Dockerfile

# Multi-stage Dockerfile for AI Task Agent
# Optimized for Google Cloud Run deployment

# Stage 1: Builder
FROM python:3.11-slim as builder

WORKDIR /app

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

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

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

# Stage 2: Runtime
FROM python:3.11-slim

WORKDIR /app

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

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

# Make sure scripts in .local are usable
ENV PATH=/root/.local/bin:$PATH

# Copy application code
COPY . .

# Create /tmp directory for database storage (writable in Cloud Run)
RUN mkdir -p /tmp

# Set environment variables
ENV PYTHONUNBUFFERED=1
ENV PORT=8080

# Expose port (Cloud Run will set PORT env var)
EXPOSE 8080

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

# Run the application
# Cloud Run will inject the PORT environment variable
CMD exec uvicorn api.main:app --host 0.0.0.0 --port ${PORT} --workers 1