Add comprehensive error handling and traceback logging to identify str.get() error source

app.py

@@ -41,6 +41,11 @@ def automated_training_check():
             # Trigger training
             results = run_feedback_training(feedback_pipeline)
             
+            # Validate results is a dictionary
+            if not isinstance(results, dict):
+                print(f"[Automated Training] Error: Expected dict, got {type(results)}: {results}")
+                return
+            
             if results.get("status") == "success":
                 print(f"[Automated Training] ✓ Training completed successfully")
                 print(f"[Automated Training] Processed {results.get('corrections_processed')} corrections")

scripts/feedback_learning_pipeline.py

@@ -264,20 +264,21 @@ class FeedbackLearningPipeline:
         """
         Execute a full training cycle: fetch feedback, analyze patterns, apply adjustments
         """
-        print(f"\n[Feedback Pipeline] Starting training cycle at {datetime.now()}")
-        
-        # Capture model state BEFORE training
-        before_state = None
-        if self.version_tracker:
-            try:
-                before_state = self.version_tracker.get_current_model_state()
-                self.version_tracker.log_model_version(before_state)
-                if before_state and isinstance(before_state, dict):
-                    print(f"[Model Versioning] Captured state before training: {before_state.get('version_id', 'unknown')[:8]}...")
-            except Exception as e:
-                print(f"[Model Versioning] Warning: Could not capture before state: {e}")
-                before_state = None  # Reset to None on error
-                self.version_tracker = None  # Disable versioning for this run
+        try:
+            print(f"\n[Feedback Pipeline] Starting training cycle at {datetime.now()}")
+            
+            # Capture model state BEFORE training
+            before_state = None
+            if self.version_tracker:
+                try:
+                    before_state = self.version_tracker.get_current_model_state()
+                    self.version_tracker.log_model_version(before_state)
+                    if before_state and isinstance(before_state, dict):
+                        print(f"[Model Versioning] Captured state before training: {before_state.get('version_id', 'unknown')[:8]}...")
+                except Exception as e:
+                    print(f"[Model Versioning] Warning: Could not capture before state: {e}")
+                    before_state = None  # Reset to None on error
+                    self.version_tracker = None  # Disable versioning for this run
         
         # Fetch new feedback
         feedback_logs = self.fetch_new_feedback(limit=1000)
@@ -359,15 +360,26 @@ class FeedbackLearningPipeline:
                 return state
             return None
 
-        return {
-            "status": "success",
-            "corrections_processed": len(corrections),
-            "patterns": patterns,
-            "total_feedback_processed": self.training_state["total_feedback_processed"],
-            "before_version_id": _extract_version_id(before_state),
-            "after_version_id": _extract_version_id(after_state),
-            "training_cycle_id": training_cycle_id
-        }
+            return {
+                "status": "success",
+                "corrections_processed": len(corrections),
+                "patterns": patterns,
+                "total_feedback_processed": self.training_state["total_feedback_processed"],
+                "before_version_id": _extract_version_id(before_state),
+                "after_version_id": _extract_version_id(after_state),
+                "training_cycle_id": training_cycle_id
+            }
+            
+        except Exception as e:
+            print(f"[Feedback Pipeline] CRITICAL ERROR in run_training_cycle: {e}")
+            print(f"[Feedback Pipeline] Error type: {type(e).__name__}")
+            import traceback
+            traceback.print_exc()
+            return {
+                "status": "error",
+                "message": str(e),
+                "error_type": type(e).__name__
+            }
     
     def get_feedback_stats(self) -> Dict[str, Any]:
         """Get statistics about feedback and training"""