Update app.py

app.py

@@ -165,12 +165,20 @@ def parse_excel(data_bytes: bytes):
 def read_book_bytes(b: bytes): return parse_excel(b) if b else {}
 
 def ensure_cols(df, cols):
-    miss = [c for c in cols if c not in df.columns]
+    """
+    Check required columns exist; auto-fix common typos first.
+    """
+    # Auto-fix known variants before checking
+    fixed = _normalize_columns(df)
+    miss = [c for c in cols if c not in fixed.columns]
     if miss:
-        st.error(f"Missing columns: {miss}\nFound: {list(df.columns)}")
+        st.error(f"Missing columns: {miss}\nFound: {list(fixed.columns)}")
         return False
+    # If everything exists in the fixed version, reflect back to caller
+    # (callers typically use the same df instance; we return True only)
     return True
 
+
 def find_sheet(book, names):
     low2orig = {k.lower(): k for k in book.keys()}
     for nm in names:
@@ -863,6 +871,7 @@ if st.session_state.app_step == "predict":
 # Run preview modal after all other elements
 # =========================
 if st.session_state.show_preview_modal:
+    # Select the correct workbook bytes for this step
     book_to_preview = {}
     if st.session_state.app_step == "dev":
         book_to_preview = read_book_bytes(st.session_state.dev_file_bytes)
@@ -877,22 +886,32 @@ if st.session_state.show_preview_modal:
             tabs = st.tabs(names)
             for t, name in zip(tabs, names):
                 with t:
-                    df = book_to_preview[name]
+                    # 🔧 Normalize columns BEFORE plotting/summarizing
+                    df_raw = book_to_preview[name]
+                    df = _normalize_columns(df_raw)
+
+                    # Tracks
                     t1, t2 = st.tabs(["Tracks", "Summary"])
                     with t1:
                         st.pyplot(preview_tracks(df, FEATURES), use_container_width=True)
+
+                    # Summary (guard against any missing cols after normalization)
                     with t2:
-                        tbl = (df[FEATURES]
-                                 .agg(['min','max','mean','std'])
-                                 .T.rename(columns={"min":"Min","max":"Max","mean":"Mean","std":"Std"}))
-                        df_centered_rounded(tbl.reset_index(names="Feature"))
+                        feat_present = [c for c in FEATURES if c in df.columns]
+                        if not feat_present:
+                            st.info("No feature columns found to summarize.")
+                        else:
+                            tbl = (
+                                df[feat_present]
+                                  .agg(['min','max','mean','std'])
+                                  .T.rename(columns={"min":"Min","max":"Max","mean":"Mean","std":"Std"})
+                                  .reset_index(names="Feature")
+                            )
+                            df_centered_rounded(tbl)
+
+    # Reset the flag so the modal doesn't stick around
     st.session_state.show_preview_modal = False
 
-# === Bottom-of-page Export (per step) =========================================
-if st.session_state.app_step in ("dev", "validate", "predict"):
-    has_results = any(k in st.session_state.results for k in ("Train", "Test", "Validate", "PredictOnly"))
-    if has_results:
-        render_export_button(key=f"export_{st.session_state.app_step}")
 
 # =========================
 # Footer