Initial microservices setup from artifacts

services/training/requirements.txt

@@ -0,0 +1,84 @@
+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")
+
+@