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src/social_sphere_llm/unified_prediction_service.py

@@ -120,30 +120,23 @@ class UnifiedSocialMediaPredictionService:
     def _load_addicted_model(self):
         """Load the addicted score regression model from Notebook 08."""
         try:
-            # Try to load from MLflow first
-            try:
-                model_uri = "models:/addicted_score_regressor/latest"
-                self.addicted_model = mlflow.sklearn.load_model(model_uri)
-                logger.info(f"✅ Loaded addicted model from MLflow: {model_uri}")
-            except Exception as e:
-                logger.warning(f"⚠️ Could not load addicted model from MLflow: {e}")
-                # Try local paths
-                model_paths = [
-                    'models/addicted_score_model.joblib',
-                    '../models/addicted_score_model.joblib',
-                    'notebooks/models/addicted_score_model.joblib'
-                ]
-                loaded = False
-                for path in model_paths:
-                    try:
-                        self.addicted_model = joblib.load(path)
-                        logger.info(f"✅ Loaded addicted model from: {path}")
-                        loaded = True
-                        break
-                    except Exception as e2:
-                        logger.warning(f"⚠️ Could not load addicted model from {path}: {e2}")
-                if not loaded:
-                    logger.error("❌ Failed to load addicted score model from all known paths.")
+            # Only use local joblib files for Gradio Spaces compatibility
+            model_paths = [
+                'models/addicted_score_model.joblib',
+                '../models/addicted_score_model.joblib',
+                'notebooks/models/addicted_score_model.joblib'
+            ]
+            loaded = False
+            for path in model_paths:
+                try:
+                    self.addicted_model = joblib.load(path)
+                    logger.info(f"✅ Loaded addicted model from: {path}")
+                    loaded = True
+                    break
+                except Exception as e2:
+                    logger.warning(f"⚠️ Could not load addicted model from {path}: {e2}")
+            if not loaded:
+                logger.error("❌ Failed to load addicted score model from all known paths.")
             # Load scaler
             scaler_paths = [
                 'models/addicted_score_scaler.joblib',