Update Dockerfile

Dockerfile

@@ -1,75 +1,73 @@
-# Use the NVIDIA CUDA runtime as a base image (MATCHING PyTorch)
+# 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
 
-# Create a non-root user
 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 \
-    ninja-build \
     build-essential \
     libgl1-mesa-glx \
     libglib2.0-0 \
     ffmpeg \
-    python3.9 \
+    python3.8 \
     python3-pip \
-    python3.9-dev \
-    python3.9-venv \
+    python3.8-dev \
     && rm -rf /var/lib/apt/lists/*
 
-# Set Python 3.9 as the default python and pip versions
-RUN update-alternatives --install /usr/bin/python python /usr/bin/python3.9 1
+
+# 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
 
-# Switch to non-root user
 USER user
-
-# Create virtual environment
-RUN python -m venv /home/user/venv
-ENV VIRTUAL_ENV=/home/user/venv
-ENV PATH="$VIRTUAL_ENV/bin:$PATH"
-
-# Set environment variables
-ENV HOME=/home/user \
-    CUDA_HOME=/usr/local/cuda \
-    LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH} \
-    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 working directory
-WORKDIR $HOME/app
-
-# Upgrade pip and install numpy<2 first, then PyTorch (with correct CUDA version)
-RUN pip install --no-cache-dir --upgrade pip && \
-    pip install --no-cache-dir "numpy<2" && \
-    pip install --no-cache-dir torch==1.10.0 torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113 && \
-    python -c "import torch; print('PyTorch version:', torch.__version__); print('CUDA Available:', torch.cuda.is_available()); print('Device:', torch.cuda.get_device_name(0))"
-
-# Clone the repository
+# Clone the repository (adjust the URL if needed)
 RUN git clone --recursive https://github.com/jnjaby/KEEP.git .
 
-# Copy application files
+# Copy the app.py script into the container
 COPY app.py .
 COPY requirements_HF.txt .
 
-# Install Python dependencies
-RUN pip install --no-cache-dir -r requirements_HF.txt
-RUN pip install --no-cache-dir gradio ffmpeg-python dlib-
-RUN pip install basicsr
+# 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
 
-# Set CUDA device settings
+USER user
+# Install additional Python packages
+RUN pip install ffmpeg-python
+RUN pip install dlib-bin
+
+# Set the environment variable to specify the GPU device
 ENV CUDA_DEVICE_ORDER=PCI_BUS_ID
 ENV CUDA_VISIBLE_DEVICES=0
 
-# Command to run the application
+
+# Command to run your application
 CMD ["python", "app.py"]