81 lines
2.1 KiB
Python
81 lines
2.1 KiB
Python
"""
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Training Service
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Handles ML model training for bakery demand forecasting
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"""
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import structlog
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from fastapi import FastAPI, BackgroundTasks
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from fastapi.middleware.cors import CORSMiddleware
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from app.core.config import settings
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from app.core.database import database_manager
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from app.api import training, models
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from app.services.messaging import setup_messaging, cleanup_messaging
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from shared.monitoring.logging import setup_logging
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from shared.monitoring.metrics import MetricsCollector
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# Setup logging
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setup_logging("training-service", settings.LOG_LEVEL)
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logger = structlog.get_logger()
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# Create FastAPI app
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app = FastAPI(
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title="Training Service",
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description="ML model training service for bakery demand forecasting",
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version="1.0.0"
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)
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# Initialize metrics collector
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metrics_collector = MetricsCollector("training-service")
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# CORS middleware
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# Include routers
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app.include_router(training.router, prefix="/training", tags=["training"])
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app.include_router(models.router, prefix="/models", tags=["models"])
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@app.on_event("startup")
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async def startup_event():
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"""Application startup"""
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logger.info("Starting Training Service")
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# Create database tables
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await database_manager.create_tables()
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# Initialize message publisher
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await setup_messaging()
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# Start metrics server
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metrics_collector.start_metrics_server(8080)
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logger.info("Training Service started successfully")
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@app.on_event("shutdown")
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async def shutdown_event():
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"""Application shutdown"""
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logger.info("Shutting down Training Service")
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# Cleanup message publisher
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await cleanup_messaging()
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logger.info("Training Service shutdown complete")
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@app.get("/health")
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async def health_check():
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"""Health check endpoint"""
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return {
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"status": "healthy",
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"service": "training-service",
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"version": "1.0.0"
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}
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8000) |