Update app.py

app.py

@@ -232,10 +232,31 @@ else:
     df_final = df_resp
 
 # Overlay mode
-if grouped and not df_final.empty and source_choice == "(All)" and not overlay_sources:
-    df_final["_student"] = df_final["label"].apply(lambda s: s.split(" — ", 1)[0])
-    df_final = df_final.groupby("_student", dropna=False)[list(SKILL_GROUPS.keys())].mean().reset_index()
-    df_final = df_final.rename(columns={"_student": "label"})
+# if grouped and not df_final.empty and source_choice == "(All)" and not overlay_sources:
+#     df_final["_student"] = df_final["label"].apply(lambda s: s.split(" — ", 1)[0])
+#     df_final = df_final.groupby("_student", dropna=False)[list(SKILL_GROUPS.keys())].mean().reset_index()
+#     df_final = df_final.rename(columns={"_student": "label"})
+# ---------------- Overlay vs Combine ----------------
+if not df_final.empty and source_choice == "(All)":
+    if overlay_sources:
+        # Overlay ON → keep one line per source (do nothing)
+        pass
+    else:
+        # Overlay OFF → combine all sources into one line per student
+        df_final["_student"] = df_final["label"].apply(lambda s: s.split(" — ", 1)[0])
+
+        if grouped:
+            cols = list(SKILL_GROUPS.keys())
+        else:
+            cols = SKILLS
+
+        df_final = (
+            df_final
+            .groupby("_student", dropna=False)[cols]
+            .mean()
+            .reset_index()
+            .rename(columns={"_student": "label"})
+        )
 
 # ------------------- Output -------------------
 fig = plot_radar(df_final, grouped, chart_title)