Update app.py

app.py

@@ -2313,6 +2313,63 @@ with tab_predict:
             out["predicted_class"] = pred_class
             
             out["risk_band"] = band_one(proba_one)
+
+            # ---- SHAP for single patient (works even without inference Excel) ----
+            X_one_t = transform_before_clf(pipe, X_one)
+            
+            explainer = st.session_state.get("explainer")
+            explainer_sig = st.session_state.get("explainer_sig")
+            
+            current_sig = (
+                selected,
+                None if st.session_state.get("X_bg_for_shap") is None else int(len(st.session_state["X_bg_for_shap"]))
+            )
+            
+            if explainer is None or explainer_sig != current_sig:
+                X_bg = st.session_state.get("X_bg_for_shap")
+                if X_bg is None:
+                    st.error("SHAP background not available. Admin must publish latest/background.csv.")
+                    st.stop()
+            
+                st.session_state.explainer = build_shap_explainer(pipe, X_bg)
+                st.session_state.explainer_sig = current_sig
+                explainer = st.session_state.explainer
+            
+            shap_vals = explainer.shap_values(X_one_t)
+            if isinstance(shap_vals, list):
+                shap_vals = shap_vals[1]
+            
+            names = get_final_feature_names(pipe)
+            if len(names) != shap_vals.shape[1]:
+                names = [f"f{i}" for i in range(shap_vals.shape[1])]
+            
+            try:
+                x_dense = X_one_t.toarray()[0]
+            except Exception:
+                x_dense = np.array(X_one_t)[0]
+
+            base = explainer.expected_value
+            if not np.isscalar(base):
+                base = float(np.array(base).reshape(-1)[0])
+            
+            exp = shap.Explanation(
+                values=shap_vals[0],
+                base_values=float(base),
+                data=x_dense,
+                feature_names=names,
+            )
+            
+            # Plot
+            plt.figure(figsize=FIGSIZE, dpi=plot_dpi_screen)
+            shap.plots.waterfall(exp, show=False, max_display=20)
+            fig_w = plt.gcf()
+            render_plot_with_download(fig_w, title="Single-patient SHAP waterfall", filename="single_patient_shap_waterfall.png", export_dpi=export_dpi, key="dl_sp_wf")
+            
+            plt.figure(figsize=FIGSIZE, dpi=plot_dpi_screen)
+            shap.plots.bar(exp, show=False, max_display=20)
+            fig_b = plt.gcf()
+            render_plot_with_download(fig_b, title="Single-patient SHAP bar", filename="single_patient_shap_bar.png", export_dpi=export_dpi, key="dl_sp_bar")
+
         
             st.dataframe(out, use_container_width=True)