diff --git a/ammico/faces.py b/ammico/faces.py
index 3730cbd..debbfa6 100644
--- a/ammico/faces.py
+++ b/ammico/faces.py
@@ -275,7 +275,6 @@ class EmotionDetector(AnalysisMethod):
         # one dictionary per face that is detected in the image
         # since we are only passing a subregion of the image
         # that contains one face, the list will only contain one dict
-        print("actions are:", self.actions)
         if self.actions != []:
             fresult["result"] = DeepFace.analyze(
                 img_path=face,
diff --git a/ammico/notebooks/DemoNotebook_ammico.ipynb b/ammico/notebooks/DemoNotebook_ammico.ipynb
index 6c3d355..9d9642d 100644
--- a/ammico/notebooks/DemoNotebook_ammico.ipynb
+++ b/ammico/notebooks/DemoNotebook_ammico.ipynb
@@ -276,8 +276,7 @@
     "# dump file name\n",
     "dump_file = \"dump_file.csv\"\n",
     "# dump every N images\n",
-    "dump_every = 10\n",
-    "print(len(image_dict))"
+    "dump_every = 10"
    ]
   },
   {
@@ -395,8 +394,15 @@
     "model = ammico.MultimodalSummaryModel()\n",
     "image_summary_detector = ammico.ImageSummaryDetector(\n",
     "    subdict=image_dict, summary_model=model\n",
-    ")\n",
-    "\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "for num, key in tqdm(\n",
     "    enumerate(image_dict.keys()), total=len(image_dict)\n",
     "):  # loop through all images\n",
@@ -470,7 +476,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "image_df.to_csv(\"/content/drive/MyDrive/misinformation-data/data_out.csv\")"
+    "image_df.to_csv(\"data_out.csv\")"
    ]
   },
   {
@@ -489,7 +495,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ta = ammico.TextAnalyzer(csv_path=\"../data/ref/test.csv\", column_key=\"text\")"
+    "ta = ammico.TextAnalyzer(csv_path=\"test.csv\", column_key=\"text\")"
    ]
   },
   {
@@ -589,7 +595,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = \"/content/drive/MyDrive/misinformation-data/misinformation-campaign-981aa55a3b13.json\"\n"
+    "# os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = \"<path_to_your_service_account_key>.json\"\n"
    ]
   },
   {
@@ -612,13 +618,6 @@
     "):  # loop through all images\n",
     "    image_dict[key] = ammico.TextDetector(\n",
     "        image_dict[key],  # analyse image with TextDetector and update dict\n",
-    "        analyse_text=True,\n",
-    "        model_names=[\n",
-    "            \"sshleifer/distilbart-cnn-12-6\",\n",
-    "            \"distilbert-base-uncased-finetuned-sst-2-english\",\n",
-    "            \"dbmdz/bert-large-cased-finetuned-conll03-english\",\n",
-    "        ],\n",
-    "        revision_numbers=[\"a4f8f3e\", \"af0f99b\", \"f2482bf\"],\n",
     "    ).analyse_image()\n",
     "\n",
     "    if (\n",
@@ -636,7 +635,7 @@
    "source": [
     "# write output to csv\n",
     "image_df = ammico.get_dataframe(image_dict)\n",
-    "image_df.to_csv(\"/content/drive/MyDrive/misinformation-data/data_out.csv\")"
+    "image_df.to_csv(\"data_out.csv\")"
    ]
   },
   {
@@ -1035,7 +1034,7 @@
    "source": [
     "# write output to csv\n",
     "image_df = ammico.get_dataframe(image_dict)\n",
-    "image_df.to_csv(\"/content/drive/MyDrive/misinformation-data/data_out.csv\")"
+    "image_df.to_csv(\"data_out.csv\")"
    ]
   },
   {
@@ -1445,24 +1444,6 @@
     "The output are N primary colors and their corresponding percentage."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "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.\n",
-    "Here, we display the color detection results provided by `colorgram` and `colour` libraries. Click on the tabs to see the results in the right sidebar. You may need to increment the `port` number if you are already running several notebook instances on the same server."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "analysis_explorer = ammico.AnalysisExplorer(image_dict)\n",
-    "analysis_explorer.run_server(port=8057)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1525,7 +1506,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df.to_csv(\"/content/drive/MyDrive/misinformation-data/data_out.csv\")"
+    "df.to_csv(\"data_out.csv\")"
    ]
   },
   {
@@ -1533,7 +1514,7 @@
    "metadata": {},
    "source": [
     "## Further detector modules\n",
-    "Further detector modules exist, also it is possible to carry out a topic analysis on the text data, as well as crop social media posts automatically. These are more experimental features and have their own demonstration notebooks."
+    "Please get in touch or open an issue, if you would like to propose further detector modules."
    ]
   },
   {