Dockerfile

# Use Python 3.9 as the base image
FROM python:3.9

# Create a user and set permissions
RUN useradd -m -u 1000 user
USER user

# Ensure PATH includes user's local bin
ENV PATH="/home/user/.local/bin:$PATH"

# Set environment variables to cache Hugging Face & Torch models
ENV TRANSFORMERS_CACHE="/cache/huggingface"
ENV TORCH_HOME="/cache/torch"

# Set working directory
WORKDIR /app

# Create model cache directories with correct permissions
RUN mkdir -p /cache/huggingface /cache/torch && chmod -R 777 /cache

# Preload models into cache
RUN python -c "from transformers import AutoModelForCausalLM, AutoTokenizer; \
               AutoModelForCausalLM.from_pretrained('microsoft/phi-2'); \
               AutoTokenizer.from_pretrained('microsoft/phi-2')" \
    && python -c "from torchvision.models.detection import fasterrcnn_resnet50_fpn, FasterRCNN_ResNet50_FPN_Weights; \
                  weights = FasterRCNN_ResNet50_FPN_Weights.DEFAULT; \
                  model = fasterrcnn_resnet50_fpn(weights=weights)"

# Copy requirements and install dependencies
COPY --chown=user ./requirements.txt requirements.txt
RUN pip install --no-cache-dir --upgrade -r requirements.txt

# Copy the rest of the application code
COPY --chown=user . /app

# Expose the port (optional)
EXPOSE 7860

# Start the FastAPI application with Uvicorn
CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]