Update app.py

app.py

@@ -8,12 +8,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 # Load the XGBoost model from Pickle (Corrected: Using 'rb' mode)
-with open("h22_xgb_Final.pkl", "rb") as f:
-    raw_model = pickle.load(f)
-
-# Restore the Booster from the raw format
-loaded_model = xgb.Booster()
-loaded_model.load_model(raw_model)
+loaded_model = pickle.load(open("h22_xgb_Final(2).pkl", 'rb'))
 
 # Setup SHAP Explainer for XGBoost
 explainer = shap.TreeExplainer(loaded_model)
@@ -29,11 +24,9 @@ def main_func(SupportiveGM, Merit, LearningDevelopment, WorkEnvironment, Engagem
         'WellBeing': WellBeing
     }, orient='index').transpose()
     
-    # Convert new_row to DMatrix for XGBoost Booster
-    dmatrix_new = xgb.DMatrix(new_row)
-
+   
     # Predict probability for staying (XGBoost Booster returns class probabilities)
-    prob = loaded_model.predict(dmatrix_new)
+    prob = loaded_model.predict_proba(new_row)
 
     # Compute SHAP values
     shap_values = explainer(new_row)