checking logs

app.py

@@ -31,6 +31,27 @@ def load_model(model_name):
         print(f"Error loading model {model_name}: {e}")
         return None, None
 
+# def get_sentiment_prediction(text, model, tokenizer):
+#     """Get sentiment prediction from model"""
+#     if model is None:
+#         # Fallback to dummy predictions for demo
+#         return {
+#             "label": "NM",
+#             "probabilities": {"Negative": 0.01, "Neutral": 0.01, "Positive": 0.01}
+#         }
+    
+#     try:
+#         # Build full prompt for analysis
+#         prefix = "Analyze the sentiment of this statement extracted from a financial news article. Provide your answer as either negative, positive, or neutral.. Text: "
+#         suffix = ".. Answer: "
+#         full_prompt = f"{prefix}{text}{suffix}"
+#         # Added a small comment here.
+#         result = model.generate(prompt=text)
+#         return result
+#     except Exception as e:
+#         print(f"Error in prediction: {e}")
+#         return {"label": "NA", "probabilities": {"Negative": 0.0, "Neutral": 0.0, "Positive": 0.0}}
+
 def get_sentiment_prediction(text, model, tokenizer):
     """Get sentiment prediction from model"""
     if model is None:
@@ -41,17 +62,28 @@ def get_sentiment_prediction(text, model, tokenizer):
         }
     
     try:
-        # # Build full prompt for analysis
-        # prefix = "Analyze the sentiment of this statement extracted from a financial news article. Provide your answer as either negative, positive, or neutral.. Text: "
-        # suffix = ".. Answer: "
-        # full_prompt = f"{prefix}{text}{suffix}"
-        # # Added a small comment here.
+        # Print model's label mapping for debugging
+        if hasattr(model, 'id2label'):
+            print(f"[DEBUG] Model id2label mapping: {model.id2label}")
+        
         result = model.generate(prompt=text)
+        
+        # Print raw prediction
+        print(f"[DEBUG] Text (first 50 chars): {text[:50]}")
+        print(f"[DEBUG] Raw prediction: {result}")
+        
+        # Normalize label to title case to match your offline results
+        if 'label' in result:
+            original_label = result['label']
+            normalized_label = original_label.capitalize()
+            result['label'] = normalized_label
+            print(f"[DEBUG] Normalized label: {original_label} → {normalized_label}")
+        
         return result
     except Exception as e:
         print(f"Error in prediction: {e}")
         return {"label": "NA", "probabilities": {"Negative": 0.0, "Neutral": 0.0, "Positive": 0.0}}
-
+    
 def calculate_distances(orig_probs, mut_probs):
     """Calculate Jensen-Shannon distance and Cosine similarity"""
     try: