Update app.py

app.py

@@ -936,31 +936,34 @@ with tab_train:
 
     if not is_admin():
         st.info("Training and publishing are restricted. Use Predict + SHAP for inference.")
-        st.stop()
+    else:
+        st.markdown("### Feature reduction options")
 
-    st.markdown("### Feature reduction options")
+        use_feature_selection = st.checkbox(
+            "Drop columns that do not affect prediction (L1 feature selection)",
+            value=True,
+            key="train_use_feature_selection"
+        )
+        l1_C = st.slider(
+            "L1 selection strength (lower = fewer features)",
+            0.01, 10.0, 1.0, 0.01
+        ) if use_feature_selection else 1.0
+
+        use_dimred = st.checkbox(
+            "Dimensionality reduction (TruncatedSVD) — reduces interpretability",
+            value=False
+        )
 
-    use_feature_selection = st.checkbox(
-        "Drop columns that do not affect prediction (L1 feature selection)",
-        value=True,
-        key="train_use_feature_selection"
-    )
-    l1_C = st.slider(
-        "L1 selection strength (lower = fewer features)",
-        0.01, 10.0, 1.0, 0.01
-    ) if use_feature_selection else 1.0
-    
-    use_dimred = st.checkbox(
-        "Dimensionality reduction (TruncatedSVD) — reduces interpretability",
-        value=False
-    )
-    
-    svd_components = st.slider(
-        "SVD components (only used if enabled)",
-        5, 300, 50, 5
-    ) if use_dimred else 50
+        svd_components = st.slider(
+            "SVD components (only used if enabled)",
+            5, 300, 50, 5
+        ) if use_dimred else 50
+
+        st.divider()
+
+        # then keep your file uploader + training button + publish block here
+        ...
 
-    st.divider()
 
 # ---------------- TRAIN ----------------
 
@@ -1409,7 +1412,7 @@ with tab_predict:
         "CNS Involvement",
         "Extramedullary Involvement",
     ]
-    DROPDOWN_FIELDS = [f for f in DROPDOWN_FIELDS if f not in TAB_MANAGED_FIELDS]
+    
     
 
     
@@ -1457,6 +1460,11 @@ with tab_predict:
     ]
     NGS_COUNT_COL = "No. of ngs_mutation"
 
+
+    AGE_FEATURE = "Age (years)"
+    DX_DATE_FEATURE = "Date of 1st Bone Marrow biopsy (Date of Diagnosis) "  # keep exact if your schema has trailing space
+
+    
     # =========================
     # Fields managed by dedicated tabs (do NOT render duplicates in Core tab)
     # =========================
@@ -1470,6 +1478,10 @@ with tab_predict:
     
     # NGS handled in NGS tab
     TAB_MANAGED_FIELDS.update([f for f in NGS_MARKERS if f in feature_cols])
+
+    DROPDOWN_FIELDS = [f for f in DROPDOWN_FIELDS if f not in TAB_MANAGED_FIELDS]
+    YESNO_FIELDS    = [f for f in YESNO_FIELDS if f not in TAB_MANAGED_FIELDS]  # optional
+
     
     # Optional marker counts: handled automatically
     if FISH_COUNT_COL in feature_cols:
@@ -1524,8 +1536,7 @@ with tab_predict:
     st.divider()
     st.subheader("Enter patient details to get prediction (Example: DOB → Age, Dx date → prediction)")
     
-    AGE_FEATURE = "Age (years)"
-    DX_DATE_FEATURE = "Date of 1st Bone Marrow biopsy (Date of Diagnosis) "  # keep exact if your schema has trailing space
+    
     
     from datetime import date
 
@@ -1760,7 +1771,7 @@ with tab_predict:
         df_inf = st.session_state.df_inf
         if df_inf is None:
             st.info("Upload an inference Excel to run batch prediction / external validation.")
-            
+            st.stop()
 
 
         
@@ -2226,4 +2237,4 @@ with tab_predict:
                 fig_b = plt.gcf()
                 render_plot_with_download(fig_b, title="SHAP bar", filename="shap_bar_row.png", export_dpi=export_dpi)
         
-            st.stop()
+