# ─────────────────────────────────────────────────────────────────────────────
# IPL MLOps Pipeline — docker-compose
#
# Services:
#   api        FastAPI prediction service          :8000
#   monitor    Drift detection (runs every 5 min)
#   dashboard  Streamlit monitoring dashboard      :8501
#
# Shared volume:  ./data/  (predictions.db, drift_status.json, drift_history.json)
# Artefacts:      ./api/artefacts/  (model.pkl, encoders, feature list)
#
# Quick start:
#   1. cd training && python train.py      # generates api/artefacts/
#   2. docker-compose up --build
#   3. Open http://localhost:8501          # dashboard
#      Open http://localhost:8000/docs    # API swagger
# ─────────────────────────────────────────────────────────────────────────────

services:

  api:
    build: ./api
    ports:
      - "8000:8000"
    volumes:
      - ./api/artefacts:/app/artefacts:ro
      - ./data:/app/data
    environment:
      - PYTHONUNBUFFERED=1
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
      interval: 30s
      timeout: 10s
      retries: 3
    restart: unless-stopped

  monitor:
    build: ./monitoring
    volumes:
      - ./api/artefacts:/app/artefacts:ro
      - ./data:/app/data
    environment:
      - DRIFT_INTERVAL_SECONDS=300
      - PYTHONUNBUFFERED=1
    depends_on:
      - api
    restart: unless-stopped

  dashboard:
    build: ./dashboard
    ports:
      - "8501:8501"
    volumes:
      - ./data:/app/data
    environment:
      - DATA_DIR=/app/data
      - API_URL=http://api:8000
      - PYTHONUNBUFFERED=1
    depends_on:
      - api
    restart: unless-stopped