explain

app.py

@@ -101,6 +101,13 @@ learn = load_learner(p)
 def like(issue):
   pred,idx,probs = learn.predict(issue)
   return pred
+
+'''
+EXPLAINABLE AI
+'''
+import shap
+from xgb import X_test
+
 '''
 https://www.gradio.app/docs/interface
 '''
@@ -213,6 +220,27 @@ With no need for jargon, SSDS delivers tangible value to our fintech operations.
     tuning the distance for use case
       """)
 
+  with gr.Tab("Explainable AI"):
+
+
+    file_name = 'xgb/xgb.model'
+    bst = pickle.load(open(file_name, "rb"))
+    explainer_xgb = shap.Explainer(bst)  # , X100)
+    shap_values_xgb = explainer_xgb(X_test)
+
+    sample_ind = 20
+    shap.partial_dependence_plot(
+      "dist_subway",
+      bst.predict,
+      X_test,
+      model_expected_value=True,
+      feature_expected_value=True,
+      ice=False,
+      shap_values=shap_values_xgb[sample_ind: sample_ind + 1, :],
+    )
+    shap.force_plot(explainer_xgb.expected_value, shap_values_xgb[0].values, X_test.iloc[0], matplotlib=True)
+    shap.plots.beeswarm(shap_values_xgb)
+
   with gr.Tab("Fine Tune LLM"):
     in_like = gr.Textbox(placeholder="having credit card problem"   ,                                             label="Issue",
                             info="issue you want to explore about")

requirements.txt

@@ -5,3 +5,5 @@ pandas==2.1.3
 #yfinance==0.2.31
 #scikit-learn
 plotly
+shap
+xgboost