final

app.py

@@ -100,7 +100,8 @@ def predict_label(sentence, tokenizer, model1, model2, device):
 
     # Map the predicted index back to the original class label using class_names
     class_names = list(common_label_map.keys())
-    predicted_index = torch.argmax(output, dim=1)
+    
+    predicted_index = torch.argmax(ensemble_outputs, dim=1)
     predicted_label = class_names[predicted_index.item()]
 
     # Create JSON response