a

app.py

@@ -199,7 +199,7 @@ if uploaded_file:
 
     input_pool = Pool(input_df, cat_features=cat_features)
     catboost_preds = catboost.predict(input_pool)
-
+    catboost_probs = catboost.predict_proba(input_df)[:, 1]
     label_encoders = {}  # Store encoders to ensure consistency
 
     for col in cat_features:
@@ -260,17 +260,15 @@ if uploaded_file:
     # # ✅ Make Predictions with RandomForest
     # rf_preds = rf.predict(input_df_rf)
 
-    catboost_probs = catboost.predict_proba(input_df)[:, 1]
     xgb_probs = xgb.predict_proba(input_df)[:, 1]
     #rf_probs = rf.predict_proba(input_df)[:, 1]
-
+ #test
     # Combine results
     predictions_df = pd.DataFrame({
         "CatBoost": catboost_preds,
         "XGBoost": xgb_preds,
       #  "RandomForest": rf_preds
     })
-
     # Apply "at least one model predicts 1" rule
     predictions_df["is_click_predicted"] = predictions_df.max(axis=1)