Dockerfile

# Use Python 3.10 slim image for compatibility with AI libraries
FROM python:3.10-slim-bullseye

# Set environment variables
ENV PYTHONDONTWRITEBYTECODE=1
ENV PYTHONUNBUFFERED=1
ENV DEBIAN_FRONTEND=noninteractive

# Set working directory
WORKDIR /app

# Install system dependencies required for OpenCV
RUN apt-get update && apt-get install -y --no-install-recommends \
    libgl1-mesa-glx \
    libglib2.0-0 \
    libsm6 \
    libxext6 \
    libxrender-dev \
    wget \
    && rm -rf /var/lib/apt/lists/*



# Copy requirements and install Python dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy the rest of the application code
COPY . .

# Ensure the LBF model is present (download if missing during build)
# We can run a small python script to check/download it
RUN python3 -c "import landmarks; landmarks.ensure_model()"

# Train the model during build (if dataset is provided in the context)
RUN if [ -d "dataset" ]; then \
    echo "Dataset found. Training face shape model..." && \
    python3 train_means.py; \
    else \
    echo "No dataset found. Using pre-trained face_shape_means.pkl if available."; \
    fi


# Expose port (HF Spaces uses 7860 by default)
EXPOSE 7860

# Start the server (Railway/Spaces provide PORT env var)
# We default to 7860 for HF Spaces
CMD ["sh", "-c", "uvicorn app:app --host 0.0.0.0 --port ${PORT:-7860}"]