new update

app.py

@@ -6,8 +6,7 @@ from config import get_config
 migrate = Migrate()
 
 def create_flask_app():
-    app = create_app()
-    app.config.from_object(get_config())
+    app = create_app(get_config())
     migrate.init_app(app, db)
     return app
 

app/services/ai_processor.py

@@ -12,16 +12,15 @@ class ProcessingError(Exception):
 
 class AIPipeline:
     def __init__(self):
+        self.nlp = None  # Initialize as None
+        self.detector = None
         try:
-            self.nlp = pipeline("text-classification", model="roberta-base")
-            
             model_dir = Path("app/models")
             weights_path = model_dir / "yolov4.weights"
             config_path = model_dir / "yolov4.cfg"
             
             if not (weights_path.exists() and config_path.exists()):
                 logger.warning("YOLOv4 files not found. Please run setup_yolo.py first.")
-                self.detector = None
             else:
                 self.detector = cv2.dnn.readNet(str(weights_path), str(config_path))
                 
@@ -29,6 +28,17 @@ class AIPipeline:
             logger.error(f"Error initializing AI Pipeline: {e}")
             raise
 
+    def _ensure_nlp_loaded(self):
+        """Ensure NLP model is loaded before use."""
+        if self.nlp is None:
+            try:
+                logger.info("Loading NLP model...")
+                self.nlp = pipeline("text-classification", model="roberta-base")
+                logger.info("NLP model loaded successfully")
+            except Exception as e:
+                logger.error(f"Error loading NLP model: {e}")
+                raise
+
     def process_ad(self, ad):
         if not ad:
             raise ValueError("Ad content cannot be empty")
@@ -45,8 +55,11 @@ class AIPipeline:
             raise ProcessingError(f"Failed to process ad: {str(e)}")
 
     def _analyze_sentiment(self, text):
+        if not text:
+            return None
         try:
-            return self.nlp(text)[0] if text else None
+            self._ensure_nlp_loaded()  # Load model if needed
+            return self.nlp(text)[0]
         except Exception as e:
             logger.error(f"Sentiment analysis error: {e}")
             return None