Update app.py

app.py

@@ -210,11 +210,7 @@ else:
 if "app_step" not in st.session_state: st.session_state.app_step = "intro"
 if "results" not in st.session_state: st.session_state.results = {}
 if "train_ranges" not in st.session_state: st.session_state.train_ranges = None
-# Track when dev run has completed at least once (for enabling the Proceed button immediately)
-if "dev_ready" not in st.session_state:
-    st.session_state.dev_ready = False
-
-# If results already exist (page refresh) mark ready
+if "dev_ready" not in st.session_state: st.session_state.dev_ready = False
 if ("Train" in st.session_state.results) or ("Test" in st.session_state.results):
     st.session_state.dev_ready = True
 
@@ -276,9 +272,10 @@ if st.session_state.app_step == "dev":
         on_click=(lambda: st.session_state.update(app_step="predict")) if st.session_state.dev_ready else None,
     )
 
-    st.caption("Upload your data to train the model and review the development performance.")
-
+    # ---- Header + helper sentence positioned under the header (your request) ----
     st.subheader("Model Development")
+    st.write("Upload your data to train the model and review the development performance.")
+
     if run_btn and train_test_file is not None:
         with st.status("Processing…", expanded=False) as status:
             book = read_book(train_test_file)
@@ -414,6 +411,11 @@ if st.session_state.app_step == "predict":
     if "Validate" in st.session_state.results:
         st.subheader("Validation Results")
         sv = st.session_state.results["summary_val"]; oor_table = st.session_state.results.get("oor_table")
+
+        # ---- NEW: show OOR warning above the plots when applicable ----
+        if sv["oor_pct"] > 0:
+            st.warning("Some validation inputs fall outside the **training min–max** ranges. Interpret predictions with caution.")
+
         c1,c2,c3,c4 = st.columns(4)
         c1.metric("points", f"{sv['n_points']}"); c2.metric("Pred min", f"{sv['pred_min']:.2f}")
         c3.metric("Pred max", f"{sv['pred_max']:.2f}"); c4.metric("OOR %", f"{sv['oor_pct']:.1f}%")
@@ -425,6 +427,7 @@ if st.session_state.app_step == "predict":
                 st.info("Actual UCS values are not available in the validation data. Cross-plot cannot be generated.")
         with right:
             st.pyplot(depth_or_index_track(st.session_state.results["Validate"], title=None, include_actual=(TARGET in st.session_state.results["Validate"].columns)), use_container_width=True)
+
         if oor_table is not None:
             st.write("*Out-of-range rows (vs. Training min–max):*")
             st.dataframe(oor_table, use_container_width=True)