b[1;32m    192\u001b[0m model_cls \u001b[38;5;241m=\u001b[39m registry\u001b[38;5;241m.\u001b[39mget_model_class(name)\n\u001b[1;32m    194\u001b[0m \u001b[38;5;66;03m# load model\u001b[39;00m\n\u001b[0;32m--> 195\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_cls\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    197\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_eval:\n\u001b[1;32m    198\u001b[0m     model\u001b[38;5;241m.\u001b[39meval()\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/base_model.py:70\u001b[0m, in \u001b[0;36mBaseModel.from_pretrained\u001b[0;34m(cls, model_type)\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     61\u001b[0m \u001b[38;5;124;03mBuild a pretrained model from default configuration file, specified by model_type.\u001b[39;00m\n\u001b[1;32m     62\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[38;5;124;03m    - model (nn.Module): pretrained or finetuned model, depending on the configuration.\u001b[39;00m\n\u001b[1;32m     68\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     69\u001b[0m model_cfg \u001b[38;5;241m=\u001b[39m OmegaConf\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mdefault_config_path(model_type))\u001b[38;5;241m.\u001b[39mmodel\n\u001b[0;32m---> 70\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_cfg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/blip_models/blip_feature_extractor.py:208\u001b[0m, in \u001b[0;36mBlipFeatureExtractor.from_config\u001b[0;34m(cls, cfg)\u001b[0m\n\u001b[1;32m    206\u001b[0m pretrain_path \u001b[38;5;241m=\u001b[39m cfg\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpretrained\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    207\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pretrain_path \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 208\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_from_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl_or_filename\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpretrain_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    209\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    210\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo pretrained weights are loaded.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/models/blip_models/blip.py:30\u001b[0m, in \u001b[0;36mBlipBase.load_from_pretrained\u001b[0;34m(self, url_or_filename)\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_from_pretrained\u001b[39m(\u001b[38;5;28mself\u001b[39m, url_or_filename):\n\u001b[1;32m     29\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m is_url(url_or_filename):\n\u001b[0;32m---> 30\u001b[0m         cached_file \u001b[38;5;241m=\u001b[39m \u001b[43mdownload_cached_file\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     31\u001b[0m \u001b[43m            \u001b[49m\u001b[43murl_or_filename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_hash\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m     32\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     33\u001b[0m         checkpoint \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mload(cached_file, map_location\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     34\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misfile(url_or_filename):\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/lavis/common/dist_utils.py:132\u001b[0m, in \u001b[0;36mdownload_cached_file\u001b[0;34m(url, check_hash, progress)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cached_file\n\u001b[1;32m    131\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_main_process():\n\u001b[0;32m--> 132\u001b[0m     \u001b[43mtimm_hub\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_cached_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_hash\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    134\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_dist_avail_and_initialized():\n\u001b[1;32m    135\u001b[0m     dist\u001b[38;5;241m.\u001b[39mbarrier()\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/timm/models/hub.py:51\u001b[0m, in \u001b[0;36mdownload_cached_file\u001b[0;34m(url, check_hash, progress)\u001b[0m\n\u001b[1;32m     49\u001b[0m         r \u001b[38;5;241m=\u001b[39m HASH_REGEX\u001b[38;5;241m.\u001b[39msearch(filename)  \u001b[38;5;66;03m# r is Optional[Match[str]]\u001b[39;00m\n\u001b[1;32m     50\u001b[0m         hash_prefix \u001b[38;5;241m=\u001b[39m r\u001b[38;5;241m.\u001b[39mgroup(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m r \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 51\u001b[0m     \u001b[43mdownload_url_to_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcached_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhash_prefix\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprogress\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cached_file\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/torch/hub.py:636\u001b[0m, in \u001b[0;36mdownload_url_to_file\u001b[0;34m(url, dst, hash_prefix, progress)\u001b[0m\n\u001b[1;32m    634\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(buffer) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    635\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m--> 636\u001b[0m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    637\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m hash_prefix \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    638\u001b[0m     sha256\u001b[38;5;241m.\u001b[39mupdate(buffer)\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/tempfile.py:478\u001b[0m, in \u001b[0;36m_TemporaryFileWrapper.__getattr__.<locals>.func_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    476\u001b[0m \u001b[38;5;129m@_functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m    477\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfunc_wrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 478\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[0;31mOSError\u001b[0m: [Errno 28] No space left on device"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "(\n",
     "    model,\n",
@@ -11453,15 +889,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.399067Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.398613Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.401763Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.401225Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# (\n",
@@ -11495,15 +924,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.406155Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.405642Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.409654Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.408999Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import importlib_resources                                                                       # only require for image query example\n",
@@ -11529,28 +951,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.412858Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.412664Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.436288Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.435775Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'model' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[40], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m similarity, sorted_lists \u001b[38;5;241m=\u001b[39m my_obj\u001b[38;5;241m.\u001b[39mmultimodal_search(\n\u001b[0;32m----> 2\u001b[0m     \u001b[43mmodel\u001b[49m,\n\u001b[1;32m      3\u001b[0m     vis_processors,\n\u001b[1;32m      4\u001b[0m     txt_processors,\n\u001b[1;32m      5\u001b[0m     model_type,\n\u001b[1;32m      6\u001b[0m     image_keys,\n\u001b[1;32m      7\u001b[0m     features_image_stacked,\n\u001b[1;32m      8\u001b[0m     search_query,\n\u001b[1;32m      9\u001b[0m     filter_number_of_images\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m,\n\u001b[1;32m     10\u001b[0m )\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "similarity, sorted_lists = my_obj.multimodal_search(\n",
     "    model,\n",
@@ -11566,56 +969,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.443401Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.442981Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.462491Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.461996Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'similarity' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[41], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msimilarity\u001b[49m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'similarity' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "similarity"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.465916Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.465540Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.484725Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.484175Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'sorted_lists' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[42], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msorted_lists\u001b[49m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'sorted_lists' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "sorted_lists"
    ]
@@ -11629,171 +994,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.487891Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.487505Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.494730Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.494169Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'img4': {'filename': 'data-test/img4.png',\n",
-       "  'face': 'No',\n",
-       "  'multiple_faces': 'No',\n",
-       "  'no_faces': 0,\n",
-       "  'wears_mask': ['No'],\n",
-       "  'age': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'emotion': [None],\n",
-       "  'emotion (category)': [None],\n",
-       "  'text': 'MOODOVIN XI',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'MOODOVIN XI',\n",
-       "  'text_clean': 'XI',\n",
-       "  'text_summary': ' MOODOVIN XI XI: Vladimir Putin, Vladimir Vladmir Zelizer, Vladimir',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 0.66,\n",
-       "  'entity': ['MOODOVIN XI'],\n",
-       "  'entity_type': ['ORG'],\n",
-       "  'const_image_summary': 'a river running through a city next to tall buildings',\n",
-       "  '3_non-deterministic_summary': ['there is a pretty house that sits above the water',\n",
-       "   'there is a building with a balcony and lots of plants on the side of it',\n",
-       "   'several buildings with a river flowing through it']},\n",
-       " 'img1': {'filename': 'data-test/img1.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions JOHN R. TAYLOR University of Colorado',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions JOHN R. TAYLOR University of Colorado',\n",
-       "  'text_clean': 'THEORY The Quantum Theory of Collisions JOHN R. TAYLOR University of Colorado',\n",
-       "  'text_summary': ' SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 0.91,\n",
-       "  'entity': ['Non',\n",
-       "   '##vist',\n",
-       "   'Col',\n",
-       "   '##N',\n",
-       "   'R',\n",
-       "   'T',\n",
-       "   '##AYL',\n",
-       "   'University of Colorado'],\n",
-       "  'entity_type': ['MISC', 'MISC', 'MISC', 'ORG', 'PER', 'PER', 'ORG', 'ORG'],\n",
-       "  'const_image_summary': 'a close up of a piece of paper with writing on it',\n",
-       "  '3_non-deterministic_summary': ['a book opened to the book title for a novel',\n",
-       "   'there are many text on this page',\n",
-       "   'the text in a book is a handwritten poem']},\n",
-       " 'img2': {'filename': 'data-test/img2.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J.. H. WILKINSON',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J.. H. WILKINSON',\n",
-       "  'text_clean': 'THE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J .. H. WILKINSON',\n",
-       "  'text_summary': ' H. H. W. WILKINSON: The Algebri',\n",
-       "  'sentiment': 'NEGATIVE',\n",
-       "  'sentiment_score': 0.97,\n",
-       "  'entity': ['ALGEBRAIC EIGENVAL', 'NVS TIO MI', 'J', 'H', 'WILKINSON'],\n",
-       "  'entity_type': ['MISC', 'ORG', 'ORG', 'ORG', 'ORG'],\n",
-       "  'const_image_summary': 'a yellow book with green lettering on it',\n",
-       "  '3_non-deterministic_summary': ['a book cover with green writing on a black background',\n",
-       "   'the title page of a book with information from its authors',\n",
-       "   'a book about the age - related engineering and engineering']},\n",
-       " 'img3': {'filename': 'data-test/img3.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'm OOOO 0000 www.',\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': 'm OOOO 0000 www.',\n",
-       "  'text_clean': 'm www .',\n",
-       "  'text_summary': ' www. m OOOO 0000 0000 www.m.m OOOo 0000',\n",
-       "  'sentiment': 'NEGATIVE',\n",
-       "  'sentiment_score': 0.62,\n",
-       "  'entity': [],\n",
-       "  'entity_type': [],\n",
-       "  'const_image_summary': 'a bus that is sitting on the side of a road',\n",
-       "  '3_non-deterministic_summary': ['there are cars and a bus on the side of the road',\n",
-       "   'a bus that is sitting in the middle of a street',\n",
-       "   'an aerial view of an empty city street with two large buses passing by']},\n",
-       " 'img0': {'filename': 'data-test/img0.png',\n",
-       "  'no_faces': 0,\n",
-       "  'age': [None],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': [None],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': [None],\n",
-       "  'gender': [None],\n",
-       "  'race': [None],\n",
-       "  'face': 'No',\n",
-       "  'text': 'Mathematische Formelsammlung für Ingenieure und Naturwissenschaftler Mit zahlreichen Abbildungen und Rechenbeispielen und einer ausführlichen Integraltafel 3., verbesserte Auflage',\n",
-       "  'text_language': 'de',\n",
-       "  'text_english': 'Mathematical formula collection for engineers and scientists With numerous illustrations and calculation examples and a detailed integral table 3rd, improved edition',\n",
-       "  'text_clean': 'Mathematical formula collection for engineers and scientists With numerous illustrations and calculation examples and a detailed integral table 3rd , improved edition',\n",
-       "  'text_summary': ' Mathematical formula collection for engineers and scientists . Includes numerous illustrations and calculation examples . Includes',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 1.0,\n",
-       "  'entity': [],\n",
-       "  'entity_type': [],\n",
-       "  'const_image_summary': 'a close up of an open book with writing on it',\n",
-       "  '3_non-deterministic_summary': ['a close up of a book with many languages',\n",
-       "   'a book that is opened up in german',\n",
-       "   'book about mathemarche formulals and their meaning']},\n",
-       " 'img5': {'filename': 'data-test/img5.png',\n",
-       "  'no_faces': 1,\n",
-       "  'age': [26],\n",
-       "  'wears_mask': ['No'],\n",
-       "  'emotion (category)': ['Negative'],\n",
-       "  'multiple_faces': 'No',\n",
-       "  'emotion': ['sad'],\n",
-       "  'gender': ['Man'],\n",
-       "  'race': [None],\n",
-       "  'face': 'Yes',\n",
-       "  'text': None,\n",
-       "  'text_language': 'en',\n",
-       "  'text_english': '',\n",
-       "  'text_clean': '',\n",
-       "  'text_summary': ' CNN.com will feature iReporter photos in a weekly Travel Snapshots gallery .',\n",
-       "  'sentiment': 'POSITIVE',\n",
-       "  'sentiment_score': 0.75,\n",
-       "  'entity': [],\n",
-       "  'entity_type': [],\n",
-       "  'const_image_summary': 'a person running on a beach near a rock formation',\n",
-       "  '3_non-deterministic_summary': ['a woman is running down the beach next to some rocks',\n",
-       "   'a woman running along the beach by the ocean',\n",
-       "   'there is a person running on the beach next to the ocean']}}"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "image_dict"
    ]
@@ -11807,57 +1010,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.498308Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.498116Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.561629Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.560724Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Your search query: politician press conference'"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'--------------------------------------------------'"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'Results:'"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "ename": "KeyError",
-     "evalue": "'politician press conference'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[44], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmy_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow_results\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[43msearch_query\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# you can change the index to see the results for other queries\u001b[39;49;00m\n\u001b[1;32m      3\u001b[0m \u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/multimodal_search.py:970\u001b[0m, in \u001b[0;36mMultimodalSearch.show_results\u001b[0;34m(self, query, itm, image_gradcam_with_itm)\u001b[0m\n\u001b[1;32m    967\u001b[0m     current_querry_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(query\u001b[38;5;241m.\u001b[39mvalues())[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    968\u001b[0m     current_querry_rank \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrank \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mlist\u001b[39m(query\u001b[38;5;241m.\u001b[39mvalues())[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m--> 970\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28;43msorted\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m    971\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubdict\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcurrent_querry_val\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreverse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m    972\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m    973\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m s[\u001b[38;5;241m1\u001b[39m][current_querry_rank] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    974\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/multimodal_search.py:971\u001b[0m, in \u001b[0;36mMultimodalSearch.show_results.<locals>.<lambda>\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m    967\u001b[0m     current_querry_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(query\u001b[38;5;241m.\u001b[39mvalues())[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    968\u001b[0m     current_querry_rank \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrank \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mlist\u001b[39m(query\u001b[38;5;241m.\u001b[39mvalues())[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    970\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28msorted\u001b[39m(\n\u001b[0;32m--> 971\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubdict\u001b[38;5;241m.\u001b[39mitems(), key\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m t: \u001b[43mt\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcurrent_querry_val\u001b[49m\u001b[43m]\u001b[49m, reverse\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    972\u001b[0m ):\n\u001b[1;32m    973\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m s[\u001b[38;5;241m1\u001b[39m][current_querry_rank] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    974\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'politician press conference'"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "my_obj.show_results(\n",
     "    search_query[0], # you can change the index to see the results for other queries\n",
@@ -11866,68 +1021,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.564539Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.564124Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.742817Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.742251Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Your search query: '"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/jpeg": 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",
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",
-      "text/plain": [
-       "<PIL.Image.Image image mode=RGB size=400x185>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'--------------------------------------------------'"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'Results:'"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "ename": "KeyError",
-     "evalue": "'/home/runner/work/AMMICO/AMMICO/ammico/data/test-crop-image.png'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[45], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmy_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow_results\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[43msearch_query\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# you can change the index to see the results for other queries\u001b[39;49;00m\n\u001b[1;32m      3\u001b[0m \u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/multimodal_search.py:970\u001b[0m, in \u001b[0;36mMultimodalSearch.show_results\u001b[0;34m(self, query, itm, image_gradcam_with_itm)\u001b[0m\n\u001b[1;32m    967\u001b[0m     current_querry_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(query\u001b[38;5;241m.\u001b[39mvalues())[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    968\u001b[0m     current_querry_rank \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrank \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mlist\u001b[39m(query\u001b[38;5;241m.\u001b[39mvalues())[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m--> 970\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28;43msorted\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m    971\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubdict\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcurrent_querry_val\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreverse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m    972\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m    973\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m s[\u001b[38;5;241m1\u001b[39m][current_querry_rank] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    974\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/multimodal_search.py:971\u001b[0m, in \u001b[0;36mMultimodalSearch.show_results.<locals>.<lambda>\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m    967\u001b[0m     current_querry_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(query\u001b[38;5;241m.\u001b[39mvalues())[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    968\u001b[0m     current_querry_rank \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrank \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mlist\u001b[39m(query\u001b[38;5;241m.\u001b[39mvalues())[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    970\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28msorted\u001b[39m(\n\u001b[0;32m--> 971\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubdict\u001b[38;5;241m.\u001b[39mitems(), key\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m t: \u001b[43mt\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcurrent_querry_val\u001b[49m\u001b[43m]\u001b[49m, reverse\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    972\u001b[0m ):\n\u001b[1;32m    973\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m s[\u001b[38;5;241m1\u001b[39m][current_querry_rank] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    974\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
-      "\u001b[0;31mKeyError\u001b[0m: '/home/runner/work/AMMICO/AMMICO/ammico/data/test-crop-image.png'"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "my_obj.show_results(\n",
     "    search_query[3], # you can change the index to see the results for other queries\n",
@@ -11945,15 +1041,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.746808Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.746601Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.749457Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.748884Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "itm_model = \"blip_base\"\n",
@@ -11963,28 +1052,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.752555Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.752084Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.773035Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.772413Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'image_keys' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[47], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m itm_scores, image_gradcam_with_itm \u001b[38;5;241m=\u001b[39m my_obj\u001b[38;5;241m.\u001b[39mimage_text_match_reordering(\n\u001b[1;32m      2\u001b[0m     search_query,\n\u001b[1;32m      3\u001b[0m     itm_model,\n\u001b[0;32m----> 4\u001b[0m     \u001b[43mimage_keys\u001b[49m,\n\u001b[1;32m      5\u001b[0m     sorted_lists,\n\u001b[1;32m      6\u001b[0m     batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m      7\u001b[0m     need_grad_cam\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m      8\u001b[0m )\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'image_keys' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "itm_scores, image_gradcam_with_itm = my_obj.image_text_match_reordering(\n",
     "    search_query,\n",
@@ -12005,28 +1075,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.775851Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.775512Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.795328Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.794723Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'image_gradcam_with_itm' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[48], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m my_obj\u001b[38;5;241m.\u001b[39mshow_results(\n\u001b[0;32m----> 2\u001b[0m     search_query[\u001b[38;5;241m0\u001b[39m], itm\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, image_gradcam_with_itm\u001b[38;5;241m=\u001b[39m\u001b[43mimage_gradcam_with_itm\u001b[49m\n\u001b[1;32m      3\u001b[0m )\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'image_gradcam_with_itm' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "my_obj.show_results(\n",
     "    search_query[0], itm=True, image_gradcam_with_itm=image_gradcam_with_itm\n",
@@ -12049,28 +1100,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.799498Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.799139Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.818722Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.818206Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "module 'ammico' has no attribute 'append_data_to_dict'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[49], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m outdict \u001b[38;5;241m=\u001b[39m \u001b[43mammico\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mappend_data_to_dict\u001b[49m(image_dict)\n\u001b[1;32m      2\u001b[0m df \u001b[38;5;241m=\u001b[39m ammico\u001b[38;5;241m.\u001b[39mdump_df(outdict)\n",
-      "\u001b[0;31mAttributeError\u001b[0m: module 'ammico' has no attribute 'append_data_to_dict'"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "outdict = ammico.append_data_to_dict(image_dict)\n",
     "df = ammico.dump_df(outdict)"
@@ -12085,28 +1117,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.822275Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.821902Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.841826Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.841214Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'df' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[50], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m10\u001b[39m)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df.head(10)"
    ]
@@ -12120,28 +1133,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.845662Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.845309Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.864880Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.864321Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'df' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[51], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241m.\u001b[39mto_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/content/drive/MyDrive/misinformation-data/data_out.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df.to_csv(\"/content/drive/MyDrive/misinformation-data/data_out.csv\")"
    ]
@@ -12166,38 +1160,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.868479Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.868084Z",
-     "iopub.status.idle": "2024-02-19T08:54:26.911851Z",
-     "shell.execute_reply": "2024-02-19T08:54:26.911132Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"100%\"\n",
-       "            height=\"650\"\n",
-       "            src=\"http://127.0.0.1:8057/\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x7f9791095040>"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "analysis_explorer = ammico.AnalysisExplorer(image_dict)\n",
     "analysis_explorer.run_server(port = 8057)"
@@ -12212,15 +1177,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:26.917049Z",
-     "iopub.status.busy": "2024-02-19T08:54:26.916702Z",
-     "iopub.status.idle": "2024-02-19T08:54:38.080384Z",
-     "shell.execute_reply": "2024-02-19T08:54:38.079754Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "for key in image_dict.keys():\n",
@@ -12236,15 +1194,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:38.085405Z",
-     "iopub.status.busy": "2024-02-19T08:54:38.084973Z",
-     "iopub.status.idle": "2024-02-19T08:54:38.089091Z",
-     "shell.execute_reply": "2024-02-19T08:54:38.088517Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "df = ammico.get_dataframe(image_dict)"
@@ -12259,243 +1210,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:38.097290Z",
-     "iopub.status.busy": "2024-02-19T08:54:38.096874Z",
-     "iopub.status.idle": "2024-02-19T08:54:38.119692Z",
-     "shell.execute_reply": "2024-02-19T08:54:38.119109Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
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-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
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-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>data-test/img4.png</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>0</td>\n",
-       "      <td>[No]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.16</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.10</td>\n",
-       "      <td>0.42</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>0.21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>data-test/img1.png</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>0</td>\n",
-       "      <td>[No]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.96</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>data-test/img2.png</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>0</td>\n",
-       "      <td>[No]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>0.15</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>data-test/img3.png</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>0</td>\n",
-       "      <td>[No]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.06</td>\n",
-       "      <td>0.92</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>data-test/img0.png</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>0</td>\n",
-       "      <td>[No]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.98</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>data-test/img5.png</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>1</td>\n",
-       "      <td>[No]</td>\n",
-       "      <td>[26]</td>\n",
-       "      <td>[Man]</td>\n",
-       "      <td>[None]</td>\n",
-       "      <td>[sad]</td>\n",
-       "      <td>[Negative]</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.12</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>6 rows × 33 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             filename face multiple_faces  no_faces wears_mask     age  \\\n",
-       "0  data-test/img4.png   No             No         0       [No]  [None]   \n",
-       "1  data-test/img1.png   No             No         0       [No]  [None]   \n",
-       "2  data-test/img2.png   No             No         0       [No]  [None]   \n",
-       "3  data-test/img3.png   No             No         0       [No]  [None]   \n",
-       "4  data-test/img0.png   No             No         0       [No]  [None]   \n",
-       "5  data-test/img5.png  Yes             No         1       [No]    [26]   \n",
-       "\n",
-       "   gender    race emotion emotion (category)  ...  blue yellow cyan orange  \\\n",
-       "0  [None]  [None]  [None]             [None]  ...  0.16   0.00    0      0   \n",
-       "1  [None]  [None]  [None]             [None]  ...  0.00   0.00    0      0   \n",
-       "2  [None]  [None]  [None]             [None]  ...  0.00   0.75    0      0   \n",
-       "3  [None]  [None]  [None]             [None]  ...  0.00   0.00    0      0   \n",
-       "4  [None]  [None]  [None]             [None]  ...  0.00   0.00    0      0   \n",
-       "5   [Man]  [None]   [sad]         [Negative]  ...  0.12   0.00    0      0   \n",
-       "\n",
-       "  purple pink  brown  grey white black  \n",
-       "0   0.00    0   0.10  0.42  0.05  0.21  \n",
-       "1   0.00    0   0.00  0.96  0.00  0.04  \n",
-       "2   0.00    0   0.04  0.15  0.00  0.02  \n",
-       "3   0.02    0   0.06  0.92  0.01  0.00  \n",
-       "4   0.00    0   0.00  0.98  0.00  0.02  \n",
-       "5   0.00    0   0.02  0.50  0.00  0.00  \n",
-       "\n",
-       "[6 rows x 33 columns]"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df.head(10)"
    ]
@@ -12509,34 +1226,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:54:38.124073Z",
-     "iopub.status.busy": "2024-02-19T08:54:38.123661Z",
-     "iopub.status.idle": "2024-02-19T08:54:38.203962Z",
-     "shell.execute_reply": "2024-02-19T08:54:38.203276Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "OSError",
-     "evalue": "Cannot save file into a non-existent directory: '/content/drive/MyDrive/misinformation-data'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[56], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/content/drive/MyDrive/misinformation-data/data_out.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/util/_decorators.py:333\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    327\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m    328\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    329\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m    330\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    331\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m    332\u001b[0m     )\n\u001b[0;32m--> 333\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/core/generic.py:3961\u001b[0m, in \u001b[0;36mNDFrame.to_csv\u001b[0;34m(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, lineterminator, chunksize, date_format, doublequote, escapechar, decimal, errors, storage_options)\u001b[0m\n\u001b[1;32m   3950\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m, ABCDataFrame) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mto_frame()\n\u001b[1;32m   3952\u001b[0m formatter \u001b[38;5;241m=\u001b[39m DataFrameFormatter(\n\u001b[1;32m   3953\u001b[0m     frame\u001b[38;5;241m=\u001b[39mdf,\n\u001b[1;32m   3954\u001b[0m     header\u001b[38;5;241m=\u001b[39mheader,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3958\u001b[0m     decimal\u001b[38;5;241m=\u001b[39mdecimal,\n\u001b[1;32m   3959\u001b[0m )\n\u001b[0;32m-> 3961\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mDataFrameRenderer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mformatter\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_csv\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3962\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath_or_buf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3963\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlineterminator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlineterminator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3964\u001b[0m \u001b[43m    \u001b[49m\u001b[43msep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msep\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3965\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3966\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3967\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3968\u001b[0m \u001b[43m    \u001b[49m\u001b[43mquoting\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquoting\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3969\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3970\u001b[0m \u001b[43m    \u001b[49m\u001b[43mindex_label\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex_label\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3971\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3972\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunksize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunksize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3973\u001b[0m \u001b[43m    \u001b[49m\u001b[43mquotechar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquotechar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3974\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdate_format\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdate_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3975\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdoublequote\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdoublequote\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3976\u001b[0m \u001b[43m    \u001b[49m\u001b[43mescapechar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mescapechar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3977\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3978\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/io/formats/format.py:1014\u001b[0m, in \u001b[0;36mDataFrameRenderer.to_csv\u001b[0;34m(self, path_or_buf, encoding, sep, columns, index_label, mode, compression, quoting, quotechar, lineterminator, chunksize, date_format, doublequote, escapechar, errors, storage_options)\u001b[0m\n\u001b[1;32m    993\u001b[0m     created_buffer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    995\u001b[0m csv_formatter \u001b[38;5;241m=\u001b[39m CSVFormatter(\n\u001b[1;32m    996\u001b[0m     path_or_buf\u001b[38;5;241m=\u001b[39mpath_or_buf,\n\u001b[1;32m    997\u001b[0m     lineterminator\u001b[38;5;241m=\u001b[39mlineterminator,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1012\u001b[0m     formatter\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfmt,\n\u001b[1;32m   1013\u001b[0m )\n\u001b[0;32m-> 1014\u001b[0m \u001b[43mcsv_formatter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1016\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m created_buffer:\n\u001b[1;32m   1017\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(path_or_buf, StringIO)\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/io/formats/csvs.py:251\u001b[0m, in \u001b[0;36mCSVFormatter.save\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    247\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    248\u001b[0m \u001b[38;5;124;03mCreate the writer & save.\u001b[39;00m\n\u001b[1;32m    249\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    250\u001b[0m \u001b[38;5;66;03m# apply compression and byte/text conversion\u001b[39;00m\n\u001b[0;32m--> 251\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    252\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    253\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    254\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    255\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    256\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    257\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    258\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m handles:\n\u001b[1;32m    259\u001b[0m     \u001b[38;5;66;03m# Note: self.encoding is irrelevant here\u001b[39;00m\n\u001b[1;32m    260\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwriter \u001b[38;5;241m=\u001b[39m csvlib\u001b[38;5;241m.\u001b[39mwriter(\n\u001b[1;32m    261\u001b[0m         handles\u001b[38;5;241m.\u001b[39mhandle,\n\u001b[1;32m    262\u001b[0m         lineterminator\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlineterminator,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    267\u001b[0m         quotechar\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mquotechar,\n\u001b[1;32m    268\u001b[0m     )\n\u001b[1;32m    270\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_save()\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/io/common.py:749\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    747\u001b[0m \u001b[38;5;66;03m# Only for write methods\u001b[39;00m\n\u001b[1;32m    748\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode \u001b[38;5;129;01mand\u001b[39;00m is_path:\n\u001b[0;32m--> 749\u001b[0m     \u001b[43mcheck_parent_directory\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    751\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m compression:\n\u001b[1;32m    752\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m compression \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mzstd\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    753\u001b[0m         \u001b[38;5;66;03m# compression libraries do not like an explicit text-mode\u001b[39;00m\n",
-      "File \u001b[0;32m/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/pandas/io/common.py:616\u001b[0m, in \u001b[0;36mcheck_parent_directory\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m    614\u001b[0m parent \u001b[38;5;241m=\u001b[39m Path(path)\u001b[38;5;241m.\u001b[39mparent\n\u001b[1;32m    615\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m parent\u001b[38;5;241m.\u001b[39mis_dir():\n\u001b[0;32m--> 616\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(\u001b[38;5;124mrf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot save file into a non-existent directory: \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparent\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "\u001b[0;31mOSError\u001b[0m: Cannot save file into a non-existent directory: '/content/drive/MyDrive/misinformation-data'"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df.to_csv(\"/content/drive/MyDrive/misinformation-data/data_out.csv\")"
    ]
@@ -12571,7 +1263,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,
diff --git a/build/html/notebooks/Example cropposts.html b/build/html/notebooks/Example cropposts.html
index 0b9bf5e..ed3c417 100644
--- a/build/html/notebooks/Example cropposts.html	
+++ b/build/html/notebooks/Example cropposts.html	
@@ -90,7 +90,7 @@
 <h1>Crop posts module<a class="headerlink" href="#Crop-posts-module" title="Link to this heading"></a></h1>
 <p>Crop posts from social media posts images, to keep import text informations from social media posts images. We can set some manually cropped views from social media posts as reference for cropping the same type social media posts images.</p>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Please ignore this cell: extra install steps that are only executed when running the notebook on Google Colab</span>
@@ -117,7 +117,7 @@
 </div>
 </div>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">ammico.cropposts</span> <span class="k">as</span> <span class="nn">crpo</span>
@@ -130,8 +130,8 @@
 </div>
 </div>
 <p>The cropping is carried out by finding reference images on the image to be cropped. If a reference matches a region on the image, then everything below the matched region is removed. Manually look at a reference and an example post with the code below.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># load ref view for cropping the same type social media posts images.</span>
@@ -152,23 +152,9 @@
 </pre></div>
 </div>
 </div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_Example_cropposts_5_0.png" src="../_images/notebooks_Example_cropposts_5_0.png" />
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_Example_cropposts_5_1.png" src="../_images/notebooks_Example_cropposts_5_1.png" />
-</div>
-</div>
 <p>You can now crop the image and check on the way that everything looks fine. <code class="docutils literal notranslate"><span class="pre">plt_match</span></code> will plot the matches on the image and below which line content will be cropped; <code class="docutils literal notranslate"><span class="pre">plt_crop</span></code> will plot the cropped text part of the social media post with the comments removed; <code class="docutils literal notranslate"><span class="pre">plt_image</span></code> will plot the image part of the social media post if applicable.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># crop a posts from reference view, check the cropping</span>
@@ -180,38 +166,10 @@
 </pre></div>
 </div>
 </div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_Example_cropposts_7_0.png" src="../_images/notebooks_Example_cropposts_7_0.png" />
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_Example_cropposts_7_1.png" src="../_images/notebooks_Example_cropposts_7_1.png" />
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_Example_cropposts_7_2.png" src="../_images/notebooks_Example_cropposts_7_2.png" />
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_Example_cropposts_7_3.png" src="../_images/notebooks_Example_cropposts_7_3.png" />
-</div>
-</div>
 <p>Batch crop images from the image folder given in <code class="docutils literal notranslate"><span class="pre">crop_dir</span></code>. The cropped images will save in <code class="docutils literal notranslate"><span class="pre">save_crop_dir</span></code> folder with the same file name as the original file. The reference images with the items to match are provided in <code class="docutils literal notranslate"><span class="pre">ref_dir</span></code>.</p>
 <p>Sometimes the cropping will be imperfect, due to improper matches on the image. It is sometimes easier to first categorize the social media posts and then set different references in the reference folder <code class="docutils literal notranslate"><span class="pre">ref_dir</span></code>.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><br/><span></span><span class="n">crop_dir</span> <span class="o">=</span> <span class="s2">&quot;data/&quot;</span>
@@ -226,30 +184,6 @@
 </pre></div>
 </div>
 </div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
-<span class="ansi-red-fg">FileNotFoundError</span>                         Traceback (most recent call last)
-Cell <span class="ansi-green-fg">In[5], line 5</span>
-<span class="ansi-green-intense-fg ansi-bold">      2</span> ref_dir <span style="color: rgb(98,98,98)">=</span> pkg <span style="color: rgb(98,98,98)">/</span> <span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">data</span><span style="color: rgb(175,0,0)">&#34;</span> <span style="color: rgb(98,98,98)">/</span> <span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">ref</span><span style="color: rgb(175,0,0)">&#34;</span>
-<span class="ansi-green-intense-fg ansi-bold">      3</span> save_crop_dir <span style="color: rgb(98,98,98)">=</span> <span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">data/crop/</span><span style="color: rgb(175,0,0)">&#34;</span>
-<span class="ansi-green-fg">----&gt; 5</span> files <span style="color: rgb(98,98,98)">=</span> <span class="ansi-yellow-bg">utils</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">.</span><span class="ansi-yellow-bg">find_files</span><span class="ansi-yellow-bg">(</span><span class="ansi-yellow-bg">path</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg">crop_dir</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg">limit</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">=</span><span class="ansi-yellow-bg" style="color: rgb(98,98,98)">10</span><span class="ansi-yellow-bg">,</span><span class="ansi-yellow-bg">)</span>
-<span class="ansi-green-intense-fg ansi-bold">      6</span> ref_files <span style="color: rgb(98,98,98)">=</span> utils<span style="color: rgb(98,98,98)">.</span>find_files(path<span style="color: rgb(98,98,98)">=</span>ref_dir<span style="color: rgb(98,98,98)">.</span>as_posix(), limit<span style="color: rgb(98,98,98)">=</span><span style="color: rgb(98,98,98)">100</span>)
-<span class="ansi-green-intense-fg ansi-bold">      8</span> crpo<span style="color: rgb(98,98,98)">.</span>crop_media_posts(files, ref_files, save_crop_dir, plt_match<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">True</span>, plt_crop<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">False</span>, plt_image<span style="color: rgb(98,98,98)">=</span><span class="ansi-bold" style="color: rgb(0,135,0)">False</span>)
-
-File <span class="ansi-green-fg">~/work/AMMICO/AMMICO/ammico/utils.py:134</span>, in <span class="ansi-cyan-fg">find_files</span><span class="ansi-blue-fg">(path, pattern, recursive, limit, random_seed)</span>
-<span class="ansi-green-intense-fg ansi-bold">    131</span>     results<span style="color: rgb(98,98,98)">.</span>extend(_match_pattern(path, p, recursive<span style="color: rgb(98,98,98)">=</span>recursive))
-<span class="ansi-green-intense-fg ansi-bold">    133</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> <span style="color: rgb(0,135,0)">len</span>(results) <span style="color: rgb(98,98,98)">==</span> <span style="color: rgb(98,98,98)">0</span>:
-<span class="ansi-green-fg">--&gt; 134</span>     <span class="ansi-bold" style="color: rgb(0,135,0)">raise</span> <span class="ansi-bold" style="color: rgb(215,95,95)">FileNotFoundError</span>(<span style="color: rgb(175,0,0)">f</span><span style="color: rgb(175,0,0)">&#34;</span><span style="color: rgb(175,0,0)">No files found in </span><span class="ansi-bold" style="color: rgb(175,95,135)">{</span>path<span class="ansi-bold" style="color: rgb(175,95,135)">}</span><span style="color: rgb(175,0,0)"> with pattern </span><span style="color: rgb(175,0,0)">&#39;</span><span class="ansi-bold" style="color: rgb(175,95,135)">{</span>pattern<span class="ansi-bold" style="color: rgb(175,95,135)">}</span><span style="color: rgb(175,0,0)">&#39;</span><span style="color: rgb(175,0,0)">&#34;</span>)
-<span class="ansi-green-intense-fg ansi-bold">    136</span> <span class="ansi-bold" style="color: rgb(0,135,0)">if</span> random_seed <span class="ansi-bold" style="color: rgb(175,0,255)">is</span> <span class="ansi-bold" style="color: rgb(175,0,255)">not</span> <span class="ansi-bold" style="color: rgb(0,135,0)">None</span>:
-<span class="ansi-green-intense-fg ansi-bold">    137</span>     random<span style="color: rgb(98,98,98)">.</span>seed(random_seed)
-
-<span class="ansi-red-fg">FileNotFoundError</span>: No files found in data/ with pattern &#39;[&#39;png&#39;, &#39;jpg&#39;, &#39;jpeg&#39;, &#39;gif&#39;, &#39;webp&#39;, &#39;avif&#39;, &#39;tiff&#39;]&#39;
-</pre></div></div>
-</div>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
diff --git a/build/html/notebooks/Example cropposts.ipynb b/build/html/notebooks/Example cropposts.ipynb
index 0fb807c..d7c2bcb 100644
--- a/build/html/notebooks/Example cropposts.ipynb	
+++ b/build/html/notebooks/Example cropposts.ipynb	
@@ -21,16 +21,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "2",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:55:18.705057Z",
-     "iopub.status.busy": "2024-02-19T08:55:18.704857Z",
-     "iopub.status.idle": "2024-02-19T08:55:18.719904Z",
-     "shell.execute_reply": "2024-02-19T08:55:18.719429Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Please ignore this cell: extra install steps that are only executed when running the notebook on Google Colab\n",
@@ -57,16 +50,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "3",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:55:18.722234Z",
-     "iopub.status.busy": "2024-02-19T08:55:18.721861Z",
-     "iopub.status.idle": "2024-02-19T08:55:34.897136Z",
-     "shell.execute_reply": "2024-02-19T08:55:34.896567Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import ammico.cropposts as crpo\n",
@@ -88,38 +74,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:55:34.900259Z",
-     "iopub.status.busy": "2024-02-19T08:55:34.899574Z",
-     "iopub.status.idle": "2024-02-19T08:55:35.590003Z",
-     "shell.execute_reply": "2024-02-19T08:55:35.589334Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# load ref view for cropping the same type social media posts images.\n",
     "# substitute the below paths for your samples\n",
@@ -149,58 +107,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "7",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:55:35.594186Z",
-     "iopub.status.busy": "2024-02-19T08:55:35.593795Z",
-     "iopub.status.idle": "2024-02-19T08:55:38.382987Z",
-     "shell.execute_reply": "2024-02-19T08:55:38.382273Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# crop a posts from reference view, check the cropping \n",
     "# this will only plot something if the reference is found on the image\n",
@@ -223,30 +133,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "9",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-02-19T08:55:38.385523Z",
-     "iopub.status.busy": "2024-02-19T08:55:38.385315Z",
-     "iopub.status.idle": "2024-02-19T08:55:39.135269Z",
-     "shell.execute_reply": "2024-02-19T08:55:39.134594Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "No files found in data/ with pattern '['png', 'jpg', 'jpeg', 'gif', 'webp', 'avif', 'tiff']'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[5], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m ref_dir \u001b[38;5;241m=\u001b[39m pkg \u001b[38;5;241m/\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mref\u001b[39m\u001b[38;5;124m\"\u001b[39m \n\u001b[1;32m      3\u001b[0m save_crop_dir \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata/crop/\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 5\u001b[0m files \u001b[38;5;241m=\u001b[39m \u001b[43mutils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfind_files\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcrop_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43mlimit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      6\u001b[0m ref_files \u001b[38;5;241m=\u001b[39m utils\u001b[38;5;241m.\u001b[39mfind_files(path\u001b[38;5;241m=\u001b[39mref_dir\u001b[38;5;241m.\u001b[39mas_posix(), limit\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m100\u001b[39m)\n\u001b[1;32m      8\u001b[0m crpo\u001b[38;5;241m.\u001b[39mcrop_media_posts(files, ref_files, save_crop_dir, plt_match\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, plt_crop\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, plt_image\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
-      "File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/utils.py:134\u001b[0m, in \u001b[0;36mfind_files\u001b[0;34m(path, pattern, recursive, limit, random_seed)\u001b[0m\n\u001b[1;32m    131\u001b[0m     results\u001b[38;5;241m.\u001b[39mextend(_match_pattern(path, p, recursive\u001b[38;5;241m=\u001b[39mrecursive))\n\u001b[1;32m    133\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(results) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 134\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo files found in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m with pattern \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpattern\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    136\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m random_seed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    137\u001b[0m     random\u001b[38;5;241m.\u001b[39mseed(random_seed)\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: No files found in data/ with pattern '['png', 'jpg', 'jpeg', 'gif', 'webp', 'avif', 'tiff']'"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "\n",
     "crop_dir = \"data/\"\n",
@@ -285,7 +175,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.9.16"
   }
  },
  "nbformat": 4,
diff --git a/build/html/searchindex.js b/build/html/searchindex.js
index 3f71dc3..4eaf4cc 100644
--- a/build/html/searchindex.js
+++ b/build/html/searchindex.js
@@ -1 +1 @@
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First, install tensorflow (https://www.tensorflow.org/install/pip)": [[7, "first-install-tensorflow-https-www-tensorflow-org-install-pip"]], "2. Second, install pytorch": [[7, "second-install-pytorch"]], "3. After we prepared right environment we can install the ammico package": [[7, "after-we-prepared-right-environment-we-can-install-the-ammico-package"]], "Micromamba": [[7, "micromamba"]], "Windows": [[7, "windows"]], "Usage": [[7, "usage"]], "Features": [[7, "features"]], "Text extraction": [[7, "text-extraction"]], "Content extraction": [[7, "content-extraction"]], "Emotion recognition": [[7, "emotion-recognition"]], "Color/hue detection": [[7, "color-hue-detection"]], "Cropping of posts": [[7, "cropping-of-posts"]], "FAQ": [[7, "faq"]], "What happens to the images that are sent to google Cloud Vision?": [[7, "what-happens-to-the-images-that-are-sent-to-google-cloud-vision"]], "What happens to the text that is sent to google Translate?": [[7, "what-happens-to-the-text-that-is-sent-to-google-translate"]], "What happens if I don\u2019t have internet access - can I still use ammico?": [[7, "what-happens-if-i-don-t-have-internet-access-can-i-still-use-ammico"]]}, "indexentries": {"analysisexplorer (class in display)": [[0, "display.AnalysisExplorer"]], "analysismethod (class in utils)": [[0, "utils.AnalysisMethod"]], "colordetector (class in colors)": [[0, "colors.ColorDetector"]], "downloadresource (class in utils)": [[0, "utils.DownloadResource"]], "emotiondetector (class in faces)": [[0, "faces.EmotionDetector"]], "multimodalsearch (class in multimodal_search)": [[0, "multimodal_search.MultimodalSearch"]], "postprocesstext (class in text)": [[0, "text.PostprocessText"]], "summarydetector (class in summary)": [[0, "summary.SummaryDetector"]], "textdetector (class in text)": [[0, "text.TextDetector"]], "all_allowed_model_types (summary.summarydetector attribute)": [[0, "summary.SummaryDetector.all_allowed_model_types"]], "allowed_analysis_types (summary.summarydetector attribute)": [[0, "summary.SummaryDetector.allowed_analysis_types"]], "allowed_model_types (summary.summarydetector attribute)": [[0, "summary.SummaryDetector.allowed_model_types"]], "allowed_new_model_types (summary.summarydetector attribute)": [[0, "summary.SummaryDetector.allowed_new_model_types"]], "ammico_prefetch_models() (in module utils)": [[0, "utils.ammico_prefetch_models"]], "analyse_image() (colors.colordetector method)": [[0, "colors.ColorDetector.analyse_image"]], "analyse_image() (faces.emotiondetector method)": [[0, "faces.EmotionDetector.analyse_image"]], "analyse_image() (summary.summarydetector method)": [[0, "summary.SummaryDetector.analyse_image"]], "analyse_image() (text.textdetector method)": [[0, "text.TextDetector.analyse_image"]], "analyse_image() (utils.analysismethod method)": [[0, "utils.AnalysisMethod.analyse_image"]], "analyse_questions() (summary.summarydetector method)": [[0, "summary.SummaryDetector.analyse_questions"]], "analyse_summary() (summary.summarydetector method)": [[0, "summary.SummaryDetector.analyse_summary"]], "analyse_topic() (text.postprocesstext method)": [[0, "text.PostprocessText.analyse_topic"]], "analyze_single_face() (faces.emotiondetector method)": [[0, "faces.EmotionDetector.analyze_single_face"]], "append_data_to_dict() (in module utils)": [[0, "utils.append_data_to_dict"]], "check_for_missing_keys() (in module utils)": [[0, "utils.check_for_missing_keys"]], "check_model() (summary.summarydetector method)": [[0, "summary.SummaryDetector.check_model"]], "clean_subdict() (faces.emotiondetector method)": [[0, "faces.EmotionDetector.clean_subdict"]], "clean_text() (text.textdetector method)": [[0, "text.TextDetector.clean_text"]], "colors": [[0, "module-colors"]], "compute_crop_corner() (in module cropposts)": [[0, "cropposts.compute_crop_corner"]], "compute_gradcam_batch() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.compute_gradcam_batch"]], "crop_image_from_post() (in module cropposts)": [[0, "cropposts.crop_image_from_post"]], "crop_media_posts() (in module cropposts)": [[0, "cropposts.crop_media_posts"]], "crop_posts_from_refs() (in module cropposts)": [[0, "cropposts.crop_posts_from_refs"]], "crop_posts_image() (in module cropposts)": [[0, "cropposts.crop_posts_image"]], "cropposts": [[0, "module-cropposts"]], "deepface_symlink_processor() (in module faces)": [[0, "faces.deepface_symlink_processor"]], "display": [[0, "module-display"]], "draw_matches() (in module cropposts)": [[0, "cropposts.draw_matches"]], "dump_df() (in module utils)": [[0, "utils.dump_df"]], "extract_image_features_basic() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.extract_image_features_basic"]], "extract_image_features_blip2() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.extract_image_features_blip2"]], "extract_image_features_clip() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.extract_image_features_clip"]], "extract_text_features() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.extract_text_features"]], "faces": [[0, "module-faces"]], "facial_expression_analysis() (faces.emotiondetector method)": [[0, "faces.EmotionDetector.facial_expression_analysis"]], "find_files() (in module utils)": [[0, "utils.find_files"]], "get() (utils.downloadresource method)": [[0, "utils.DownloadResource.get"]], "get_att_map() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.get_att_map"]], "get_color_table() (in module utils)": [[0, "utils.get_color_table"]], "get_dataframe() (in module utils)": [[0, "utils.get_dataframe"]], "get_pathes_from_query() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.get_pathes_from_query"]], "get_text_df() (text.postprocesstext method)": [[0, "text.PostprocessText.get_text_df"]], "get_text_dict() (text.postprocesstext method)": [[0, "text.PostprocessText.get_text_dict"]], "get_text_from_image() (text.textdetector method)": [[0, "text.TextDetector.get_text_from_image"]], "image_text_match_reordering() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.image_text_match_reordering"]], "initialize_dict() (in module utils)": [[0, "utils.initialize_dict"]], "is_interactive() (in module utils)": [[0, "utils.is_interactive"]], "iterable() (in module utils)": [[0, "utils.iterable"]], "itm_text_precessing() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.itm_text_precessing"]], "kp_from_matches() (in module cropposts)": [[0, "cropposts.kp_from_matches"]], "load_feature_extractor_model_albef() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.load_feature_extractor_model_albef"]], "load_feature_extractor_model_blip() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.load_feature_extractor_model_blip"]], "load_feature_extractor_model_blip2() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.load_feature_extractor_model_blip2"]], "load_feature_extractor_model_clip_base() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.load_feature_extractor_model_clip_base"]], "load_feature_extractor_model_clip_vitl14() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.load_feature_extractor_model_clip_vitl14"]], "load_feature_extractor_model_clip_vitl14_336() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.load_feature_extractor_model_clip_vitl14_336"]], "load_model() (summary.summarydetector method)": [[0, "summary.SummaryDetector.load_model"]], "load_model_base() (summary.summarydetector method)": [[0, "summary.SummaryDetector.load_model_base"]], "load_model_base_blip2_opt_caption_coco_opt67b() (summary.summarydetector method)": [[0, "summary.SummaryDetector.load_model_base_blip2_opt_caption_coco_opt67b"]], "load_model_base_blip2_opt_pretrain_opt67b() (summary.summarydetector method)": [[0, "summary.SummaryDetector.load_model_base_blip2_opt_pretrain_opt67b"]], "load_model_blip2_opt_caption_coco_opt27b() (summary.summarydetector method)": [[0, "summary.SummaryDetector.load_model_blip2_opt_caption_coco_opt27b"]], "load_model_blip2_opt_pretrain_opt27b() (summary.summarydetector method)": [[0, "summary.SummaryDetector.load_model_blip2_opt_pretrain_opt27b"]], "load_model_blip2_t5_caption_coco_flant5xl() (summary.summarydetector method)": [[0, "summary.SummaryDetector.load_model_blip2_t5_caption_coco_flant5xl"]], "load_model_blip2_t5_pretrain_flant5xl() (summary.summarydetector method)": [[0, "summary.SummaryDetector.load_model_blip2_t5_pretrain_flant5xl"]], "load_model_blip2_t5_pretrain_flant5xxl() (summary.summarydetector method)": [[0, "summary.SummaryDetector.load_model_blip2_t5_pretrain_flant5xxl"]], "load_model_large() (summary.summarydetector method)": [[0, "summary.SummaryDetector.load_model_large"]], "load_new_model() (summary.summarydetector method)": [[0, "summary.SummaryDetector.load_new_model"]], "load_tensors() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.load_tensors"]], "load_vqa_model() (summary.summarydetector method)": [[0, "summary.SummaryDetector.load_vqa_model"]], "matching_points() (in module cropposts)": [[0, "cropposts.matching_points"]], "module": [[0, "module-colors"], [0, "module-cropposts"], [0, "module-display"], [0, "module-faces"], [0, "module-multimodal_search"], [0, "module-summary"], [0, "module-text"], [0, "module-utils"]], "multimodal_device (multimodal_search.multimodalsearch attribute)": [[0, "multimodal_search.MultimodalSearch.multimodal_device"]], "multimodal_search": [[0, "module-multimodal_search"]], "multimodal_search() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.multimodal_search"]], "parsing_images() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.parsing_images"]], "paste_image_and_comment() (in module cropposts)": [[0, "cropposts.paste_image_and_comment"]], "querys_processing() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.querys_processing"]], "read_and_process_images() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.read_and_process_images"]], "read_and_process_images_itm() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.read_and_process_images_itm"]], "read_img() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.read_img"]], "remove_linebreaks() (text.textdetector method)": [[0, "text.TextDetector.remove_linebreaks"]], "resize_img() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.resize_img"]], "resources (utils.downloadresource attribute)": [[0, "utils.DownloadResource.resources"]], "rgb2name() (colors.colordetector method)": [[0, "colors.ColorDetector.rgb2name"]], "run_server() (display.analysisexplorer method)": [[0, "display.AnalysisExplorer.run_server"]], "save_tensors() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.save_tensors"]], "set_keys() (colors.colordetector method)": [[0, "colors.ColorDetector.set_keys"]], "set_keys() (faces.emotiondetector method)": [[0, "faces.EmotionDetector.set_keys"]], "set_keys() (text.textdetector method)": [[0, "text.TextDetector.set_keys"]], "set_keys() (utils.analysismethod method)": [[0, "utils.AnalysisMethod.set_keys"]], "show_results() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.show_results"]], "summary": [[0, "module-summary"]], "text": [[0, "module-text"]], "text_ner() (text.textdetector method)": [[0, "text.TextDetector.text_ner"]], "text_sentiment_transformers() (text.textdetector method)": [[0, "text.TextDetector.text_sentiment_transformers"]], "text_summary() (text.textdetector method)": [[0, "text.TextDetector.text_summary"]], "translate_text() (text.textdetector method)": [[0, "text.TextDetector.translate_text"]], "update_picture() (display.analysisexplorer method)": [[0, "display.AnalysisExplorer.update_picture"]], "upload_model_blip2_coco() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.upload_model_blip2_coco"]], "upload_model_blip_base() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.upload_model_blip_base"]], "upload_model_blip_large() (multimodal_search.multimodalsearch method)": [[0, "multimodal_search.MultimodalSearch.upload_model_blip_large"]], "utils": [[0, "module-utils"]], "wears_mask() (faces.emotiondetector method)": [[0, "faces.EmotionDetector.wears_mask"]]}})
\ No newline at end of file
diff --git a/source/conf.py b/source/conf.py
index cb42285..047d47b 100644
--- a/source/conf.py
+++ b/source/conf.py
@@ -22,6 +22,7 @@ release = "0.0.1"
 
 extensions = ["sphinx.ext.autodoc", "sphinx.ext.napoleon", "myst_parser", "nbsphinx"]
 nbsphinx_allow_errors = True
+nbsphinx_execute = "never"
 napoleon_custom_sections = [("Returns", "params_style")]
 myst_heading_anchors = 3
 
diff --git a/source/notebooks/data-test/img0.png b/source/notebooks/data-test/img0.png
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diff --git a/source/notebooks/data-test/img1.png b/source/notebooks/data-test/img1.png
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diff --git a/source/notebooks/data-test/img2.png b/source/notebooks/data-test/img2.png
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diff --git a/source/notebooks/data-test/img3.png b/source/notebooks/data-test/img3.png
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index 1715d99..0000000
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diff --git a/source/notebooks/data-test/img4.png b/source/notebooks/data-test/img4.png
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diff --git a/source/notebooks/data-test/img5.png b/source/notebooks/data-test/img5.png
deleted file mode 100644
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diff --git a/source/notebooks/dump_file.csv b/source/notebooks/dump_file.csv
deleted file mode 100644
index fddf7cf..0000000
--- a/source/notebooks/dump_file.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-,filename,face,multiple_faces,no_faces,wears_mask,age,gender,race,emotion,emotion (category),text,text_language,text_english,text_clean,text_summary,sentiment,sentiment_score,entity,entity_type,const_image_summary,3_non-deterministic_summary
-0,data-test/img4.png,No,No,0,['No'],[None],[None],[None],[None],[None],MOODOVIN XI,en,MOODOVIN XI,XI," MOODOVIN XI XI: Vladimir Putin, Vladimir Vladmir Zelizer, Vladimir",POSITIVE,0.66,['MOODOVIN XI'],['ORG'],a river running through a city next to tall buildings,"['there is a pretty house that sits above the water', 'there is a building with a balcony and lots of plants on the side of it', 'several buildings with a river flowing through it']"
-1,data-test/img1.png,No,No,0,['No'],[None],[None],[None],[None],[None],SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions JOHN R. TAYLOR University of Colorado,en,SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions JOHN R. TAYLOR University of Colorado,THEORY The Quantum Theory of Collisions JOHN R. TAYLOR University of Colorado, SCATTERING THEORY The Quantum Theory of Nonrelativistic Collisions,POSITIVE,0.91,"['Non', '##vist', 'Col', '##N', 'R', 'T', '##AYL', 'University of Colorado']","['MISC', 'MISC', 'MISC', 'ORG', 'PER', 'PER', 'ORG', 'ORG']",a close up of a piece of paper with writing on it,"['a book opened to the book title for a novel', 'there are many text on this page', 'the text in a book is a handwritten poem']"
-2,data-test/img2.png,No,No,0,['No'],[None],[None],[None],[None],[None],THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J.. H. WILKINSON,en,THE ALGEBRAIC EIGENVALUE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J.. H. WILKINSON,THE PROBLEM DOM NVS TIO MINA Monographs on Numerical Analysis J .. H. WILKINSON, H. H. W. WILKINSON: The Algebri,NEGATIVE,0.97,"['ALGEBRAIC EIGENVAL', 'NVS TIO MI', 'J', 'H', 'WILKINSON']","['MISC', 'ORG', 'ORG', 'ORG', 'ORG']",a yellow book with green lettering on it,"['a book cover with green writing on a black background', 'the title page of a book with information from its authors', 'a book about the age - related engineering and engineering']"
-3,data-test/img3.png,No,No,0,['No'],[None],[None],[None],[None],[None],m OOOO 0000 www.,en,m OOOO 0000 www.,m www ., www. m OOOO 0000 0000 www.m.m OOOo 0000,NEGATIVE,0.62,[],[],a bus that is sitting on the side of a road,"['there are cars and a bus on the side of the road', 'a bus that is sitting in the middle of a street', 'an aerial view of an empty city street with two large buses passing by']"
-4,data-test/img0.png,No,No,0,['No'],[None],[None],[None],[None],[None],"Mathematische Formelsammlung für Ingenieure und Naturwissenschaftler Mit zahlreichen Abbildungen und Rechenbeispielen und einer ausführlichen Integraltafel 3., verbesserte Auflage",de,"Mathematical formula collection for engineers and scientists With numerous illustrations and calculation examples and a detailed integral table 3rd, improved edition","Mathematical formula collection for engineers and scientists With numerous illustrations and calculation examples and a detailed integral table 3rd , improved edition", Mathematical formula collection for engineers and scientists . Includes numerous illustrations and calculation examples . Includes,POSITIVE,1.0,[],[],a close up of an open book with writing on it,"['a close up of a book with many languages', 'a book that is opened up in german', 'book about mathemarche formulals and their meaning']"
-5,data-test/img5.png,Yes,No,1,['No'],[26],['Man'],[None],['sad'],['Negative'],,en,,, CNN.com will feature iReporter photos in a weekly Travel Snapshots gallery .,POSITIVE,0.75,[],[],a person running on a beach near a rock formation,"['a woman is running down the beach next to some rocks', 'a woman running along the beach by the ocean', 'there is a person running on the beach next to the ocean']"