Dockerfile.azure

# Azure-optimized Dockerfile for production deployment
FROM python:3.11-slim as builder

# Set working directory
WORKDIR /app

# Install system dependencies
RUN apt-get update && apt-get install -y \
    gcc \
    g++ \
    libpq-dev \
    && rm -rf /var/lib/apt/lists/*

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

# Final stage
FROM python:3.11-slim

# Install runtime dependencies
RUN apt-get update && apt-get install -y \
    libpq5 \
    curl \
    && rm -rf /var/lib/apt/lists/*

# Set working directory
WORKDIR /app

# Copy Python packages from builder stage
COPY --from=builder /root/.local /root/.local

# Update PATH to include user packages
ENV PATH=/root/.local/bin:$PATH

# Copy application code
COPY src/ ./src/
COPY data/ ./data/
COPY models/ ./models/

# Create necessary directories
RUN mkdir -p monitoring mlruns

# Set environment variables for Azure
ENV PYTHONPATH=/app
ENV MLFLOW_TRACKING_URI=sqlite:///mlflow.db
ENV PYTHONUNBUFFERED=1

# Azure Web App expects PORT environment variable
ENV PORT=8000

# Expose port
EXPOSE $PORT

# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
    CMD curl -f http://localhost:$PORT/health || exit 1

# Run the application (Azure Web App will override this with its own command)
CMD ["python", "-m", "uvicorn", "src.api.main:app", "--host", "0.0.0.0", "--port", "8000"]