Dockerfile

# Base image with TensorFlow GPU support
# Use CPU version for Hugging Face Spaces free tier compatibility if needed
# But keeping GPU as the project is configured for it. 
# HF Spaces offers CPU Basic (Free) and GPU upgrades. 
# Using a lighter base image might be better for free tier, but TF is heavy anyway.
FROM tensorflow/tensorflow:2.13.0

# Set working directory
WORKDIR /app

# Install system dependencies including libGL for OpenCV
RUN apt-get update && apt-get install -y \
    libgl1-mesa-glx \
    libglib2.0-0 \
    libsm6 \
    libxext6 \
    libxrender-dev \
    && rm -rf /var/lib/apt/lists/*

# Copy requirements first for caching
COPY requirements.txt .

# Install Python dependencies
RUN pip install --no-cache-dir -r requirements.txt

# Copy application code
COPY src/ ./src/
COPY frontend/ ./frontend/

# Create a models directory
# Note: You must upload your trained models here or use Git LFS
COPY models/ ./models/

# Expose the port Hugging Face Spaces expects
EXPOSE 7860

# Default command to run Streamlit on port 7860
CMD ["streamlit", "run", "frontend/app.py", "--server.port", "7860", "--server.address", "0.0.0.0"]