Update app.py

app.py

@@ -7,16 +7,16 @@ import gradio as gr
 import numpy as np
 import matplotlib.pyplot as plt
 
-# Load the XGBoost model from Pickle
+# Load the XGBoost model from Pickle (Corrected: Using 'rb' mode)
 with open("h22_xgb_Final.pkl", "rb") as f:
-    loaded_model = pickle.load(f)
+    raw_model = pickle.load(f)
 
-# Ensure model is a Booster (handles both XGBClassifier & Booster cases)
-if isinstance(loaded_model, xgb.XGBClassifier):
-    loaded_model = loaded_model.get_booster()
+# Restore the Booster from the raw format
+loaded_model = xgb.Booster()
+loaded_model.load_model(raw_model)
 
 # Setup SHAP Explainer for XGBoost
-explainer = shap.TreeExplainer(loaded_model)  # Use TreeExplainer for XGBoost models
+explainer = shap.TreeExplainer(loaded_model)
 
 # Define the prediction function
 def main_func(SupportiveGM, Merit, LearningDevelopment, WorkEnvironment, Engagement, WellBeing):
@@ -32,12 +32,14 @@ def main_func(SupportiveGM, Merit, LearningDevelopment, WorkEnvironment, Engagem
     # Convert new_row to DMatrix for XGBoost Booster
     dmatrix_new = xgb.DMatrix(new_row)
 
-    # Predict probability for staying (XGBoost Booster returns only one class probability)
+    # Predict probability for staying (XGBoost Booster returns class probabilities)
     prob = loaded_model.predict(dmatrix_new)
 
     # Compute SHAP values
-    shap_values = explainer.shap_values(new_row)
-    plot = shap.plots.bar(shap_values, max_display=6, order=shap.Explanation.abs, show_data='auto', show=False)
+    shap_values = explainer(new_row)
+
+    # Generate SHAP Plot
+    plot = shap.plots.bar(shap_values[0], max_display=6, order=shap.Explanation.abs, show_data='auto', show=False)
 
     plt.tight_layout()
     local_plot = plt.gcf()