Update app.py

app.py

@@ -36,7 +36,7 @@ from datasets import load_dataset
 data = load_dataset("mertkarabacak/NSQIP-CDA", data_files="cda_imputed.csv", use_auth_token = HF_TOKEN)
 
 data = pd.DataFrame(data['train'])
-variables = ['SEX', 'TRANST', 'AGE', 'SURGSPEC', 'HEIGHT', 'WEIGHT', 'DIABETES', 'SMOKE', 'DYSPNEA', 'FNSTATUS2', 'VENTILAT', 'HXCOPD', 'ASCITES', 'HXCHF', 'HYPERMED', 'RENAFAIL', 'DIALYSIS', 'DISCANCR', 'WNDINF', 'STEROID', 'WTLOSS', 'BLEEDDIS', 'TRANSFUS', 'PRSODM', 'PRBUN', 'PRCREAT', 'PRWBC', 'PRHCT', 'PRPLATE', 'ASACLAS', 'READMISSION1', 'BMI', 'RACE', 'LEVELS', 'ADVERSE_OUTCOME']
+variables = ['SEX', 'TRANST', 'AGE', 'SURGSPEC', 'HEIGHT', 'WEIGHT', 'DIABETES', 'SMOKE', 'DYSPNEA', 'FNSTATUS2', 'VENTILAT', 'HXCOPD', 'ASCITES', 'HXCHF', 'HYPERMED', 'RENAFAIL', 'DIALYSIS', 'DISCANCR', 'WNDINF', 'STEROID', 'WTLOSS', 'BLEEDDIS', 'TRANSFUS', 'PRSODM', 'PRBUN', 'PRCREAT', 'PRWBC', 'PRHCT', 'PRPLATE', 'ASACLAS', 'BMI', 'RACE', 'LEVELS', 'ADVERSE_OUTCOME']
 data = data[variables]
 
 data['SEX'] = data['SEX'].replace(['male'], 'Male')
@@ -131,19 +131,19 @@ def y1_predict_xgb(*args):
     df_xgb = pd.DataFrame([args], columns=x.columns)
     df_xgb = df_xgb.astype({col: "category" for col in categorical_columns})
     pos_pred = y1_model_xgb.predict(xgb.DMatrix(df_xgb, enable_categorical=True))
-    return {"Prolonged LOS": float(pos_pred[0]), "Not Prolonged LOS": 1 - float(pos_pred[0])}
+    return {"Adverse Outcomes": float(pos_pred[0]), "No Adverse Outcomes": 1 - float(pos_pred[0])}
 
 def y1_predict_lgb(*args):
     df = pd.DataFrame([args], columns=data.columns)
     df = df.astype({col: "category" for col in categorical_columns})
     pos_pred = y1_model_lgb.predict(df)
-    return {"Prolonged LOS": float(pos_pred[0]), "Not Prolonged LOS": 1 - float(pos_pred[0])}
+    return {"Adverse Outcomes": float(pos_pred[0]), "No Adverse Outcomes": 1 - float(pos_pred[0])}
 
 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 {"Prolonged LOS": float(pos_pred[0][1]), "Not Prolonged LOS": float(pos_pred[0][0])}
+    return {"Adverse Outcomes": float(pos_pred[0]), "No Adverse Outcomes": 1 - float(pos_pred[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 {"Prolonged LOS": float(pos_pred[0][1]), "Not Prolonged LOS": float(pos_pred[0][0])}
+    return {"Adverse Outcomes": float(pos_pred[0]), "No Adverse Outcomes": 1 - float(pos_pred[0])}
 
 
 #Define interpret for y1/AE.