Dockerfile

# Use Python 3.10 slim image for smaller size
FROM python:3.10-slim

# Set working directory
WORKDIR /app

# Install system dependencies required for OpenCV and other packages
RUN apt-get update && apt-get install -y \
    libgl1 \
    libglib2.0-0 \
    libsm6 \
    libxext6 \
    libxrender-dev \
    libgomp1 \
    wget \
    && rm -rf /var/lib/apt/lists/*

# Copy requirements first for better caching
COPY requirements.txt .

# Install Python dependencies
# Install PyTorch CPU version to reduce image size (GPU not available on HF free tier)
RUN pip install --no-cache-dir torch torchvision --index-url https://download.pytorch.org/whl/cpu && \
    pip install --no-cache-dir -r requirements.txt

# Copy application code
COPY main.py .

# Download SAM model (using smaller vit_b model for HF)
# You can change this to vit_h or vit_l if needed, but they're larger
RUN wget -q https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth || \
    echo "Warning: Could not download SAM model. App will run with fallback methods."

# Update main.py to use the vit_b model if downloaded
RUN if [ -f "sam_vit_b_01ec64.pth" ]; then \
    sed -i 's/sam_vit_h_4b8939.pth/sam_vit_b_01ec64.pth/g' main.py && \
    sed -i 's/vit_h/vit_b/g' main.py; \
    fi

# Create a non-root user for Hugging Face
RUN useradd -m -u 1000 user
USER user

# Set environment variables
ENV HOME=/home/user \
    PATH=/home/user/.local/bin:$PATH \
    PYTHONUNBUFFERED=1

# Hugging Face Spaces uses port 7860 by default
EXPOSE 7860

# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \
    CMD python -c "import requests; requests.get('http://localhost:7860/health')" || exit 1

# Run the application on port 7860 (Hugging Face default)
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]