{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image summary and visual question answering"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebooks shows how to generate image captions and use the visual question answering with [LAVIS](https://github.com/salesforce/LAVIS). \n",
    "\n",
    "The first cell is only run on google colab and installs the [ammico](https://github.com/ssciwr/AMMICO) package.\n",
    "\n",
    "After that, we can import `ammico` and read in the files given a folder path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T08:44:59.708373Z",
     "iopub.status.busy": "2023-06-20T08:44:59.708128Z",
     "iopub.status.idle": "2023-06-20T08:44:59.718031Z",
     "shell.execute_reply": "2023-06-20T08:44:59.717064Z"
    }
   },
   "outputs": [],
   "source": [
    "# if running on google colab\n",
    "# flake8-noqa-cell\n",
    "import os\n",
    "\n",
    "if \"google.colab\" in str(get_ipython()):\n",
    "    # update python version\n",
    "    # install setuptools\n",
    "    # %pip install setuptools==61 -qqq\n",
    "    # install ammico\n",
    "    %pip install git+https://github.com/ssciwr/ammico.git -qqq\n",
    "    # mount google drive for data and API key\n",
    "    from google.colab import drive\n",
    "\n",
    "    drive.mount(\"/content/drive\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T08:44:59.721104Z",
     "iopub.status.busy": "2023-06-20T08:44:59.720822Z",
     "iopub.status.idle": "2023-06-20T08:45:12.218123Z",
     "shell.execute_reply": "2023-06-20T08:45:12.216914Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import ammico\n",
    "from ammico import utils as mutils\n",
    "from ammico import display as mdisplay\n",
    "import ammico.summary as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T08:45:12.222093Z",
     "iopub.status.busy": "2023-06-20T08:45:12.221453Z",
     "iopub.status.idle": "2023-06-20T08:45:12.226574Z",
     "shell.execute_reply": "2023-06-20T08:45:12.225675Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Here you need to provide the path to your google drive folder\n",
    "# or local folder containing the images\n",
    "images = mutils.find_files(\n",
    "    path=\"data/\",\n",
    "    limit=10,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T08:45:12.229742Z",
     "iopub.status.busy": "2023-06-20T08:45:12.229513Z",
     "iopub.status.idle": "2023-06-20T08:45:12.232932Z",
     "shell.execute_reply": "2023-06-20T08:45:12.232167Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "mydict = mutils.initialize_dict(images)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create captions for images and directly write to csv"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you can choose between two models: \"base\" or \"large\". This will generate the caption for each image and directly put the results in a dataframe. This dataframe can be exported as a csv file.\n",
    "\n",
    "The results are written into the columns `const_image_summary` - this will always be the same result (as always the same seed will be used). The column `3_non-deterministic summary` displays three different answers generated with different seeds, these are most likely different when you run the analysis again."
   ]
  },
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
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    },
    {
     "name": "stderr",
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     "text": [
      "\r",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "obj = sm.SummaryDetector(mydict)\n",
    "summary_model, summary_vis_processors = obj.load_model(model_type=\"base\")\n",
    "# summary_model, summary_vis_processors = mutils.load_model(\"large\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T08:45:45.364080Z",
     "iopub.status.busy": "2023-06-20T08:45:45.363825Z",
     "iopub.status.idle": "2023-06-20T08:46:50.742198Z",
     "shell.execute_reply": "2023-06-20T08:46:50.741315Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for key in mydict:\n",
    "    mydict[key] = sm.SummaryDetector(mydict[key]).analyse_image(\n",
    "        summary_model=summary_model, summary_vis_processors=summary_vis_processors\n",
    "    )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "Convert the dictionary of dictionarys into a dictionary with lists:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T08:46:50.747212Z",
     "iopub.status.busy": "2023-06-20T08:46:50.745710Z",
     "iopub.status.idle": "2023-06-20T08:46:50.751911Z",
     "shell.execute_reply": "2023-06-20T08:46:50.750988Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "outdict = mutils.append_data_to_dict(mydict)\n",
    "df = mutils.dump_df(outdict)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T08:46:50.755756Z",
     "iopub.status.busy": "2023-06-20T08:46:50.755299Z",
     "iopub.status.idle": "2023-06-20T08:46:50.770444Z",
     "shell.execute_reply": "2023-06-20T08:46:50.769573Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>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/102730_eng.png</td>\n",
       "      <td>two people in blue coats spray disinfection a van</td>\n",
       "      <td>[two people in scrub suits spraying pest from ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>data/106349S_por.png</td>\n",
       "      <td>a man wearing a face mask while looking at a c...</td>\n",
       "      <td>[a television showing a news reporter wearing ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>data/102141_2_eng.png</td>\n",
       "      <td>a collage of images including a corona sign, a...</td>\n",
       "      <td>[someone is holding a tube of corona - drug on...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                filename                                const_image_summary  \\\n",
       "0    data/102730_eng.png  two people in blue coats spray disinfection a van   \n",
       "1   data/106349S_por.png  a man wearing a face mask while looking at a c...   \n",
       "2  data/102141_2_eng.png  a collage of images including a corona sign, a...   \n",
       "\n",
       "                         3_non-deterministic summary  \n",
       "0  [two people in scrub suits spraying pest from ...  \n",
       "1  [a television showing a news reporter wearing ...  \n",
       "2  [someone is holding a tube of corona - drug on...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write the csv file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T08:46:50.773620Z",
     "iopub.status.busy": "2023-06-20T08:46:50.773293Z",
     "iopub.status.idle": "2023-06-20T08:46:50.779682Z",
     "shell.execute_reply": "2023-06-20T08:46:50.778901Z"
    }
   },
   "outputs": [],
   "source": [
    "df.to_csv(\"data_out.csv\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manually inspect the summaries\n",
    "\n",
    "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.\n",
    "\n",
    "`const_image_summary` - the permanent summarys, which does not change from run to run (analyse_image).\n",
    "\n",
    "`3_non-deterministic summary` - 3 different summarys examples that change from run to run (analyse_image). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T08:46:50.782581Z",
     "iopub.status.busy": "2023-06-20T08:46:50.782307Z",
     "iopub.status.idle": "2023-06-20T08:46:50.837370Z",
     "shell.execute_reply": "2023-06-20T08:46:50.836413Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dash is running on http://127.0.0.1:8055/\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:dash.dash:Dash is running on http://127.0.0.1:8055/\n",
      "\n"
     ]
    },
    {
     "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 0x7f855565ffa0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analysis_explorer = mdisplay.AnalysisExplorer(mydict, identify=\"summary\")\n",
    "analysis_explorer.run_server(port=8055)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate answers to free-form questions about images written in natural language. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the list of questions as a list of strings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T08:46:50.842602Z",
     "iopub.status.busy": "2023-06-20T08:46:50.842352Z",
     "iopub.status.idle": "2023-06-20T08:46:50.847647Z",
     "shell.execute_reply": "2023-06-20T08:46:50.846740Z"
    }
   },
   "outputs": [],
   "source": [
    "list_of_questions = [\n",
    "    \"How many persons on the picture?\",\n",
    "    \"Are there any politicians in the picture?\",\n",
    "    \"Does the picture show something from medicine?\",\n",
    "]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explore the analysis using the interface:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T08:46:50.851026Z",
     "iopub.status.busy": "2023-06-20T08:46:50.850744Z",
     "iopub.status.idle": "2023-06-20T08:46:51.363458Z",
     "shell.execute_reply": "2023-06-20T08:46:51.362671Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dash is running on http://127.0.0.1:8055/\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:dash.dash:Dash is running on http://127.0.0.1:8055/\n",
      "\n"
     ]
    },
    {
     "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 0x7f8555240f40>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analysis_explorer = mdisplay.AnalysisExplorer(mydict, identify=\"summary\")\n",
    "analysis_explorer.run_server(port=8055)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Or directly analyze for further processing\n",
    "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T08:46:51.366922Z",
     "iopub.status.busy": "2023-06-20T08:46:51.366640Z",
     "iopub.status.idle": "2023-06-20T08:47:58.631689Z",
     "shell.execute_reply": "2023-06-20T08:47:58.630665Z"
    }
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      " 92%|█████████▏| 1.24G/1.35G [00:08<00:00, 162MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 93%|█████████▎| 1.26G/1.35G [00:08<00:00, 161MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 95%|█████████▍| 1.27G/1.35G [00:08<00:00, 157MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 96%|█████████▌| 1.29G/1.35G [00:08<00:00, 157MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 97%|█████████▋| 1.30G/1.35G [00:08<00:00, 156MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 98%|█████████▊| 1.32G/1.35G [00:08<00:00, 159MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 99%|█████████▉| 1.33G/1.35G [00:08<00:00, 159MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "100%|██████████| 1.35G/1.35G [00:08<00:00, 165MB/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for key in mydict:\n",
    "    mydict[key] = sm.SummaryDetector(mydict[key]).analyse_questions(list_of_questions)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert to dataframe and write csv\n",
    "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T08:47:58.643550Z",
     "iopub.status.busy": "2023-06-20T08:47:58.643276Z",
     "iopub.status.idle": "2023-06-20T08:47:58.651207Z",
     "shell.execute_reply": "2023-06-20T08:47:58.650223Z"
    }
   },
   "outputs": [],
   "source": [
    "outdict2 = mutils.append_data_to_dict(mydict)\n",
    "df2 = mutils.dump_df(outdict2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T08:47:58.654160Z",
     "iopub.status.busy": "2023-06-20T08:47:58.653922Z",
     "iopub.status.idle": "2023-06-20T08:47:58.670333Z",
     "shell.execute_reply": "2023-06-20T08:47:58.669465Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>const_image_summary</th>\n",
       "      <th>3_non-deterministic summary</th>\n",
       "      <th>How many persons on the picture?</th>\n",
       "      <th>Are there any politicians in the picture?</th>\n",
       "      <th>Does the picture show something from medicine?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>data/102730_eng.png</td>\n",
       "      <td>two people in blue coats spray disinfection a van</td>\n",
       "      <td>[two people in scrub suits spraying pest from ...</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>data/106349S_por.png</td>\n",
       "      <td>a man wearing a face mask while looking at a c...</td>\n",
       "      <td>[a television showing a news reporter wearing ...</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>data/102141_2_eng.png</td>\n",
       "      <td>a collage of images including a corona sign, a...</td>\n",
       "      <td>[someone is holding a tube of corona - drug on...</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                filename                                const_image_summary  \\\n",
       "0    data/102730_eng.png  two people in blue coats spray disinfection a van   \n",
       "1   data/106349S_por.png  a man wearing a face mask while looking at a c...   \n",
       "2  data/102141_2_eng.png  a collage of images including a corona sign, a...   \n",
       "\n",
       "                         3_non-deterministic summary  \\\n",
       "0  [two people in scrub suits spraying pest from ...   \n",
       "1  [a television showing a news reporter wearing ...   \n",
       "2  [someone is holding a tube of corona - drug on...   \n",
       "\n",
       "  How many persons on the picture? Are there any politicians in the picture?  \\\n",
       "0                                2                                        no   \n",
       "1                                1                                       yes   \n",
       "2                                1                                        no   \n",
       "\n",
       "  Does the picture show something from medicine?  \n",
       "0                                            yes  \n",
       "1                                            yes  \n",
       "2                                            yes  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-20T08:47:58.675533Z",
     "iopub.status.busy": "2023-06-20T08:47:58.675043Z",
     "iopub.status.idle": "2023-06-20T08:47:58.681854Z",
     "shell.execute_reply": "2023-06-20T08:47:58.681024Z"
    }
   },
   "outputs": [],
   "source": [
    "df2.to_csv(\"data_out2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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