code-review-fix task 15.1: replace @app.on_event startup with FastAPI lifespan pattern in main.py

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

services/ml/app/main.py

@@ -11,6 +11,7 @@ import os
 import re
 import threading
 import uuid
+from contextlib import asynccontextmanager
 from pathlib import Path
 from typing import Optional, Dict, Any, List
 from datetime import datetime
@@ -57,11 +58,97 @@ async def verify_api_key(x_api_key: str = Header(default="")):
         raise HTTPException(status_code=401, detail="Unauthorized")
 
 
+# --- Lifespan ---
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """
+    FastAPI lifespan context manager.
+    Handles startup and shutdown logic.
+    """
+    # --- Startup ---
+    logger.info("Starting inference service...")
+
+    # Ensure training_runs table exists
+    init_db()
+
+    # Mark any stale "running" records as failed — they belong to a previous
+    # process and will never complete.
+    try:
+        with get_db() as db:
+            stmt = (
+                sa_update(TrainingRun)
+                .where(TrainingRun.status == "running")
+                .values(
+                    status="failed",
+                    completed_at=datetime.utcnow(),
+                    metrics_summary={"error": "Service restarted while training was in progress"},
+                )
+            )
+            result = db.execute(stmt)
+            db.commit()
+            if result.rowcount:
+                logger.warning(
+                    f"Marked {result.rowcount} stale training run(s) as failed on startup"
+                )
+    except Exception as exc:
+        logger.error(f"Failed to clean up stale training runs: {exc}")
+
+    # Load pipeline config
+    config_path = Path("config/pipeline.yaml")
+    if config_path.exists():
+        try:
+            state.pipeline_config = load_config(config_path)
+            logger.info(f"Loaded pipeline config from {config_path}")
+
+            # Load model based on config
+            inference_config = state.pipeline_config.stages.inference
+
+            if not inference_config.enabled:
+                logger.warning("Inference stage is disabled in config")
+            else:
+                # Load model
+                try:
+                    if inference_config.model_source == "mlflow":
+                        # Configure MLflow tracking URI
+                        mlflow.set_tracking_uri(state.pipeline_config.stages.training.mlflow.tracking_uri)
+
+                        state.model, state.model_info = load_model_from_mlflow(
+                            model_name=inference_config.mlflow_model_name,
+                            stage=inference_config.mlflow_model_stage
+                        )
+                    elif inference_config.model_source == "local":
+                        state.model, state.model_info = load_model_from_local(
+                            model_path=inference_config.local_model_path
+                        )
+                    else:
+                        logger.error(f"Unknown model_source: {inference_config.model_source}")
+
+                    logger.info("Model loaded successfully")
+                    logger.info(f"Model info: {state.model_info['model_name']} "
+                               f"v{state.model_info['model_version']} "
+                               f"({state.model_info['model_type']})")
+
+                except Exception as e:
+                    logger.error(f"Failed to load model: {e}")
+                    logger.warning("Service will start without a model. Use /health to check status.")
+
+        except Exception as e:
+            logger.error(f"Failed to load pipeline config: {e}")
+    else:
+        logger.warning(f"Config file not found: {config_path}. Using defaults.")
+
+    yield
+
+    # --- Shutdown (nothing to do currently) ---
+
+
 # FastAPI app
 app = FastAPI(
     title="Candle Pattern Inference API",
     description="ML inference service for candlestick pattern recognition",
-    version="1.0.0"
+    version="1.0.0",
+    lifespan=lifespan
 )
 
 # Parse CORS origins from environment variable or use default
@@ -391,88 +478,6 @@ def load_model_from_local(model_path: str) -> tuple[Any, Dict[str, Any]]:
         raise
 
 
-# --- Startup Event ---
-
-@app.on_event("startup")
-async def startup_event():
-    """
-    Load model and pipeline config on startup.
-    """
-    logger.info("Starting inference service...")
-
-    # Ensure training_runs table exists
-    init_db()
-
-    # Mark any stale "running" records as failed — they belong to a previous
-    # process and will never complete.
-    try:
-        with get_db() as db:
-            stmt = (
-                sa_update(TrainingRun)
-                .where(TrainingRun.status == "running")
-                .values(
-                    status="failed",
-                    completed_at=datetime.utcnow(),
-                    metrics_summary={"error": "Service restarted while training was in progress"},
-                )
-            )
-            result = db.execute(stmt)
-            db.commit()
-            if result.rowcount:
-                logger.warning(
-                    f"Marked {result.rowcount} stale training run(s) as failed on startup"
-                )
-    except Exception as exc:
-        logger.error(f"Failed to clean up stale training runs: {exc}")
-    
-    # Load pipeline config
-    config_path = Path("config/pipeline.yaml")
-    if not config_path.exists():
-        logger.warning(f"Config file not found: {config_path}. Using defaults.")
-        return
-    
-    try:
-        state.pipeline_config = load_config(config_path)
-        logger.info(f"Loaded pipeline config from {config_path}")
-    except Exception as e:
-        logger.error(f"Failed to load pipeline config: {e}")
-        return
-    
-    # Load model based on config
-    inference_config = state.pipeline_config.stages.inference
-    
-    if not inference_config.enabled:
-        logger.warning("Inference stage is disabled in config")
-        return
-    
-    # Load model
-    try:
-        if inference_config.model_source == "mlflow":
-            # Configure MLflow tracking URI
-            mlflow.set_tracking_uri(state.pipeline_config.stages.training.mlflow.tracking_uri)
-            
-            state.model, state.model_info = load_model_from_mlflow(
-                model_name=inference_config.mlflow_model_name,
-                stage=inference_config.mlflow_model_stage
-            )
-        elif inference_config.model_source == "local":
-            state.model, state.model_info = load_model_from_local(
-                model_path=inference_config.local_model_path
-            )
-        else:
-            logger.error(f"Unknown model_source: {inference_config.model_source}")
-            return
-        
-        logger.info("Model loaded successfully")
-        logger.info(f"Model info: {state.model_info['model_name']} "
-                   f"v{state.model_info['model_version']} "
-                   f"({state.model_info['model_type']})")
-        
-    except Exception as e:
-        logger.error(f"Failed to load model: {e}")
-        logger.warning("Service will start without a model. Use /health to check status.")
-
-
 # --- Health Check ---
 
 @app.get("/health", response_model=HealthResponse)