Update app.py

app.py

@@ -973,31 +973,31 @@ with tab_train:
            st.info("Upload a training Excel file to enable training.")
         else: 
     
-        df = pd.read_excel(train_file, engine="openpyxl")
-        feature_cols = get_feature_cols_from_df(df)
-    
-        st.dataframe(df.head(), use_container_width=True)
-        feature_cols = get_feature_cols_from_df(df)
-    
-        st.markdown("### Choose variable types (saved into the model)")
-        default_numeric = feature_cols[:13]  # initial suggestion
-        num_cols = st.multiselect(
-        "Numeric variables (will be median-imputed + scaled)",
-        options=feature_cols,
-        default=default_numeric
-        )       
-                                
-        # Everything not selected as numeric becomes categorical
-        cat_cols = [c for c in feature_cols if c not in num_cols]
-                
-        st.write(f"Categorical variables (will be most-frequent-imputed + one-hot): {len(cat_cols)}")
-        st.caption("Note: The selected schema is stored with the trained model and must match inference files.")
-    
-        st.markdown("### Evaluation settings")
-        n_bins = st.slider("Calibration bins", 5, 20, 10, 1)
-        cal_strategy = st.selectbox("Calibration binning strategy", ["uniform", "quantile"], index=0)
-                
-        dca_points = st.slider("Decision curve points", 25, 200, 99, 1)           
+            df = pd.read_excel(train_file, engine="openpyxl")
+            feature_cols = get_feature_cols_from_df(df)
+        
+            st.dataframe(df.head(), use_container_width=True)
+            feature_cols = get_feature_cols_from_df(df)
+        
+            st.markdown("### Choose variable types (saved into the model)")
+            default_numeric = feature_cols[:13]  # initial suggestion
+            num_cols = st.multiselect(
+            "Numeric variables (will be median-imputed + scaled)",
+            options=feature_cols,
+            default=default_numeric
+            )       
+                                    
+            # Everything not selected as numeric becomes categorical
+            cat_cols = [c for c in feature_cols if c not in num_cols]
+                    
+            st.write(f"Categorical variables (will be most-frequent-imputed + one-hot): {len(cat_cols)}")
+            st.caption("Note: The selected schema is stored with the trained model and must match inference files.")
+        
+            st.markdown("### Evaluation settings")
+            n_bins = st.slider("Calibration bins", 5, 20, 10, 1)
+            cal_strategy = st.selectbox("Calibration binning strategy", ["uniform", "quantile"], index=0)
+                    
+            dca_points = st.slider("Decision curve points", 25, 200, 99, 1)