fastapi==0.104.1
uvicorn[standard]==0.24.0
sqlalchemy==2.0.23
asyncpg==0.29.0
alembic==1.12.1
pydantic==2.5.0
pydantic-settings==2.1.0
httpx==0.25.2
redis==5.0.1
aio-pika==9.3.0
prometheus-client==0.17.1
python-json-logger==2.0.4

# ML dependencies
prophet==1.1.4
scikit-learn==1.3.2
pandas==2.1.4
numpy==1.24.4
joblib==1.3.2
scipy==1.11.4

# Utilities
pytz==2023.3
python-dateutil==2.8.2# services/training/app/main.py
"""
Training Service
Handles ML model training for bakery demand forecasting
"""

import logging
from fastapi import FastAPI, BackgroundTasks
from fastapi.middleware.cors import CORSMiddleware

from app.core.config import settings
from app.core.database import database_manager
from app.api import training, models
from app.services.messaging import message_publisher
from shared.monitoring.logging import setup_logging
from shared.monitoring.metrics import MetricsCollector

# Setup logging
setup_logging("training-service", settings.LOG_LEVEL)
logger = logging.getLogger(__name__)

# Create FastAPI app
app = FastAPI(
    title="Training Service",
    description="ML model training service for bakery demand forecasting",
    version="1.0.0"
)

# Initialize metrics collector
metrics_collector = MetricsCollector("training-service")

# CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Include routers
app.include_router(training.router, prefix="/training", tags=["training"])
app.include_router(models.router, prefix="/models", tags=["models"])

@app.on_event("startup")
async def startup_event():
    """Application startup"""
    logger.info("Starting Training Service")
    
    # Create database tables
    await database_manager.create_tables()
    
    # Initialize message publisher
    await message_publisher.connect()
    
    # Start metrics server
    metrics_collector.start_metrics_server(8080)
    
    logger.info("Training Service started successfully")

@