Create Dockerfile

Dockerfile

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+# Use the NVIDIA CUDA runtime as a base image
+FROM nvidia/cuda:11.8.0-devel-ubuntu22.04
+
+# Set environment variables
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN useradd -m -u 1000 user
+
+USER user
+
+ENV HOME=/home/user \
+    CUDA_HOME=/usr/local/cuda \
+    PATH=/home/user/.local/bin:$PATH \
+    LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH} \
+    LIBRARY_PATH=${CUDA_HOME}/lib64/stubs:${LIBRARY_PATH} \
+    PYTHONPATH=$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 \
+    python3.9 \
+    python3-pip \
+    python3.9-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
+RUN update-alternatives --install /usr/bin/pip pip /usr/bin/pip3 1
+
+USER user
+# Copy dependencies and install them via pip
+COPY . .
+RUN pip install --upgrade pip
+RUN pip install -r requirements.txt
+RUN pip install gradio
+
+USER root
+# Configure git to trust the directory
+RUN git config --global --add safe.directory /home/user/app
+RUN chown -R user:user /home/user/app
+
+USER user
+
+
+# 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"]