kermitting soon

util/app.py

@@ -218,6 +218,11 @@ def predict_champion(player_opgg_url, *champions):
         
         if label_encoder is None:
             return "Label encoder not loaded properly"
+        
+        # Print label encoder information
+        print("\nLabel Encoder Information:")
+        print("Classes in encoder:", label_encoder.classes_)
+        print("Number of classes:", len(label_encoder.classes_))
 
         # Get and process the data
         training_df = get_user_training_df(player_opgg_url)
@@ -259,20 +264,44 @@ def predict_champion(player_opgg_url, *champions):
         print("Starting model prediction...\n")
         predictions = model.predict(dtest)
         print("Model prediction complete\n")
+
+        print("\nPrediction Information:")
+        print("Raw predictions shape:", predictions.shape)
+        print("Raw predictions:", predictions)
         
         # Get the highest probability prediction
         if len(predictions.shape) > 1:
             pred_indices = predictions.argmax(axis=1)
         else:
             pred_indices = predictions.astype(int)
-        
-        # Decode predictions using loaded label encoder
-        decoded_preds = label_encoder.inverse_transform(pred_indices)
-        print("Decoded predictions: ", decoded_preds, "\n")
-        
-        # Return the first prediction
-        print("==================== Exiting: predict_champion()===================\n")
-        return f"Predicted champion: {decoded_preds[0]}"
+
+        print("\nPrediction Indices:")
+        print("Indices shape:", pred_indices.shape)
+        print("Indices:", pred_indices)
+
+        # Check if indices are within valid range
+        print("\nValidation:")
+        print("Min index:", pred_indices.min())
+        print("Max index:", pred_indices.max())
+        print("Valid index range:", 0, len(label_encoder.classes_) - 1)
+        # Try to decode predictions
+
+        try:
+            decoded_preds = label_encoder.inverse_transform(pred_indices)
+            print("\nDecoded Predictions:")
+            print("Type:", type(decoded_preds))
+            print("Value:", decoded_preds)
+            print("==================== Exiting: predict_champion()===================\n")
+            return f"Predicted champion: {decoded_preds[0]}"
+        except Exception as e:
+            print(f"\nError during decoding: {e}")
+            # Fallback: try to directly index into classes
+            try:
+                champion = label_encoder.classes_[int(pred_indices[0])]
+                return f"Predicted champion: {champion}"
+            except Exception as e2:
+                print(f"Fallback error: {e2}")
+                return f"Error decoding prediction: {pred_indices[0]}"
     
     except Exception as e:
         import traceback