Update src/streamlit_app.py

src/streamlit_app.py

@@ -452,31 +452,31 @@ if submitted:
                 patient_cov[c] = categorical_defaults.get(c, 0)
     
         try:
-        # Create one-hot design row matching training design columns
-        patient_design = make_patient_design_row(patient_cov, design_cols, cat_cols)
-        if isinstance(patient_design, tuple):
-            patient_design = patient_design[0]  # keep only the DataFrame row
-    
-        # Predict survival curve + landmarks
-        surv_fn, landmarks = predict_patient_survival(cph, patient_design, years=[3, 5, 10])
-    
-        st.subheader("Individual adjusted survival curve")
-        fig, ax = plt.subplots(figsize=(8, 5))
-        surv_fn.plot(ax=ax, legend=False)
-        ax.set_title("Predicted adjusted survival curve")
-        ax.set_xlabel("Days")
-        ax.set_ylabel("Survival probability")
-        st.pyplot(fig, bbox_inches="tight")
-        plt.clf()
-    
-        st.write("Landmark survival probabilities:")
-        for y in [3, 5, 10]:
-            s = landmarks.get(y, None)
-            if s is not None:
-                st.write(f"{y}-year survival: {100*s:.1f}%")
-    
-    except Exception as e:
-        st.error(f"Survival prediction failed: {e}")
+            # Create one-hot design row matching training design columns
+            patient_design = make_patient_design_row(patient_cov, design_cols, cat_cols)
+            if isinstance(patient_design, tuple):
+                patient_design = patient_design[0]  # keep only the DataFrame row
+        
+            # Predict survival curve + landmarks
+            surv_fn, landmarks = predict_patient_survival(cph, patient_design, years=[3, 5, 10])
+        
+            st.subheader("Individual adjusted survival curve")
+            fig, ax = plt.subplots(figsize=(8, 5))
+            surv_fn.plot(ax=ax, legend=False)
+            ax.set_title("Predicted adjusted survival curve")
+            ax.set_xlabel("Days")
+            ax.set_ylabel("Survival probability")
+            st.pyplot(fig, bbox_inches="tight")
+            plt.clf()
+        
+            st.write("Landmark survival probabilities:")
+            for y in [3, 5, 10]:
+                s = landmarks.get(y, None)
+                if s is not None:
+                    st.write(f"{y}-year survival: {100*s:.1f}%")
+        
+        except Exception as e:
+            st.error(f"Survival prediction failed: {e}")
 
     st.caption(
         "Choose the appropriate model from the left panel based on the outcome you want to predict. "