Update app.py

app.py

@@ -195,6 +195,43 @@ def df_centered_rounded(df: pd.DataFrame, hide_index=True):
             .set_table_styles(TABLE_CENTER_CSS)
     )
     st.dataframe(styler, use_container_width=True, hide_index=hide_index)
+def safe_summary(df: pd.DataFrame, features: list[str]) -> None:
+    """
+    Show a centered, 2-decimal summary for whatever expected features
+    actually exist in `df`. Never crashes if some are missing or non-numeric.
+    """
+    # Which expected columns are present / missing in this sheet?
+    present = [c for c in features if c in df.columns]
+    missing = [c for c in features if c not in df.columns]
+
+    if not present:
+        st.info(
+            "None of the expected feature columns were found in this sheet.\n"
+            f"Expected any of: {features}\n"
+            f"Found: {list(df.columns)}"
+        )
+        return
+
+    if missing:
+        st.caption(f"Columns not found in this sheet (omitted): {missing}")
+
+    # Work only with present columns and coerce to numeric (text → NaN)
+    work = df[present].copy()
+    for c in present:
+        work[c] = pd.to_numeric(work[c], errors="coerce")
+
+    # Build stats, 2 decimals, then show centered
+    tbl = (
+        work.agg(["min", "max", "mean", "std"])
+            .T.rename(columns={"min": "Min", "max": "Max", "mean": "Mean", "std": "Std"})
+            .round(2)
+    )
+    df_centered_rounded(tbl.reset_index(names="Feature"))
+
+    # Small note if coercion created NaNs
+    if work.isna().any().any():
+        st.caption("Note: non-numeric values were ignored after numeric coercion.")
+
 
 # --- target transform helpers (to support models trained on log(GR)) ---
 def inverse_target(x: np.ndarray, transform: str) -> np.ndarray:
@@ -753,11 +790,7 @@ if st.session_state.show_preview_modal:
                     with t1:
                         st.pyplot(preview_tracks(df, FEATURES), use_container_width=True)
                     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"))
-    st.session_state.show_preview_modal = False
+                        safe_summary(df, FEATURES)
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