Update src/streamlit_app.py

src/streamlit_app.py

@@ -341,15 +341,20 @@ for col in required_cols:
     if col not in meta_df.columns:
         meta_df[col] = None
 
-# Populate placeholders if file is just run summaries
-if meta_df["feature_name"].isna().all():
-    meta_df["feature_name"] = df.columns
-    meta_df["source_type"] = [
-        "engineered" if any(x in c for x in ["poly", "pca", "roll", "lag"]) else "measured"
-        for c in df.columns
-    ]
-    meta_df["formula"] = ""
-    meta_df["remarks"] = "auto-inferred synthetic feature metadata"
+
+# Populate placeholders if metadata is summary-like (not feature-level)
+if len(meta_df) < len(df.columns) or "feature_name" not in meta_df.columns:
+    st.warning("Metadata appears to be summary-only. Rebuilding feature-level metadata.")
+    meta_df = pd.DataFrame({
+        "feature_name": df.columns,
+        "source_type": [
+            "engineered" if any(x in c for x in ["poly", "pca", "roll", "lag"]) else "measured"
+            for c in df.columns
+        ],
+        "formula": ["" for _ in df.columns],
+        "remarks": ["auto-inferred synthetic feature metadata" for _ in df.columns]
+    })
+
 
 # Build sidebar safely
 feat_types = sorted(meta_df["source_type"].dropna().unique().tolist())