Upload app.py

app.py

@@ -255,7 +255,7 @@ y5_explainer_lgb = shap.TreeExplainer(y5_model_lgb)
 def y1_predict_xgb(*args):
     df1 = pd.DataFrame([args], columns=x1.columns)
     df1 = df1.astype({col: "category" for col in categorical_columns1})
-    pos_pred = y1_model_xgb.predict(xgb.DMatrix(df1, enable_categorical=True))
+    pos_pred = y1_model_xgb.predict_proba(xgb.DMatrix(df1, enable_categorical=True))
     return {"Mortality": float(pos_pred[0]), "No Mortality": 1 - float(pos_pred[0])}
 
 def y1_predict_lgb(*args):
@@ -282,7 +282,7 @@ def y1_predict_rf(*args):
 def y2_predict_xgb(*args):
     df2 = pd.DataFrame([args], columns=x2.columns)
     df2 = df2.astype({col: "category" for col in categorical_columns2})
-    pos_pred = y2_model_xgb.predict(xgb.DMatrix(df2, enable_categorical=True))
+    pos_pred = y2_model_xgb.predict_proba(xgb.DMatrix(df2, enable_categorical=True))
     return {"Facility Discharge": float(pos_pred[0]), "Home Discharge": 1 - float(pos_pred[0])}
 
 def y2_predict_lgb(*args):
@@ -309,7 +309,7 @@ def y2_predict_rf(*args):
 def y3_predict_xgb(*args):
     df3 = pd.DataFrame([args], columns=x3.columns)
     df3 = df3.astype({col: "category" for col in categorical_columns3})
-    pos_pred = y3_model_xgb.predict(xgb.DMatrix(df3, enable_categorical=True))
+    pos_pred = y3_model_xgb.predict_proba(xgb.DMatrix(df3, enable_categorical=True))
     return {"Prolonged LOS": float(pos_pred[0]), "No Prolonged LOS": 1 - float(pos_pred[0])}
 
 def y3_predict_lgb(*args):
@@ -336,12 +336,12 @@ def y3_predict_rf(*args):
 def y4_predict_xgb(*args):
     df4 = pd.DataFrame([args], columns=x4.columns)
     df4 = df4.astype({col: "category" for col in categorical_columns4})
-    pos_pred = y4_model_xgb.predict(xgb.DMatrix(df4, enable_categorical=True))
+    pos_pred = y4_model_xgb.predict_proba(xgb.DMatrix(df4, enable_categorical=True))
     return {"Prolonged ICU LOS": float(pos_pred[0]), "No Prolonged ICU LOS": 1 - float(pos_pred[0])}
 
 def y4_predict_lgb(*args):
     df4 = pd.DataFrame([args], columns=x4_lgb.columns)
-    df4 = df.astype({col: "category" for col in categorical_columns4})
+    df4 = df4.astype({col: "category" for col in categorical_columns4})
     pos_pred = y4_model_lgb.predict(df4)
     return {"Prolonged ICU LOS": float(pos_pred[0]), "No Prolonged ICU LOS": 1 - float(pos_pred[0])}
 
@@ -363,7 +363,7 @@ def y4_predict_rf(*args):
 def y5_predict_xgb(*args):
     df5 = pd.DataFrame([args], columns=x5.columns)
     df5 = df5.astype({col: "category" for col in categorical_columns5})
-    pos_pred = y5_model_xgb.predict(xgb.DMatrix(df5, enable_categorical=True))
+    pos_pred = y5_model_xgb.predict_proba(xgb.DMatrix(df5, enable_categorical=True))
     return {"Major Complications": float(pos_pred[0]), "No Major Complications": 1 - float(pos_pred[0])}
 
 def y5_predict_lgb(*args):