editted github docs.yml, Dockerfile and README.md

.github/workflows/docs.yml

@@ -15,7 +15,7 @@ jobs:
     - uses: actions/checkout@v3
       with:
         fetch-depth: 0 # otherwise, you will failed to push refs to dest repo
-    - name: install misinformation
+    - name: install ammico
       run: |
         pip install -e .
         python -m pip install -r requirements-dev.txt

Dockerfile

@@ -6,20 +6,20 @@ RUN apt update && apt install -y build-essential libgl1 libglib2.0-0 libsm6 libx
 USER $NB_USER
 
 # Copy the repository into the container
-COPY --chown=${NB_UID} . /opt/misinformation
+COPY --chown=${NB_UID} . /opt/ammico
 
 # Install the Python package
-RUN python -m pip install /opt/misinformation
+RUN python -m pip install /opt/ammico
 
 # Make JupyterLab the default for this application
 ENV JUPYTER_ENABLE_LAB=yes
 
 # Export where the data is located
-ENV XDG_DATA_HOME=/opt/misinformation/data
+ENV XDG_DATA_HOME=/opt/ammico/data
 
 # Copy notebooks into the home directory
 RUN rm -rf $HOME/work
-RUN cp /opt/misinformation/notebooks/*.ipynb $HOME
+RUN cp /opt/ammico/notebooks/*.ipynb $HOME
 
 ARG GOOGLE_CREDS
 ENV GOOGLE_APPLICATION_CREDENTIALS=credentials.json

README.md

@@ -45,17 +45,17 @@ This will install the package and its dependencies locally.
 
 There are sample notebooks in the `notebooks` folder for you to explore the package:
 1. Text extraction: Use the notebook `get-text-from-image.ipynb` to extract any text from the images. The text is directly translated into English. If the text should be further analysed, set the keyword `analyse_text` to `True` as demonstrated in the notebook.\
-**You can run this notebook on google colab: [Here](https://colab.research.google.com/github/ssciwr/misinformation/blob/main/notebooks/get-text-from-image.ipynb)**  
+**You can run this notebook on google colab: [Here](https://colab.research.google.com/github/ssciwr/ammico/blob/main/notebooks/get-text-from-image.ipynb)**  
 Place the data files and google cloud vision API key in your google drive to access the data.
 1. Emotion recognition: Use the notebook `facial_expressions.ipynb` to identify if there are faces on the image, if they are wearing masks, and if they are not wearing masks also the race, gender and dominant emotion.
-**You can run this notebook on google colab: [Here](https://colab.research.google.com/github/ssciwr/misinformation/blob/main/notebooks/facial_expressions.ipynb)**   
+**You can run this notebook on google colab: [Here](https://colab.research.google.com/github/ssciwr/ammico/blob/main/notebooks/facial_expressions.ipynb)**   
 Place the data files in your google drive to access the data.
 1. Content extraction: Use the notebook `image_summary.ipynb` to create captions for the images and ask questions about the image content.
-**You can run this notebook on google colab: [Here](https://colab.research.google.com/github/ssciwr/misinformation/blob/main/notebooks/image_summary.ipynb)**
+**You can run this notebook on google colab: [Here](https://colab.research.google.com/github/ssciwr/ammico/blob/main/notebooks/image_summary.ipynb)**
 1. Multimodal content: Use the notebook `multimodal_search.ipynb` to find the best fitting images to an image or text query.
-**You can run this notebook on google colab: [Here](https://colab.research.google.com/github/ssciwr/misinformation/blob/main/notebooks/multimodal_search.ipynb)**
+**You can run this notebook on google colab: [Here](https://colab.research.google.com/github/ssciwr/ammico/blob/main/notebooks/multimodal_search.ipynb)**
 1. Object analysis: Use the notebook `ojects_expression.ipynb` to identify certain objects in the image. Currently, the following objects are being identified: person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, cell phone.
-**You can run this notebook on google colab: [Here](https://colab.research.google.com/github/ssciwr/misinformation/blob/main/notebooks/objects_expression.ipynb)**
+**You can run this notebook on google colab: [Here](https://colab.research.google.com/github/ssciwr/ammico/blob/main/notebooks/objects_expression.ipynb)**
 
 There are further notebooks that are currently of exploratory nature (`colors_expression.ipynb` to identify certain colors on the image). To crop social media posts use the `cropposts.ipynb` notebook.