Dockerfile

# Use the NVIDIA CUDA runtime as a base image
FROM nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04

# Set environment variables
ENV DEBIAN_FRONTEND=noninteractive

RUN useradd -m -u 1000 user

USER user

ENV HOME=/home/user \
    PATH=/home/user/.local/bin:$PATH \
    PYTHONPATH=$HOME/app \
    PYTHONUNBUFFERED=1 \
    GRADIO_ALLOW_FLAGGING=never \
    GRADIO_NUM_PORTS=1 \
    GRADIO_SERVER_NAME=0.0.0.0 \
    GRADIO_THEME=huggingface \
    GRADIO_SHARE=False \
    SYSTEM=spaces

# Set the working directory to the user's home directory
WORKDIR $HOME/app

# Install system dependencies as root
USER root
RUN apt-get update && apt-get install -y --no-install-recommends \
    git \
    cmake \
    build-essential \
    libgl1-mesa-glx \
    libglib2.0-0 \
    ffmpeg \
    python3.8 \
    python3-pip \
    python3.8-dev \
    && rm -rf /var/lib/apt/lists/*

# Set Python 3.8 as the default python and pip versions
RUN update-alternatives --install /usr/bin/python python /usr/bin/python3.8 1
RUN update-alternatives --install /usr/bin/pip pip /usr/bin/pip3 1

USER user
# Clone the repository (adjust the URL if needed)
RUN git clone --recursive https://github.com/jnjaby/KEEP.git .

# Copy the app.py script into the container
COPY app.py .
COPY requirements_HF.txt .

# Install Python dependencies from requirements.txt
RUN pip install --upgrade pip
RUN pip install -r requirements_HF.txt
RUN pip install gradio
RUN pip install cupy==10.4.0

USER root
# Install basicsr (assuming setup.py is in basicsr directory)
RUN python basicsr/setup.py develop

USER user
# Install additional Python packages
RUN pip install ffmpeg-python
RUN pip install dlib

# Set the environment variable to specify the GPU device
ENV CUDA_DEVICE_ORDER=PCI_BUS_ID
ENV CUDA_VISIBLE_DEVICES=0

# Command to run your application
CMD ["python", "app.py"]