# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
# you will also find guides on how best to write your Dockerfile
FROM debian:bullseye-slim

# Install prerequisites
#RUN apt-get update -y && \
#    apt-get install -y apt-transport-https ca-certificates gnupg curl

# Add the Cloud SDK distribution URI as a package source
#RUN echo "deb [signed-by=/usr/share/keyrings/cloud.google.asc] https://packages.cloud.google.com/apt cloud-sdk main" | tee -a /etc/apt/sources.list.d/google-cloud-sdk.list

# Import the Google Cloud public key
#RUN curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | tee /usr/share/keyrings/cloud.google.asc

# Update the package list and install the Google Cloud CLI
#RUN apt-get update -y && apt-get install -y google-cloud-sdk

# You can include additional commands to configure or use the gcloud CLI here
# For example, you can authenticate with your service account key file:
#COPY your-service-account-key.json /tmp/key.json
#ENV GOOGLE_APPLICATION_CREDENTIALS=/tmp/key.json
#RUN gcloud auth activate-service-account --key-file=${GOOGLE_APPLICATION_CREDENTIALS}

# ... further instructions ...
ENV NVIDIA_VISIBLE_DEVICES all
ENV NVIDIA_DRIVER_CAPABILITIES compute,utility
ENV NVIDIA_REQUIRE_CUDA "cuda>=8.0"
ENV LD_LIBRARY_PATH=/usr/local/cuda-11.8/targets/x86_64-linux/lib:/usr/local/cuda-11.8/targets/x86_64-linux/include:/usr/local/cuda/include:/usr/local/cuda-11.8:/usr/local/cuda/lib:/usr/local/cuda/lib64:$LD_LIBRARY_PATH
#RUN dpkg --add-architecture amd64
#RUN apt-get update && \
#    apt-get install -y --no-install-recommends gnupg2 curl ca-certificates
#RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/debian11/x86_64/7fa2af80.pub

# Import the NVIDIA GPG key
#RUN apt-get update && apt-get install -y gnupg2 curl

# Fetch the NVIDIA repository GPG key
#RUN curl -fsSL https://developer.download.nvidia.com/compute/cuda/repos/debian11/x86_64/7fa2af80.pub | gpg --dearmor -o /usr/share/keyrings/cuda-archive-keyring.gpg

# Add the NVIDIA repository to the APT sources list
#RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub

#RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub


#RUN echo "deb https://developer.download.nvidia.com/compute/cuda/repos/debian11/x86_64 /" > /etc/apt/sources.list.d/cuda.list

# Install FFmpeg

RUN apt-get update && \
    apt-get install -y python3-pip ffmpeg 
#libcublas-11-8 libcudnn8=8.6.0.163-1+cuda11.8
    #libcudnn8=8.8.0.121-1+cuda11.8





# Set the working directory to /code
WORKDIR /app

# Copy the requirements file into the container at /code/
#COPY ./requirements.txt /app/requirements.txt
# Copy the FastAP application code into the container
COPY ./ /app

#COPY ./interface.html /app/interface.html
#COPY ./styles.css /app/styles.css
# Install the required Python packages from requirements.txt
RUN pip install --no-cache-dir --upgrade -r requirements.txt
RUN pip install --upgrade fastapi authlib starlette

# Create a non-root user (optional but recommended for security)
RUN useradd -m -u 1000 user

# Switch to the non-root user
USER user

# Define environment variables
ENV HOME=/home/user \
    PATH=/home/user/.local/bin:$PATH

# Change the working directory to /home/user/app
WORKDIR $HOME/app

# Copy the rest of the application files into the container
COPY --chown=user . $HOME/app
# Expose port 80 for the FastAPI application
EXPOSE 7860
# Specify the command to run your application (modify as needed)
CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]