Improve auth models
This commit is contained in:
@@ -1,4 +1,6 @@
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# services/training/app/main.py
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# ================================================================
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# services/training/app/main.py - FIXED VERSION
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# ================================================================
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"""
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Training Service Main Application
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Enhanced with proper error handling, monitoring, and lifecycle management
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@@ -19,7 +21,7 @@ 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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from shared.auth.decorators import require_auth
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# REMOVED: from shared.auth.decorators import require_auth
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# Setup structured logging
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setup_logging("training-service", settings.LOG_LEVEL)
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@@ -52,6 +54,9 @@ async def lifespan(app: FastAPI):
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metrics_collector.start_metrics_server(8080)
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logger.info("Metrics server started on port 8080")
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# Store metrics collector in app state
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app.state.metrics_collector = metrics_collector
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# Mark service as ready
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app.state.ready = True
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logger.info("Training Service startup completed successfully")
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@@ -67,74 +72,57 @@ async def lifespan(app: FastAPI):
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logger.info("Shutting down Training Service")
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try:
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# Stop metrics server
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if hasattr(app.state, 'metrics_collector'):
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await app.state.metrics_collector.shutdown()
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# Cleanup messaging
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logger.info("Cleaning up messaging")
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await cleanup_messaging()
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logger.info("Messaging cleanup completed")
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# Close database connections
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logger.info("Closing database connections")
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await database_manager.close_connections()
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logger.info("Training Service shutdown completed")
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logger.info("Database connections closed")
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except Exception as e:
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logger.error("Error during shutdown", error=str(e))
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logger.info("Training Service shutdown completed")
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# Create FastAPI app with lifespan
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# Create FastAPI application with lifespan
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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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title="Bakery Training Service",
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description="ML training service for bakery demand forecasting",
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version="1.0.0",
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docs_url="/docs" if settings.DEBUG else None,
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redoc_url="/redoc" if settings.DEBUG else None,
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docs_url="/docs",
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redoc_url="/redoc",
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lifespan=lifespan
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)
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# Initialize app state
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app.state.ready = False
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# Security middleware
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if not settings.DEBUG:
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app.add_middleware(
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TrustedHostMiddleware,
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allowed_hosts=["localhost", "127.0.0.1", "training-service", "*.bakery-forecast.local"]
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)
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# CORS middleware
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# Add middleware
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"] if settings.DEBUG else [
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"http://localhost:3000",
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"http://localhost:8000",
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"https://dashboard.bakery-forecast.es"
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],
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allow_origins=settings.CORS_ORIGINS_LIST,
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allow_credentials=True,
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allow_methods=["GET", "POST", "PUT", "DELETE", "OPTIONS"],
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# Request logging middleware
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app.add_middleware(
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TrustedHostMiddleware,
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allowed_hosts=settings.ALLOWED_HOSTS
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)
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# Request middleware for logging and metrics
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@app.middleware("http")
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async def log_requests(request: Request, call_next):
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"""Log all incoming requests with timing"""
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async def process_request(request: Request, call_next):
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"""Process requests with logging and metrics"""
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start_time = asyncio.get_event_loop().time()
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# Log request
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logger.info(
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"Request started",
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method=request.method,
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path=request.url.path,
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client_ip=request.client.host if request.client else "unknown"
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)
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# Process request
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try:
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response = await call_next(request)
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# Calculate duration
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duration = asyncio.get_event_loop().time() - start_time
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# Log response
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logger.info(
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"Request completed",
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method=request.method,
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@@ -189,19 +177,18 @@ async def global_exception_handler(request: Request, exc: Exception):
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}
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)
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# Include API routers
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# Include API routers - NO AUTH DEPENDENCIES HERE
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# Authentication is handled by API Gateway
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app.include_router(
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training.router,
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prefix="/training",
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tags=["training"],
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dependencies=[require_auth] if not settings.DEBUG else []
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tags=["training"]
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)
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app.include_router(
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models.router,
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prefix="/models",
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tags=["models"],
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dependencies=[require_auth] if not settings.DEBUG else []
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prefix="/models",
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tags=["models"]
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)
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# Health check endpoints
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@@ -217,66 +204,32 @@ async def health_check():
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@app.get("/health/ready")
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async def readiness_check():
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"""Kubernetes readiness probe"""
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if not app.state.ready:
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"""Kubernetes readiness probe endpoint"""
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checks = {
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"database": await get_db_health(),
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"application": getattr(app.state, 'ready', False)
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}
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if all(checks.values()):
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return {"status": "ready", "checks": checks}
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else:
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return JSONResponse(
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status_code=503,
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content={"status": "not_ready", "message": "Service is starting up"}
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content={"status": "not ready", "checks": checks}
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)
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return {"status": "ready", "service": "training-service"}
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@app.get("/health/live")
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async def liveness_check():
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"""Kubernetes liveness probe"""
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# Check database connectivity
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try:
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db_healthy = await get_db_health()
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if not db_healthy:
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return JSONResponse(
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status_code=503,
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content={"status": "unhealthy", "reason": "database_unavailable"}
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)
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except Exception as e:
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logger.error("Database health check failed", error=str(e))
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return JSONResponse(
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status_code=503,
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content={"status": "unhealthy", "reason": "database_error"}
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)
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return {"status": "alive", "service": "training-service"}
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@app.get("/metrics")
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async def get_metrics():
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"""Expose service metrics"""
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return {
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"training_jobs_active": metrics_collector.get_gauge("training_jobs_active", 0),
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"training_jobs_completed": metrics_collector.get_counter("training_jobs_completed", 0),
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"training_jobs_failed": metrics_collector.get_counter("training_jobs_failed", 0),
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"models_trained_total": metrics_collector.get_counter("models_trained_total", 0),
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"uptime_seconds": metrics_collector.get_gauge("uptime_seconds", 0)
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}
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"""Prometheus metrics endpoint"""
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if hasattr(app.state, 'metrics_collector'):
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return app.state.metrics_collector.get_metrics()
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return {"status": "metrics not available"}
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@app.get("/")
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async def root():
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"""Root endpoint with service information"""
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return {
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"service": "training-service",
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"version": "1.0.0",
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"description": "ML model training service for bakery demand forecasting",
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"docs": "/docs" if settings.DEBUG else "Documentation disabled in production",
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"health": "/health"
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}
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# Development server configuration
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if __name__ == "__main__":
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uvicorn.run(
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"app.main:app",
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host="0.0.0.0",
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port=8000,
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port=settings.PORT,
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reload=settings.DEBUG,
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log_level=settings.LOG_LEVEL.lower(),
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access_log=settings.DEBUG,
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server_header=False,
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date_header=False
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log_level=settings.LOG_LEVEL.lower()
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)
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