Update app.py

app.py

@@ -704,25 +704,28 @@ if st.session_state.app_step == "predict":
             st.sidebar.caption(f"**Data loaded:** {up.name} • {df0.shape[0]} rows × {df0.shape[1]} cols")
     if st.sidebar.button("Preview data", use_container_width=True, disabled=(up is None)):
         st.session_state.show_preview_modal = True
-    
-    # Corrected logic: Move the code that depends on the button's click
-    # to be immediately after the button is defined.
+
     if st.sidebar.button("Predict", type="primary", use_container_width=True):
         if up is not None:
             book = read_book_bytes(up.getvalue()); name = list(book.keys())[0]
             df = normalize_df(book[name].copy())
             if not ensure_cols(df, FEATURES):  
-                st.markdown('<div class="st-message-box st-error">Missing required feature columns.</div>', unsafe_allow_html=True);  
+                st.markdown('<div class="st-message-box st-error">Missing required feature columns.</div>', unsafe_allow_html=True)  
                 st.stop()
-
             pred_raw = model.predict(df[FEATURES])
             df["GR_Pred"] = inverse_target(np.asarray(pred_raw, dtype=float), TARGET_TRANSFORM)
             st.session_state.results["PredictOnly"]=df
-
-            ranges = st.session_state.train_ranges; oor_pct = 0.0
+            ranges = st.session_state.train_ranges
+            oor_pct = 0.0
+            oor_tbl = None 
             if ranges:
                 any_viol = pd.DataFrame({f:(df[f]<ranges[f][0])|(df[f]>ranges[f][1]) for f in FEATURES}).any(axis=1)
                 oor_pct = float(any_viol.mean()*100.0)
+                if any_viol.any():
+                    oor_tbl = df.loc[any_viol, FEATURES].copy()
+                    for c in FEATURES:
+                        if pd.api.types.is_numeric_dtype(oor_tbl[c]): oor_tbl[c] = oor_tbl[c].round(2)
+                    oor_tbl["Violations"] = pd.DataFrame({f:(df[f]<ranges[f][0])|(df[f]>ranges[f][1]) for f in FEATURES}).loc[any_viol].apply(lambda r:", ".join([c for c,v in r.items() if v]), axis=1)
             st.session_state.results["sv_pred"]={
                 "n":len(df),
                 "pred_min":float(df["GR_Pred"].min()),
@@ -731,14 +734,42 @@ if st.session_state.app_step == "predict":
                 "pred_std":float(df["GR_Pred"].std(ddof=0)),
                 "oor":oor_pct
             }
-    
-    if st.sidebar.button("⬅ Back to Case Building", use_container_width=True): st.session_state.app_step="dev"; st.rerun()
+            st.session_state.results["oor_tbl_pred"] = oor_tbl
 
-    sticky_header("Prediction", "Upload a dataset with the feature columns (no **GR**).")
+    if st.sidebar.button("⬅ Back to Case Building", use_container_width=True): 
+        st.session_state.app_step="dev"
+        st.rerun()
 
-        if "PredictOnly" in st.session_state.results:
-        # ... (Rest of the code to display the results)
+    sticky_header("Prediction", "Upload a dataset with the feature columns (no **GR**).")
 
+    if "PredictOnly" in st.session_state.results:
+        df = st.session_state.results["PredictOnly"]
+        sv = st.session_state.results["sv_pred"]
+        col_left, col_right = st.columns([2,3], gap="large")
+        with col_left:
+            table = pd.DataFrame({
+                "Metric": ["# points","Pred min","Pred max","Pred mean","Pred std","OOR %"],
+                "Value":  [sv["n"],
+                            round(sv["pred_min"],2),
+                            round(sv["pred_max"],2),
+                            round(sv["pred_mean"],2),
+                            round(sv["pred_std"],2),
+                            f'{sv["oor"]:.1f}%']
+            })
+            st.markdown('<div class="st-message-box st-success">Predictions ready ✓</div>', unsafe_allow_html=True)
+            df_centered_rounded(table, hide_index=True)
+            st.caption("**★ OOR** = % of rows whose input features fall outside the training min–max range.")
+            
+            if st.session_state.results.get("oor_tbl_pred") is not None:
+                st.markdown('<div class="st-message-box st-warning">Some inputs fall outside **training min–max** ranges.</div>', unsafe_allow_html=True)
+                st.write("*Out-of-range rows (vs. Training min–max):*")
+                df_centered_rounded(st.session_state.results["oor_tbl_pred"])
+
+        with col_right:
+            st.plotly_chart(
+                track_plot(df, include_actual=False, pred_col="GR_Pred", actual_col="GR"),
+                use_container_width=False, config={"displayModeBar": False, "scrollZoom": True}
+            )
 # =========================
 # Preview modal (re-usable)
 # =========================