Fix syntax error: correct indentation for try-except block in run_training_cycle

scripts/feedback_learning_pipeline.py

@@ -279,86 +279,86 @@ class FeedbackLearningPipeline:
                     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)
-        
-        if not feedback_logs:
-            print("[Feedback Pipeline] No new feedback available")
-            return {
-                "status": "no_feedback",
-                "message": "No new feedback to process"
-            }
-        
-        # Extract corrections
-        corrections = self.extract_corrected_annotations(feedback_logs)
-        
-        if not corrections:
-            print("[Feedback Pipeline] No valid corrections found")
-            return {
-                "status": "no_corrections",
-                "message": "No valid corrections extracted"
-            }
-        
-        # Analyze patterns
-        patterns = self.calculate_correction_patterns(corrections)
-        
-        # Apply adjustments
-        self.apply_model_adjustments(patterns)
-        
-        # Update training state
-        self.training_state["last_training_time"] = datetime.now().isoformat()
-        self.training_state["last_processed_feedback_id"] = feedback_logs[-1].get("created_at")
-        self.training_state["total_feedback_processed"] += len(corrections)
-        self.training_state["training_runs"].append({
-            "timestamp": datetime.now().isoformat(),
-            "corrections_processed": len(corrections),
-            "patterns": patterns
-        })
-        
-        self._save_training_state()
-        
-        # Capture model state AFTER training
-        after_state = None
-        training_cycle_id = None
-        if self.version_tracker and before_state:
-            try:
-                after_state = self.version_tracker.get_current_model_state()
-                self.version_tracker.log_model_version(after_state)
-                if after_state and isinstance(after_state, dict):
-                    print(f"[Model Versioning] Captured state after training: {after_state.get('version_id', 'unknown')[:8]}...")
-                
-                # Only log training cycle if both states are valid dictionaries
-                if (before_state and isinstance(before_state, dict) and 
-                    after_state and isinstance(after_state, dict)):
-                    # Log the training cycle with before/after comparison
-                    training_cycle_id = self.version_tracker.log_training_cycle(
-                        before_state=before_state,
-                        after_state=after_state,
-                        feedback_count=len(corrections),
-                        patterns=patterns,
-                        performance_metrics=None  # TODO: Calculate actual metrics
-                    )
+            
+            # Fetch new feedback
+            feedback_logs = self.fetch_new_feedback(limit=1000)
+            
+            if not feedback_logs:
+                print("[Feedback Pipeline] No new feedback available")
+                return {
+                    "status": "no_feedback",
+                    "message": "No new feedback to process"
+                }
+            
+            # Extract corrections
+            corrections = self.extract_corrected_annotations(feedback_logs)
+            
+            if not corrections:
+                print("[Feedback Pipeline] No valid corrections found")
+                return {
+                    "status": "no_corrections",
+                    "message": "No valid corrections extracted"
+                }
+            
+            # Analyze patterns
+            patterns = self.calculate_correction_patterns(corrections)
+            
+            # Apply adjustments
+            self.apply_model_adjustments(patterns)
+            
+            # Update training state
+            self.training_state["last_training_time"] = datetime.now().isoformat()
+            self.training_state["last_processed_feedback_id"] = feedback_logs[-1].get("created_at")
+            self.training_state["total_feedback_processed"] += len(corrections)
+            self.training_state["training_runs"].append({
+                "timestamp": datetime.now().isoformat(),
+                "corrections_processed": len(corrections),
+                "patterns": patterns
+            })
+            
+            self._save_training_state()
+            
+            # Capture model state AFTER training
+            after_state = None
+            training_cycle_id = None
+            if self.version_tracker and before_state:
+                try:
+                    after_state = self.version_tracker.get_current_model_state()
+                    self.version_tracker.log_model_version(after_state)
+                    if after_state and isinstance(after_state, dict):
+                        print(f"[Model Versioning] Captured state after training: {after_state.get('version_id', 'unknown')[:8]}...")
                     
-                    if training_cycle_id:
-                        print(f"[Training History] Logged training cycle: {training_cycle_id[:8]}...")
-                else:
-                    print(f"[Training History] Skipping cycle logging - invalid state data")
-            except Exception as e:
-                print(f"[Model Versioning] Error logging version: {e}")
-        
-        print(f"[Feedback Pipeline] Training cycle completed successfully")
-        print(f"[Feedback Pipeline] Total feedback processed: {self.training_state['total_feedback_processed']}")
-        
-        # Helper to safely extract version_id whether state is a dict, str, or None
-        def _extract_version_id(state):
-            if not state:
+                    # Only log training cycle if both states are valid dictionaries
+                    if (before_state and isinstance(before_state, dict) and 
+                        after_state and isinstance(after_state, dict)):
+                        # Log the training cycle with before/after comparison
+                        training_cycle_id = self.version_tracker.log_training_cycle(
+                            before_state=before_state,
+                            after_state=after_state,
+                            feedback_count=len(corrections),
+                            patterns=patterns,
+                            performance_metrics=None  # TODO: Calculate actual metrics
+                        )
+                        
+                        if training_cycle_id:
+                            print(f"[Training History] Logged training cycle: {training_cycle_id[:8]}...")
+                    else:
+                        print(f"[Training History] Skipping cycle logging - invalid state data")
+                except Exception as e:
+                    print(f"[Model Versioning] Error logging version: {e}")
+            
+            print(f"[Feedback Pipeline] Training cycle completed successfully")
+            print(f"[Feedback Pipeline] Total feedback processed: {self.training_state['total_feedback_processed']}")
+            
+            # Helper to safely extract version_id whether state is a dict, str, or None
+            def _extract_version_id(state):
+                if not state:
+                    return None
+                if isinstance(state, dict):
+                    return state.get("version_id")
+                if isinstance(state, str):
+                    return state
                 return None
-            if isinstance(state, dict):
-                return state.get("version_id")
-            if isinstance(state, str):
-                return state
-            return None
 
             return {
                 "status": "success",