merge: update everything to v5

ai/careers/app/main.py

@@ -5,7 +5,7 @@
 for intelligent career path recommendations based on user profiles.
 
 Author: Aspira AI Team
-Version: 2.0.0
+Version: 4.0.0
 License: GNU aGPL v3.0
 """
 
@@ -22,6 +22,9 @@
 from fastapi.exceptions import RequestValidationError
 from starlette.exceptions import HTTPException as StarletteHTTPException
 
+from app.config import settings
+from app.routes.career_routes import router as career_router
+
 # Configure logging
 LOG_DIR = Path("logs")
 LOG_DIR.mkdir(exist_ok=True)
@@ -43,73 +46,101 @@
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
-    """
-    Lifespan context manager for startup and shutdown events.
+    """Lifespan context manager for startup and shutdown events.
+
     Pre-loads the ML model to avoid cold starts and improve response times.
     """
-    logger.info("="*70)
+    logger.info("=" * 70)
     logger.info("ASPIRA AI CAREER RECOMMENDATION SYSTEM")
     logger.info("ML-Powered Random Forest Classifier v2.0.0")
-    logger.info("="*70)
-    
+    logger.info("=" * 70)
+
     '''
     # Create necessary directories
     LOG_DIR.mkdir(exist_ok=True)
     Path("app/models").mkdir(exist_ok=True)
     '''
-    
+
     # Pre-load ML model at startup
     try:
         from app.routes.career_routes import get_model
         logger.info("Pre-loading Random Forest model...")
-        
+
         start_time = time.time()
         model = get_model()
         load_time = time.time() - start_time
-        
+
         logger.info(f"✓ Model loaded successfully in {load_time:.2f}s")
-        logger.info(f"  - Model version: {model.config.get('model_version', '2.0.0')}")
-        logger.info(f"  - Careers available: {len(model.label_encoder.classes_)}")
+        logger.info(
+            f"  - Model version: "
+            f"{model.config.get('model_version', '2.0.0')}"
+        )
+        logger.info(
+            f"  - Careers available: "
+            f"{len(model.label_encoder.classes_)}"
+        )
         logger.info(f"  - Features: {len(model.feature_columns)}")
-        logger.info(f"  - Trees in forest: {model.model.n_estimators}")
+
+        # Handle both calibrated and non-calibrated models
+        try:
+            # If it's a CalibratedClassifierCV, access the base estimator
+            # CalibratedClassifierCV uses base_estimator (older sklearn)
+            # or estimator_ (newer sklearn)
+            if hasattr(model.model, 'base_estimator'):
+                n_estimators = model.model.base_estimator.n_estimators
+            elif hasattr(model.model, 'estimator_'):
+                n_estimators = model.model.estimator_.n_estimators
+            elif hasattr(model.model, 'n_estimators'):
+                n_estimators = model.model.n_estimators
+            else:
+                n_estimators = "N/A"
+            logger.info(f"  - Trees in forest: {n_estimators}")
+        except AttributeError:
+            logger.info(f"  - Model type: {type(model.model).__name__}")
+
         logger.info("✓ API ready to accept requests")
-        
+
         # Store model reference in app state
         app.state.model_loaded = True
-        app.state.model_version = model.config.get('model_version', '2.0.0')
+        app.state.model_version = model.config.get(
+            'model_version', '2.0.0'
+        )
         app.state.careers_count = len(model.label_encoder.classes_)
         app.state.features_count = len(model.feature_columns)
-        
+
     except FileNotFoundError as e:
         logger.error(f"✗ Model file not found: {str(e)}")
-        logger.error("  Please ensure career_recommender.pkl exists in the models directory")
+        logger.error(
+            "  Please ensure career_recommender.pkl exists in the "
+            "models directory"
+        )
         app.state.model_loaded = False
         app.state.error_message = "Model file not found"
-        
+
     except Exception as e:
         logger.error(f"✗ Failed to pre-load model: {str(e)}", exc_info=True)
-        logger.warning("  API will attempt to load model on first request")
+        logger.warning(
+            "  API will attempt to load model on first request"
+        )
         app.state.model_loaded = False
         app.state.error_message = str(e)
-    
-    logger.info("="*70)
+
+    logger.info("=" * 70)
     logger.info("Server startup complete")
-    logger.info("="*70 + "\n")
-    
+    logger.info("=" * 70 + "\n")
+
     yield
-    
+
     # Cleanup on shutdown
-    logger.info("\n" + "="*70)
+    logger.info("\n" + "=" * 70)
     logger.info("Shutting down Aspira AI Career Recommendation System")
-    logger.info("="*70)
+    logger.info("=" * 70)
 
 
 # ============================================================================
 # FASTAPI APPLICATION INITIALIZATION
 # ============================================================================
 
-from app.config import settings
-
 app = FastAPI(
     title=settings.API_TITLE,
     description=settings.API_DESCRIPTION,
@@ -161,20 +192,20 @@ async def add_process_time_header(request: Request, call_next):
 async def log_requests(request: Request, call_next):
     """Log all incoming requests with timing information."""
     start_time = time.time()
-    
+
     # Log request
     logger.info(f"→ {request.method} {request.url.path}")
-    
+
     # Process request
     response = await call_next(request)
-    
+
     # Log response with timing
     duration = time.time() - start_time
     logger.info(
         f"← {response.status_code} {request.url.path} "
         f"({duration*1000:.2f}ms)"
     )
-    
+
     return response
 
 
@@ -183,18 +214,22 @@ async def log_requests(request: Request, call_next):
 # ============================================================================
 
 @app.exception_handler(RequestValidationError)
-async def validation_exception_handler(request: Request, exc: RequestValidationError):
+async def validation_exception_handler(
+    request: Request, exc: RequestValidationError
+):
     """Handle Pydantic validation errors with detailed messages."""
-    logger.warning(f"Validation error on {request.url.path}: {exc.errors()}")
-
+    logger.warning(
+        f"Validation error on {request.url.path}: {exc.errors()}"
+    )
+
     errors = []
     for error in exc.errors():
         errors.append({
             "field": " -> ".join(str(loc) for loc in error["loc"]),
             "message": error["msg"],
             "type": error["type"]
         })
-    
+
     return JSONResponse(
         status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,
         content={
@@ -207,10 +242,14 @@ async def validation_exception_handler(request: Request, exc: RequestValidationE
 
 
 @app.exception_handler(StarletteHTTPException)
-async def http_exception_handler(request: Request, exc: StarletteHTTPException):
+async def http_exception_handler(
+    request: Request, exc: StarletteHTTPException
+):
     """Handle HTTP exceptions with consistent format."""
-    logger.error(f"HTTP {exc.status_code} on {request.url.path}: {exc.detail}")
-
+    logger.error(
+        f"HTTP {exc.status_code} on {request.url.path}: {exc.detail}"
+    )
+
     return JSONResponse(
         status_code=exc.status_code,
         content={
@@ -228,7 +267,7 @@ async def general_exception_handler(request: Request, exc: Exception):
         f"Unexpected error on {request.url.path}: {str(exc)}",
         exc_info=True
     )
-    
+
     return JSONResponse(
         status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
         content={
@@ -243,8 +282,6 @@ async def general_exception_handler(request: Request, exc: Exception):
 # ROUTE REGISTRATION
 # ============================================================================
 
-from app.routes.career_routes import router as career_router
-
 app.include_router(career_router)
 
 
@@ -254,9 +291,8 @@ async def general_exception_handler(request: Request, exc: Exception):
 
 @app.get("/", tags=["Root"])
 async def root() -> Dict[str, Any]:
-    """
-    Root endpoint providing service information and status.
-
+    """Root endpoint providing service information and status.
+
     Returns basic information about the API service, version,
     and available endpoints.
     """
@@ -285,15 +321,19 @@ async def root() -> Dict[str, Any]:
 
 @app.get("/health", tags=["Health"])
 async def health_check() -> Dict[str, Any]:
-    """
-    Health check endpoint for monitoring and load balancers.
-
+    """Health check endpoint for monitoring and load balancers.
+
     Returns the current health status of the service and model.
     Used by Docker healthcheck and orchestration systems.
     """
-    model_status = "healthy" if getattr(app.state, 'model_loaded', False) else "unhealthy"
-    overall_status = "healthy" if model_status == "healthy" else "degraded"
-
+    model_status = (
+        "healthy" if getattr(app.state, 'model_loaded', False)
+        else "unhealthy"
+    )
+    overall_status = (
+        "healthy" if model_status == "healthy" else "degraded"
+    )
+
     health_info = {
         "status": overall_status,
         "service": "aspira-career-recommendation",
@@ -305,15 +345,17 @@ async def health_check() -> Dict[str, Any]:
         },
         "timestamp": time.time()
     }
-    
+
     if not getattr(app.state, 'model_loaded', False):
-        health_info["error"] = getattr(app.state, 'error_message', "Model not loaded")
-
+        health_info["error"] = getattr(
+            app.state, 'error_message', "Model not loaded"
+        )
+
     status_code = (
-        status.HTTP_200_OK if overall_status == "healthy" 
+        status.HTTP_200_OK if overall_status == "healthy"
         else status.HTTP_503_SERVICE_UNAVAILABLE
     )
-    
+
     return JSONResponse(
         status_code=status_code,
         content=health_info
@@ -322,9 +364,8 @@ async def health_check() -> Dict[str, Any]:
 
 @app.get("/version", tags=["Root"])
 async def version_info() -> Dict[str, Any]:
-    """
-    Detailed version information endpoint.
-
+    """Detailed version information endpoint.
+
     Returns comprehensive version and configuration details
     about the service and ML model.
     """
@@ -353,9 +394,8 @@ async def version_info() -> Dict[str, Any]:
 
 @app.get("/status", tags=["Root"])
 async def detailed_status() -> Dict[str, Any]:
-    """
-    Detailed service status endpoint.
-
+    """Detailed service status endpoint.
+
     Provides comprehensive status information including
     uptime, configuration, and operational metrics.
     """
@@ -395,17 +435,17 @@ async def detailed_status() -> Dict[str, Any]:
 
 if __name__ == "__main__":
     import uvicorn
-    
+
     logger.info("Starting Aspira AI Career Recommendation System")
     logger.info(f"Environment: {settings.ENV}")
     logger.info(f"Debug mode: {settings.DEBUG}")
     logger.info(f"Log level: {settings.LOG_LEVEL}")
-    
+
     uvicorn.run(
         "app.main:app",
         host="0.0.0.0",
         port=8000,
         reload=settings.DEBUG,
         log_level=settings.LOG_LEVEL.lower(),
         access_log=True
-    )
+    )