Upload app.py

app.py

@@ -203,7 +203,7 @@ y5_explainer_lgb = shap.TreeExplainer(y5_model_lgb)
 #Define predict for y1 (mortality).
 def y1_predict_xgb(*args):
     df1 = pd.DataFrame([args], columns=x1.columns)
-    df1 = df.astype({col: "category" for col in categorical_columns1})
+    df1 = df1.astype({col: "category" for col in categorical_columns1})
     d1 = dict.fromkeys(df1.select_dtypes(np.int64).columns, np.int32)
     df1 = df1.astype(d1)
     pos_pred = y1_model_xgb.predict_proba(df1)
@@ -211,7 +211,7 @@ def y1_predict_xgb(*args):
 
 def y1_predict_lgb(*args):
     df1 = pd.DataFrame([args], columns=x1.columns)
-    df1 = df.astype({col: "category" for col in categorical_columns1})
+    df1 = df1.astype({col: "category" for col in categorical_columns1})
     d1 = dict.fromkeys(df1.select_dtypes(np.int64).columns, np.int32)
     df1 = df1.astype(d1)
     pos_pred = y1_model_lgb.predict_proba(df1)
@@ -225,7 +225,7 @@ def y1_predict_cb(*args):
 
 def y1_predict_rf(*args):
     df1 = pd.DataFrame([args], columns=x1_rf.columns)
-    df1 = df.astype({col: "category" for col in categorical_columns1})
+    df1 = df1.astype({col: "category" for col in categorical_columns1})
     d1 = dict.fromkeys(df1.select_dtypes(np.int64).columns, np.int32)
     df1 = df1.astype(d1)
     pos_pred = y1_model_rf.predict_proba(df1)