Update handler.py

handler.py

@@ -180,12 +180,18 @@ class EndpointHandler:
             emotion = item.get("emotion", "")
             conversation_history = item.get("conversation_history", [])
 
+            # Log input details
+            logger.info(f"Turn {i}: utterance='{utterance[:50]}...', emotion={emotion}, history_len={len(conversation_history)}")
+            if conversation_history:
+                logger.info(f"  History: {conversation_history}")
+
             if not utterance:
                 logger.warning(f"Empty utterance at index {i}")
                 continue
 
             # Build context with conversation history
             context = self._build_context(utterance, conversation_history)
+            logger.info(f"Built context for turn {i}: '{context}'")
             
             # Format as QA task
             question = self.question_template.format(emotion=emotion)
@@ -244,6 +250,7 @@ class EndpointHandler:
                         "RECCON raw spans (answer, score): %s",
                         [(p.get("answer"), p.get("score", 0.0)) for p in current_preds[:5]]
                     )
+                    logger.info(f"Total predictions received: {len(current_preds)}")
                     
                     def is_good_span(ans: str) -> bool:
                         if not ans:
@@ -258,7 +265,10 @@ class EndpointHandler:
                         return True
                     
                     raw_answers = [p.get("answer", "") for p in current_preds]
+                    logger.info(f"Raw answers before filtering: {raw_answers}")
+                    
                     raw_answers = [a for a in raw_answers if is_good_span(a)]
+                    logger.info(f"Answers after is_good_span filter: {raw_answers}")
                     
                     # Extract context text (part before [TARGET] marker)
                     full_context = contexts[pred_idx]
@@ -291,6 +301,11 @@ class EndpointHandler:
         Clean and filter extracted trigger spans.
         Spans can come from either target_text or context_text.
         """
+        logger.info(f"_clean_spans called with {len(spans)} spans")
+        logger.info(f"  Target: '{target_text}'")
+        logger.info(f"  Context: '{context_text[:100]}...'" if len(context_text) > 100 else f"  Context: '{context_text}'")
+        logger.info(f"  Input spans: {spans}")
+        
         target_text = target_text or ""
         context_text = context_text or ""
         full_text = (context_text + " " + target_text).lower()
@@ -340,14 +355,19 @@ class EndpointHandler:
             })
 
         candidates.sort(key=lambda x: (x["tok_len"], x["char_len"]), reverse=True)
+        logger.info(f"Built {len(candidates)} candidates: {[c['norm'] for c in candidates]}")
+        
         kept_norms = []
         for c in list(candidates):
             n = c["norm"]
             if any(n in kn or kn in n for kn in kept_norms):
                 continue
             kept_norms.append(n)
+        
+        logger.info(f"After dedup: {kept_norms}")
 
         cleaned = [_extract_from_target(target_text, n) for n in kept_norms]
+        logger.info(f"Final cleaned spans: {cleaned}")
 
         if not cleaned and spans:
             tt_tokens = target_lower.split()