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# Use an official PyTorch image with CUDA support as the base image
FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime
# Install Git and system libraries required for OpenGL without interactive prompts
ENV DEBIAN_FRONTEND=noninteractive
# Install Git and OpenGL libraries, and libglib2.0
RUN apt-get update && apt-get install -y git libgl1-mesa-glx libglib2.0-0
# Set up a new user named "user" with user ID 1000
RUN useradd -m -u 1000 user
# Switch to the "user" 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
# Clone your repository or add your code to the container
RUN git clone -b main https://github.com/fffiloni/daclip-uir $HOME/app
# Install dependencies
RUN pip install --no-cache-dir -r requirements.txt gradio
# Copy the pretrained file and folder into the working directory
COPY pretrained_daclip_uir/ $HOME/app/universal-image-restoration/config/daclip-sde/pretrained/
RUN find $HOME/app
# Set the working directory to the app.py file's location
#WORKDIR $HOME/app/universal-image-restoration/config/daclip-sde/
# Set the environment variable to specify the GPU device
ENV CUDA_DEVICE_ORDER=PCI_BUS_ID
ENV CUDA_VISIBLE_DEVICES=0
# Set the working directory to the user's home directory
WORKDIR $HOME/app/universal-image-restoration/config/daclip-sde/
# Run your app.py script
CMD ["python", "app.py"]
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