Update app.py

app.py

@@ -530,57 +530,35 @@ def fetch_student_stage_summary(
         "notable_quotes": notable_quotes,
     }
 
-# ---------- Render the summary panel dynamically ----------
-if mongo_uri and student_choice != "(All)" and source_choice != "(All)":
-    stage = SOURCE_TO_STAGE.get(source_choice.strip())
-    if stage:
-        # set to your actual summaries collection name
-        summaries_coll_name = "summaries_IFE_2025"
-        summary = fetch_student_stage_summary(
-            mongo_uri, db_name, summaries_coll_name, coll_name,
-            student=student_choice, stage=stage
-        )
-        if summary:
-            st.markdown("---")
-            st.subheader(f"Summary — {student_choice} ({stage.replace('_', ' ').title()})")
-            c1, c2 = st.columns(2)
-            with c1:
-                st.markdown(f"**Most Consistent:** {summary.get('most_consistent') or '—'}")
-                st.markdown(f"**Most Developed:** {summary.get('most_developed') or '—'}")
-            with c2:
-                strengths = summary.get("top_strengths") or []
-                st.markdown("**Top Strengths:** " + (", ".join(strengths) if strengths else "—"))
-
-            st.markdown("**Notable Quotes:**")
-            for q in (summary.get("notable_quotes") or [])[:3]:
-                st.markdown(f"> {q}")
+
 # # ------------------- Output (Tabs) -------------------
-tab_radar, tab_analyses = st.tabs(["📈 Radar", "📝 Analyses"])
+tab_radar, tab_analyses = st.tabs(["📈 Summary", "📝 Analyses"])
 
 with tab_radar:
-    # Recompute a local copy for the tab (prevents reusing the same figure object)
-    df_plot_local = df_final.copy()
-    avg_label_local = None
-
-    if not df_plot_local.empty:
-        cols = list(SKILL_GROUPS.keys()) if grouped else SKILLS
-        show_cohort_avg = st.toggle("Show cohort average (all students)", value=True, key="toggle_avg_radar")
-        if show_cohort_avg:
-            avg_vals = df_plot_local[cols].mean()
-            avg_row = {"label": "Average (All Students)"}
-            avg_row.update({k: float(avg_vals[k]) for k in cols})
-            df_plot_local = pd.concat([df_plot_local, pd.DataFrame([avg_row])], ignore_index=True)
-            avg_label_local = "Average (All Students)"
-
-    # Build a fresh figure for the tab or copy if you already built one above:
-    fig_tab = plot_radar(df_plot_local, grouped, chart_title, avg_label=avg_label_local)
-    # fig_tab = go.Figure(fig)  # <- alternative if you already computed `fig` above
-
-    # Unique key to avoid Plotly id collisions on reruns/tab switches
-    key_suffix = f"{student_choice}|{source_choice}|{int(grouped)}|{int(overlay_sources)}"
-    st.plotly_chart(fig_tab, use_container_width=True, key=f"radar_tab_{key_suffix}")
-
-    st.caption(f"{len(df_final)} line(s) aggregated." if not df_final.empty else "No data.")
+# ---------- Render the summary panel dynamically ----------
+    if mongo_uri and student_choice != "(All)" and source_choice != "(All)":
+        stage = SOURCE_TO_STAGE.get(source_choice.strip())
+        if stage:
+            # set to your actual summaries collection name
+            summaries_coll_name = "summaries_IFE_2025"
+            summary = fetch_student_stage_summary(
+                mongo_uri, db_name, summaries_coll_name, coll_name,
+                student=student_choice, stage=stage
+            )
+            if summary:
+                st.markdown("---")
+                st.subheader(f"Summary — {student_choice} ({stage.replace('_', ' ').title()})")
+                c1, c2 = st.columns(2)
+                with c1:
+                    st.markdown(f"**Most Consistent:** {summary.get('most_consistent') or '—'}")
+                    st.markdown(f"**Most Developed:** {summary.get('most_developed') or '—'}")
+                with c2:
+                    strengths = summary.get("top_strengths") or []
+                    st.markdown("**Top Strengths:** " + (", ".join(strengths) if strengths else "—"))
+    
+                st.markdown("**Notable Quotes:**")
+                for q in (summary.get("notable_quotes") or [])[:3]:
+                    st.markdown(f"> {q}")
 
 with tab_analyses:
     st.subheader("Student Analysis (Markdown)")