Update app.py

app.py

@@ -55,13 +55,9 @@ best_model = tuner.get_best_models(1)[0]
 
 # Define prediction function for Gradio
 def predict_performance(hours, prev_score, sleep, papers, activity):
-    user_input = pd.DataFrame([{
-        'Hours Studied': hours,
-        'Previous Scores': prev_score,
-        'Sleep Hours': sleep,
-        'Sample Question Papers Practiced': papers,
-        'Extracurricular Activities': activity
-    }])
+    cols = ['Hours Studied', 'Previous Scores', 'Sleep Hours', 'Sample Question Papers Practiced', 'Extracurricular Activities']
+    values = [hours, prev_score, sleep, papers, activity]
+    user_input = pd.DataFrame([dict(zip(cols, values))])
     X_user = preprocess.transform(user_input)
     pred = best_model.predict(X_user).item()
     return f"📊 Predicted Performance Index: {pred:.2f}"