Update src/streamlit_app.py

src/streamlit_app.py

@@ -463,25 +463,14 @@ if submitted:
         # If SELF donor case, prediction is forced to 0.0 and there is no meaningful SHAP
         if st.session_state.SELF:
             st.info("SHAP is not shown for SELF donor (prediction forced to 0).")
-        elif model_to_use.endswith("ensemble"):    
+        elif model_to_use.endswith("ensemble"):
             ensemble_data = load_model_ensemble(model_to_use)
             models = ensemble_data["model"]
         
-            # Call your ensemble SHAP helper
-            shap_values, shap_base, feature_names = ensemble_shap(
-                models=models,
-                X=X_model,
-                cat_features=cat_features,
-            )
-        
-            # Build SHAP explanation object (single patient)
-            one = shap.Explanation(
-                values=shap_values[0],
-                base_values=shap_base[0] if np.ndim(shap_base) > 0 else shap_base,
-                data=X_model.iloc[0].values,
-                feature_names=feature_names,
-            )
+            # ensemble_shap() RETURNS shap.Explanation (already averaged across models)
+            ens_expl = ensemble_shap(models=models, X=X_model, positive_class=1)
         
+            one = ens_expl[0]
             vals = one.values
             feats = np.array(one.feature_names)
         
@@ -493,14 +482,15 @@ if submitted:
                 "SHAP value (pushes risk ↑ / ↓)": vals[top_idx],
             })
         
-            st.subheader("Top features driving this patient’s prediction")
+            st.subheader("Top features driving this patient’s prediction (ensemble-mean)")
             st.dataframe(shap_table, use_container_width=True)
         
-            st.subheader("Waterfall plot (ensemble)")
+            st.subheader("Waterfall plot (single patient, ensemble-mean)")
             plt.figure(figsize=(10, 6))
             shap.plots.waterfall(one, max_display=20, show=False)
             st.pyplot(plt.gcf(), bbox_inches="tight")
             plt.clf()
+        
             st.caption("SHAP values shown are averaged across ensemble models.")
         else:
             model_dict = load_model(model_to_use)