Update app.py

app.py

@@ -143,7 +143,7 @@ def y1_predict_cb(*args):
     df_cb = pd.DataFrame([args], columns=x.columns)
     df_cb = df_cb.astype({col: "category" for col in categorical_columns})
     pos_pred = y1_model_cb.predict(Pool(df_cb, cat_features = categorical_columns), prediction_type='Probability')
-    return {"Adverse Outcomes": float(pos_pred[0]), "No Adverse Outcomes": 1 - float(pos_pred[0])}
+    return {"Adverse Outcomes": float(pos_pred[0][1]), "No Adverse Outcomes": float(pos_pred[0][0])}
 
 def y1_predict_rf(*args):
     df = pd.DataFrame([args], columns=x_rf.columns)
@@ -151,7 +151,7 @@ def y1_predict_rf(*args):
     d = dict.fromkeys(df.select_dtypes(np.int64).columns, np.int32)
     df = df.astype(d)
     pos_pred = y1_model_rf.predict_proba(df)
-    return {"Adverse Outcomes": float(pos_pred[0]), "No Adverse Outcomes": 1 - float(pos_pred[0])}
+    return {"Adverse Outcomes": float(pos_pred[0][1]), "No Adverse Outcomes": float(pos_pred[0][0])}
 
 
 #Define interpret for y1/AE.