# Optimized Dockerfile for Railway Deployment
# Handles large ML dependencies and model files efficiently

FROM python:3.10-slim

ENV PYTHONUNBUFFERED=1

# Install system dependencies required for ML libraries
RUN apt-get update && apt-get install -y --no-install-recommends \
    build-essential \
    ffmpeg \
    git \
    curl \
    wget \
    libglib2.0-0 \
    libsm6 \
    libxrender1 \
    libxext6 \
 && rm -rf /var/lib/apt/lists/*

WORKDIR /app

# Copy requirements first (for better caching)
COPY requirements.txt /app/requirements.txt

# Upgrade pip and install Python dependencies
RUN pip install --upgrade pip setuptools wheel && \
    pip install --no-cache-dir -r /app/requirements.txt

# Copy application code (excluding large files via .dockerignore)
COPY . /app

# Create runtime directories
RUN mkdir -p /data/activity_runtime/uploads && \
    mkdir -p /data/activity_runtime/outputs && \
    mkdir -p /data/activity_runtime/attendance

# Set environment variables
ENV PORT=8080
ENV ACTIVITY_WEB_RUNTIME_DIR=/data/activity_runtime
ENV PYTHONPATH=/app

# Expose port
EXPOSE 8080

# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=40s --retries=3 \
  CMD curl -f http://localhost:8080/ || exit 1

# Start application
CMD ["gunicorn", "--bind", "0.0.0.0:8080", "--timeout", "120", "--workers", "2", "activity_web.backend.app:app"]