Revert to original code that worked for manual upload with minimal changes

app.py

@@ -46,64 +46,20 @@ def detect_warning_lamp(image, history: list[tuple[str, str]], system_message):
         return history
         
     try:
-        # Print debug info
-        print(f"Image type: {type(image)}")
-        
         # Convert PIL image to FastAI compatible format
         img = PILImage(image)
-        print(f"Converted to PILImage: {type(img)}")
         
         # Get model prediction
-        print("Running prediction...")
         pred_class, pred_idx, probs = learn_inf.predict(img)
         
-        # Print debug info about prediction results
-        print(f"Prediction class type: {type(pred_class)}, value: {pred_class}")
-        print(f"Prediction index type: {type(pred_idx)}, value: {pred_idx}")
-        print(f"Probabilities type: {type(probs)}, shape: {probs.shape if hasattr(probs, 'shape') else 'no shape'}")
-        
-        # Safely convert tensors to Python types
-        try:
-            # Handle pred_class (could be string or tensor)
-            if hasattr(pred_class, 'item'):
-                pred_class_str = str(pred_class.item())
-            else:
-                pred_class_str = str(pred_class)
-                
-            # Handle pred_idx (convert tensor to int)
-            if hasattr(pred_idx, 'item'):
-                pred_idx_int = pred_idx.item()
-            else:
-                pred_idx_int = int(pred_idx)
-                
-            # Get confidence score for predicted class
-            if hasattr(probs[pred_idx_int], 'item'):
-                confidence = probs[pred_idx_int].item()
-            else:
-                confidence = float(probs[pred_idx_int])
-                
-            print(f"Converted values - class: {pred_class_str}, index: {pred_idx_int}, confidence: {confidence}")
-        except Exception as conversion_error:
-            print(f"Error during tensor conversion: {conversion_error}")
-            raise
-        
         # Format the prediction results
-        response = f"Detected Warning Lamp: {pred_class_str}\nConfidence: {confidence:.2%}"
+        confidence = float(probs[pred_idx])  # Convert to float for better formatting
+        response = f"Detected Warning Lamp: {pred_class}\nConfidence: {confidence:.2%}"
         
         # Add probabilities for all classes
         response += "\n\nProbabilities for all classes:"
-        
-        # Safely iterate through probabilities
-        for i, cls in enumerate(learn_inf.dls.vocab):
-            try:
-                if hasattr(probs[i], 'item'):
-                    prob_value = probs[i].item()
-                else:
-                    prob_value = float(probs[i])
-                response += f"\n- {cls}: {prob_value:.2%}"
-            except Exception as prob_error:
-                print(f"Error processing probability for class {cls}: {prob_error}")
-                response += f"\n- {cls}: Error"
+        for i, (cls, prob) in enumerate(zip(learn_inf.dls.vocab, probs)):
+            response += f"\n- {cls}: {float(prob):.2%}"
             
         # Update chat history
         history.append((None, response))
@@ -111,8 +67,6 @@ def detect_warning_lamp(image, history: list[tuple[str, str]], system_message):
     except Exception as e:
         error_msg = f"Error processing image: {str(e)}"
         print(f"Exception in detect_warning_lamp: {e}")
-        import traceback
-        traceback.print_exc()
         history.append((None, error_msg))
         return history