dockerfile

# Use an official NVIDIA CUDA base image for GPU support
FROM nvidia/cuda:12.1.0-runtime-ubuntu22.04

# Set up Python and other system dependencies
RUN apt-get update && apt-get install -y \
    python3 \
    python3-pip \
    git \
    wget \
    libgl1-mesa-glx \
    libglib2.0-0 && \
    rm -rf /var/lib/apt/lists/*

# Set the working directory
WORKDIR /app

# Install Conda and create the environment
COPY environment.yml .
RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh && \
    bash miniconda.sh -b -p /opt/conda && \
    rm miniconda.sh
ENV PATH="/opt/conda/bin:$PATH"

# Create the Conda environment
RUN conda env create -f environment.yml

# Activate the environment and install GPU-specific dependencies
SHELL ["conda", "run", "-n", "yolo-onnx-cpu-env", "/bin/bash", "-c"]

# Replace onnxruntime with the GPU version
RUN pip uninstall -y onnxruntime && pip install onnxruntime-gpu==1.22.1 gunicorn flask

# Copy your application files
COPY scoring_Yolo_Model.py .
COPY best.onnx .
COPY scoring_Yolo_Model_Gunicorn.py . # This is the file you provided

# Expose the default port for Hugging Face Spaces Docker
EXPOSE 7860

# The CMD to run the Gunicorn server
# Note: Hugging Face Spaces uses port 7860 by default for Docker.
CMD ["conda", "run", "-n", "yolo-onnx-cpu-env", "gunicorn", "--bind", "0.0.0.0:7860", "scoring_Yolo_Model_Gunicorn:app"]