remove object_detector (#157)

* `Object_Detector` has been removed  from the project

---------

Co-authored-by: Inga Ulusoy <inga.ulusoy@uni-heidelberg.de>

.github/workflows/ci.yml

@@ -36,10 +36,10 @@ jobs:
       run: |
         cd ammico
         python -m pytest test/test_cropposts.py -svv --cov=. --cov-report=xml --cov-append
-    # - name: Run pytest test_display
+    - name: Run pytest test_display
-    #   run: |
+      run: |
-    #     cd ammico
+        cd ammico
-    #     python -m pytest test/test_display.py -svv --cov=. --cov-report=xml --cov-append
+        python -m pytest test/test_display.py -svv --cov=. --cov-report=xml --cov-append
     - name: Run pytest test_faces
       run: |
         cd ammico
@@ -48,11 +48,7 @@ jobs:
       run: |
         cd ammico
         python -m pytest test/test_multimodal_search.py -m "not long" -svv --cov=. --cov-report=xml --cov-append
-    # - name: Run pytest test_objects
+    - name: Clear cache ubuntu 1
-    #   run: |
-    #     cd ammico
-    #     python -m pytest test/test_objects.py -svv --cov=. --cov-report=xml --cov-append
-    - name: Clear cache ubuntu
       if: matrix.os == 'ubuntu-22.04'
       run: |
           rm -rf ~/.cache/*    
@@ -60,7 +56,7 @@ jobs:
       run: |
         cd ammico
         python -m pytest test/test_summary.py -m "not long" -svv --cov=. --cov-report=xml --cov-append
-    - name: Clear cache2
+    - name: Clear cache ubuntu 2
       if: matrix.os == 'ubuntu-22.04'
       run: |
           rm -rf ~/.cache/*

ammico/__init__.py

@@ -7,7 +7,6 @@ from ammico.cropposts import crop_media_posts, crop_posts_from_refs
 from ammico.display import AnalysisExplorer
 from ammico.faces import EmotionDetector
 from ammico.multimodal_search import MultimodalSearch
-from ammico.objects import ObjectDetector
 from ammico.summary import SummaryDetector
 from ammico.text import TextDetector, PostprocessText
 from ammico.utils import find_files, get_dataframe
@@ -22,7 +21,6 @@ __all__ = [
     "AnalysisExplorer",
     "EmotionDetector",
     "MultimodalSearch",
-    "ObjectDetector",
     "SummaryDetector",
     "TextDetector",
     "PostprocessText",

ammico/display.py

@@ -1,6 +1,5 @@
 import ammico.faces as faces
 import ammico.text as text
-import ammico.objects as objects
 import ammico.colors as colors
 from ammico.utils import is_interactive
 import ammico.summary as summary
@@ -332,7 +331,6 @@ class AnalysisExplorer:
                             dcc.Dropdown(
                                 options=[
                                     "TextDetector",
-                                    "ObjectDetector",
                                     "EmotionDetector",
                                     "SummaryDetector",
                                     "ColorDetector",
@@ -465,7 +463,6 @@ class AnalysisExplorer:
         identify_dict = {
             "EmotionDetector": faces.EmotionDetector,
             "TextDetector": text.TextDetector,
-            "ObjectDetector": objects.ObjectDetector,
             "SummaryDetector": summary.SummaryDetector,
             "ColorDetector": colors.ColorDetector,
         }

ammico/notebooks/DemoNotebook_ammico.ipynb

@@ -226,17 +226,7 @@
     "```\n",
     "image_dict[\"image_id\"] = ammico.EmotionDetector(image_dict[\"image_id\"], emotion_threshold=50, race_threshold=50).analyse_image()\n",
     "```\n",
-    "The thresholds can be adapted interactively in the notebook interface and the optimal value can then be used in a subsequent analysis of the whole data set.\n",
+    "The thresholds can be adapted interactively in the notebook interface and the optimal value can then be used in a subsequent analysis of the whole data set."
-    "\n",
-    "## Object detection\n",
-    "Certain specified objects on the image are detected with the cvlib library and YOLOv4 model. Of the 80 objects from the [yolov library](https://github.com/AlexeyAB/darknet/blob/master/data/coco.names), the detection is restricted to person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light.\n",
-    "\n",
-    "The analysis returns a dictionary with \"yes\" or \"no\" answers for the detected items: `\"person\": \"yes\", \"bicycle\": \"no\", \"car\": \"no\", \"motorcycle\": \"no\", \"airplane\": \"no\", \"bus\": \"no\", \"train\": \"no\", \"truck\": \"no\", \"boat\": \"no\", \"traffic light\": \"no\", \"cell phone\": \"no\"`.\n",
-    "\n",
-    "The object detection is very straightforward with no further parameters, and is called using\n",
-    "```\n",
-    "image_dict[\"image_id\"] = ammico.ObjectDetector(image_dict[\"image_id\"]).analyse_image()\n",
-    "```"
    ]
   },
   {

ammico/notebooks/objects_expression.ipynb

@@ -1,203 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Objects recognition"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebooks shows how to detect objects quickly using [cvlib](https://github.com/arunponnusamy/cvlib) and the [YOLOv4](https://github.com/AlexeyAB/darknet) model. This library detects faces, people, and several inanimate objects; we currently have restricted the output to person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, cell phone.\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": null,
-   "metadata": {},
-   "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": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import ammico"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Set an image path as input file path."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Here you need to provide the path to your google drive folder\n",
-    "# or local folder containing the images\n",
-    "mydict = ammico.find_files(\n",
-    "    path=\"/content/drive/MyDrive/misinformation-data/\",\n",
-    "    limit=10,\n",
-    ")\n",
-    "mydict"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Detect objects and directly write to csv\n",
-    "You can 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": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for key in mydict:\n",
-    "    mydict[key] = ammico.ObjectDetector(mydict[key]).analyse_image()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Convert the dictionary of dictionarys into a dictionary with lists:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "outdict = ammico.append_data_to_dict(mydict)\n",
-    "df = ammico.dump_df(outdict)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Check the dataframe:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.head(10)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Write the csv file:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.to_csv(\"/content/drive/MyDrive/misinformation-data/data_out.csv\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Manually inspect what was detected\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, you can directly export a csv file in the step above.\n",
-    "Here, we display the object detection results provided by the above library. 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(mydict)\n",
-    "analysis_explorer.run_server(port=8056)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "f1142466f556ab37fe2d38e2897a16796906208adb09fea90ba58bdf8a56f0ba"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

ammico/objects.py

@@ -1,54 +0,0 @@
-from ammico.utils import AnalysisMethod
-from ammico.objects_cvlib import ObjectCVLib
-from ammico.objects_cvlib import init_default_objects
-
-
-class ObjectDetectorClient(AnalysisMethod):
-    def __init__(self):
-        # The detector is set to CVLib by default
-        self.detector = ObjectCVLib()
-
-    def set_client_to_cvlib(self):
-        """Set the object detection client to use CVLib."""
-        self.detector = ObjectCVLib()
-
-    def analyse_image(self, subdict=None):
-        """Localize objects in the given image.
-
-        Args:
-            subdict (dict): The dictionary for an image expression instance.
-
-        Returns:
-            dict: The updated dictionary with object detection results.
-        """
-        return self.detector.analyse_image(subdict)
-
-
-class ObjectDetector(AnalysisMethod):
-    od_client = ObjectDetectorClient()
-
-    def __init__(self, subdict: dict):
-        super().__init__(subdict)
-        self.subdict.update(self.set_keys())
-
-    def set_keys(self):
-        """Set the default object keys for analysis.
-
-        Returns:
-            dict: The dictionary with default object keys.
-        """
-        return init_default_objects()
-
-    def analyse_image(self):
-        """Perform object detection on the image.
-
-        Returns:
-            dict: The updated dictionary with object detection results.
-        """
-        self.subdict = ObjectDetector.od_client.analyse_image(self.subdict)
-        return self.subdict
-
-    @staticmethod
-    def set_client_to_cvlib():
-        """Set the object detection client to use CVLib."""
-        ObjectDetector.od_client.set_client_to_cvlib()

ammico/objects_cvlib.py

@@ -1,77 +0,0 @@
-import cv2
-import cvlib as cv
-
-
-def objects_from_cvlib(objects_list: list) -> dict:
-    objects = init_default_objects()
-    for key in objects:
-        if key in objects_list:
-            objects[key] = "yes"
-    return objects
-
-
-def init_default_objects():
-    objects = {
-        "person": "no",
-        "bicycle": "no",
-        "car": "no",
-        "motorcycle": "no",
-        "airplane": "no",
-        "bus": "no",
-        "train": "no",
-        "truck": "no",
-        "boat": "no",
-        "traffic light": "no",
-        "cell phone": "no",
-    }
-    return objects
-
-
-class ObjectsMethod:
-    """Base class to be inherited by all objects methods."""
-
-    def __init__(self):
-        # initialize in child class
-        pass
-
-    def analyse_image(self, subdict):
-        raise NotImplementedError()
-
-
-class ObjectCVLib(ObjectsMethod):
-    def __init__(self, client_type=1):
-        # as long as imageai is not activated this remains empty
-        pass
-
-    def detect_objects_cvlib(self, image_path):
-        """Localize objects in the local image.
-
-        Args:
-            image_path: The path to the local file.
-        """
-        img = cv2.imread(image_path)
-
-        _, label, _ = cv.detect_common_objects(img)
-        objects = objects_from_cvlib(label)
-        return objects
-
-    def analyse_image_from_file(self, image_path):
-        """Localize objects in the local image.
-
-        Args:
-            image_path: The path to the local file.
-        """
-        objects = self.detect_objects_cvlib(image_path)
-        return objects
-
-    def analyse_image(self, subdict):
-        """Localize objects in the local image.
-
-        Args:
-            subdict: The dictionary for an image expression instance.
-        """
-        objects = self.analyse_image_from_file(subdict["filename"])
-        for key in objects:
-            subdict[key] = objects[key]
-
-        return subdict

ammico/test/data/example_analysis_objects.json

@@ -1 +0,0 @@
-{"person": "yes", "bicycle": "no", "car": "yes", "motorcycle": "no", "airplane": "no", "bus": "yes", "train": "no", "truck": "no", "boat": "no", "traffic light": "no", "cell phone": "no"}

ammico/test/data/example_objects_cvlib.json

@@ -1 +0,0 @@
-{"filename": "IMG_2809.png", "person": "yes", "bicycle": "no", "car": "yes", "motorcycle": "no", "airplane": "no", "bus": "yes", "train": "no", "truck": "no", "boat": "no", "traffic light": "no", "cell phone": "no"}

ammico/test/test_display.py

@@ -37,40 +37,11 @@ def test_explore_analysis_faces(get_path):
             assert sub_dict[key] == outs[key]
 
 
-def test_explore_analysis_objects(get_path):
-    mydict = {"IMG_2809": {"filename": get_path + "IMG_2809.png"}}
-    with open(get_path + "example_analysis_objects.json", "r") as file:
-        outs = json.load(file)
-    mydict["IMG_2809"].pop("filename", None)
-    for im_key in mydict.keys():
-        sub_dict = mydict[im_key]
-        for key in sub_dict.keys():
-            assert sub_dict[key] == outs[key]
-
-
 def test_AnalysisExplorer(get_AE, get_options):
     get_AE.update_picture(get_options[0])
     assert get_AE.update_picture(None) is None
 
 
-def test_right_output_analysis_objects(get_AE, get_options):
-    get_AE._right_output_analysis(
-        2,
-        get_options[3],
-        get_options[0],
-        "ObjectDetector",
-        True,
-        None,
-        None,
-        50,
-        50,
-        "CIE 1976",
-        "summary_and_questions",
-        "base",
-        "How many people are in the picture?",
-    )
-
-
 def test_right_output_analysis_emotions(get_AE, get_options):
     get_AE._right_output_analysis(
         2,

ammico/test/test_objects.py

@@ -1,94 +0,0 @@
-import json
-import pytest
-import ammico.objects as ob
-import ammico.objects_cvlib as ob_cvlib
-import sys
-
-OBJECT_1 = "cell phone"
-OBJECT_2 = "motorcycle"
-OBJECT_3 = "traffic light"
-TEST_IMAGE_1 = "IMG_2809.png"
-JSON_1 = "example_objects_cvlib.json"
-
-
-@pytest.fixture()
-def default_objects():
-    return ob.init_default_objects()
-
-
-def test_objects_from_cvlib(default_objects):
-    objects_list = [OBJECT_1, OBJECT_2, OBJECT_3]
-    objects = ob_cvlib.objects_from_cvlib(objects_list)
-    out_objects = default_objects
-    for obj in objects_list:
-        out_objects[obj] = "yes"
-
-    assert str(objects) == str(out_objects)
-
-
-def test_analyse_image_cvlib(get_path):
-    mydict = {"filename": get_path + TEST_IMAGE_1}
-    ob_cvlib.ObjectCVLib().analyse_image(mydict)
-
-    with open(get_path + JSON_1, "r") as file:
-        out_dict = json.load(file)
-    out_dict["filename"] = get_path + out_dict["filename"]
-    for key in mydict.keys():
-        assert mydict[key] == out_dict[key]
-
-
-def test_init_default_objects():
-    default_obj_list = [
-        "person",
-        "bicycle",
-        "car",
-        OBJECT_2,
-        "airplane",
-        "bus",
-        "train",
-        "truck",
-        "boat",
-        OBJECT_3,
-        OBJECT_1,
-    ]
-    init_objects = ob_cvlib.init_default_objects()
-    for obj in default_obj_list:
-        assert init_objects[obj] == "no"
-
-
-def test_analyse_image_from_file_cvlib(get_path):
-    file_path = get_path + TEST_IMAGE_1
-    objs = ob_cvlib.ObjectCVLib().analyse_image_from_file(file_path)
-
-    with open(get_path + JSON_1, "r") as file:
-        out_dict = json.load(file)
-    out_dict["filename"] = get_path + out_dict["filename"]
-    for key in objs.keys():
-        assert objs[key] == out_dict[key]
-
-
-def test_detect_objects_cvlib(get_path):
-    file_path = get_path + TEST_IMAGE_1
-    objs = ob_cvlib.ObjectCVLib().detect_objects_cvlib(file_path)
-
-    with open(get_path + JSON_1, "r") as file:
-        out_dict = json.load(file)
-    for key in objs.keys():
-        assert objs[key] == out_dict[key]
-
-
-def test_set_keys(default_objects, get_path):
-    mydict = {"filename": get_path + TEST_IMAGE_1}
-    key_objs = ob.ObjectDetector(mydict).set_keys()
-    assert str(default_objects) == str(key_objs)
-
-
-def test_analyse_image(get_path):
-    mydict = {"filename": get_path + TEST_IMAGE_1}
-    ob.ObjectDetector.set_client_to_cvlib()
-    ob.ObjectDetector(mydict).analyse_image()
-    with open(get_path + JSON_1, "r") as file:
-        out_dict = json.load(file)
-    out_dict["filename"] = get_path + out_dict["filename"]
-
-    assert str(mydict) == str(out_dict)

docs/source/notebooks/Example objects.ipynb

@@ -1,214 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Objects recognition"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebooks shows how to detect objects quickly using [cvlib](https://github.com/arunponnusamy/cvlib) and the [YOLOv4](https://github.com/AlexeyAB/darknet) model. This library detects faces, people, and several inanimate objects; we currently have restricted the output to person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, cell phone.\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": null,
-   "metadata": {},
-   "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": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import ammico\n",
-    "from ammico import utils as mutils\n",
-    "from ammico import display as mdisplay\n",
-    "import ammico.objects as ob"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Set an image path as input file path."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "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": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mydict = mutils.initialize_dict(images)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Detect objects and directly write to csv\n",
-    "You can 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": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for key in mydict:\n",
-    "    mydict[key] = ob.ObjectDetector(mydict[key]).analyse_image()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Convert the dictionary of dictionarys into a dictionary with lists:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "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": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.head(10)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Write the csv file:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.to_csv(\"data_out.csv\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Manually inspect what was detected\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, you can directly export a csv file in the step above.\n",
-    "Here, we display the object detection results provided by the above library. 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 = mdisplay.AnalysisExplorer(mydict, identify=\"objects\")\n",
-    "analysis_explorer.run_server(port=8056)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "f1142466f556ab37fe2d38e2897a16796906208adb09fea90ba58bdf8a56f0ba"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

pyproject.toml

@@ -23,7 +23,6 @@ classifiers = [
 
 dependencies = [
     "bertopic<=0.14.1",
-    "cvlib @ git+https://github.com/iulusoy/cvlib.git@iulusoy-patch-1",
     "dash>=2.11.0",
     "deepface<=0.0.75",
     "googletrans==3.1.0a0",