Update app.py

app.py

@@ -1,5 +1,4 @@
 import pickle
-import xgboost as xgb
 import pandas as pd
 import shap
 import gradio as gr
@@ -9,7 +8,7 @@ import matplotlib.pyplot as plt
 # Load the XGBoost model from Pickle
 loaded_model = pickle.load(open("h22_xgb_Final(2).pkl", 'rb'))
 
-# Setup SHAP Explainer for XGBoost
+# Setup SHAP Explainer for XGBoost (Do not change this)
 explainer = shap.Explainer(loaded_model)
 
 def safe_convert(value, default, min_val, max_val):
@@ -75,9 +74,11 @@ def main_func(Department, ChainScale, SupportiveGM, Merit, LearningDevelopment,
     shap_values = explainer(new_row)
 
     fig, ax = plt.subplots(figsize=(8, 4))
-    shap.waterfall_plot(shap.Explanation(values=shap_values.values[0],
-                                         base_values=shap_values.base_values[0],
-                                         data=new_row.iloc[0]))  # Fix waterfall plot
+    shap.waterfall_plot(shap.Explanation(
+        values=shap_values.values[0],
+        base_values=shap_values.base_values[0],
+        data=new_row.iloc[0]
+    ))
     plt.tight_layout()
     local_plot = plt.gcf()
     plt.close()
@@ -164,4 +165,4 @@ with gr.Blocks(title=title) as demo:
         cache_examples=True
     )
 
-demo.launch()
+demo.launch()