Update app.py

app.py

@@ -53,19 +53,24 @@ def predict_performance(Location, Course, College, Faculty, Source, Event, Prese
     print("\nDataFrame after encoding:")
     print(df)
     
-    # Ensure the DataFrame has the same columns as the scaler was trained on
-    expected_columns = scaler.feature_names_in_
-    for col in expected_columns:
-        if col not in df.columns:
-            df[col] = 0  # Add missing columns with default value 0
-
-    df = df[expected_columns]  # Reorder columns to match the scaler's training data
-
-   
+    # Extract features to scale
+    features_to_scale = df[["College Fee", "Year"]]
+    
+    # Scale only the College Fee and Year
+    scaled_features = scaler.transform(features_to_scale)
+    
+    # Replace the original features with the scaled features
+    df[["College Fee", "Year"]] = scaled_features
+    
+    # Debug print: Show DataFrame after scaling
+    print("\nDataFrame after scaling:")
+    print(df)
+    
+    # Prepare the input for the model
+    model_input = df.values  # Convert DataFrame to numpy array for model input
     
-  
     # Make the prediction
-    prediction = model.predict(df)[0]
+    prediction = model.predict(model_input)[0]
     
     # Clip the prediction to be between 0 and 1
     prediction = np.clip(prediction, 0, 1)