Update app.py

app.py

@@ -246,6 +246,90 @@ fig = plot_radar(df_final, grouped, chart_title)
 st.plotly_chart(fig, use_container_width=True)
 st.caption(f"{len(df_final)} line(s) aggregated." if not df_final.empty else "No data.")
 
+# ================== Dynamic Stage Summaries ==================
+
+STAGE_TO_SOURCE = {
+    "onboarding": "onboarding_responses",
+    "week_2": "week_2_responses",
+    "week_3": "week_3_responses",
+    "closing": "closing_responses"  # for future
+}
+SOURCE_TO_STAGE = {v: k for k, v in STAGE_TO_SOURCE.items()}
+
+def fetch_student_summary(uri, db, summaries_coll, responses_coll, student, stage):
+    """Fetch summary for a student/stage, filling missing or cut-off quotes from responses."""
+    if not (uri and student and stage):
+        return {}
+
+    c = _client(uri)
+    summary_doc = c[db][summaries_coll].find_one(
+        {"student_name": student, "stage": stage},
+        {"_id": 0, "most_consistent": 1, "most_developed": 1,
+         "top_strengths": 1, "notable_quotes": 1}
+    ) or {}
+
+    most_consistent = summary_doc.get("most_consistent")
+    most_developed = summary_doc.get("most_developed")
+    top_strengths = summary_doc.get("top_strengths", [])
+    notable_quotes = summary_doc.get("notable_quotes", [])
+
+    # If notable quotes missing or incomplete, pull from responses_IFE_2025
+    stage_source = STAGE_TO_SOURCE.get(stage)
+    if stage_source:
+        responses = list(c[db][responses_coll].find(
+            {"student": student, "source": stage_source},
+            {"_id": 0, "answer": 1, "skills": 1}
+        ))
+
+        # Fix cut-off quotes
+        fixed_quotes = []
+        for q in notable_quotes:
+            if len(q.strip()) < 50:  # arbitrary cutoff detection
+                # Try to find a full match in responses
+                for r in responses:
+                    if q.strip() in (r.get("answer") or ""):
+                        fixed_quotes.append(r.get("answer"))
+                        break
+            else:
+                fixed_quotes.append(q)
+        notable_quotes = fixed_quotes
+
+        # If still no quotes, pick up to 3 top scoring answers
+        if not notable_quotes:
+            scored_answers = []
+            for r in responses:
+                skills = r.get("skills", {})
+                max_skill_score = max(
+                    (to_01_or_nan(v) for v in skills.values() if v is not None),
+                    default=0
+                )
+                scored_answers.append((max_skill_score, r.get("answer")))
+            scored_answers.sort(reverse=True, key=lambda x: x[0])
+            notable_quotes = [ans for _, ans in scored_answers[:3] if ans]
+
+    return {
+        "most_consistent": most_consistent,
+        "most_developed": most_developed,
+        "top_strengths": top_strengths,
+        "notable_quotes": notable_quotes
+    }
+
+# Render summaries dynamically when a single student & single source/stage is selected
+if student_choice != "(All)" and source_choice != "(All)":
+    stage = SOURCE_TO_STAGE.get(source_choice)
+    if stage:
+        summary_data = fetch_student_summary(
+            mongo_uri, db_name, "summaries_IFE_2025",
+            coll_name, student_choice, stage
+        )
+        if summary_data:
+            st.subheader(f"Summary — {student_choice} ({stage})")
+            st.markdown(f"**Most Consistent Skill:** {summary_data.get('most_consistent', 'N/A')}")
+            st.markdown(f"**Most Developed Skill:** {summary_data.get('most_developed', 'N/A')}")
+            st.markdown("**Top Strengths:** " + ", ".join(summary_data.get('top_strengths', [])) or "N/A")
+            st.markdown("**Notable Quotes:**")
+            for q in summary_data.get("notable_quotes", []):
+                st.markdown(f"> {q}")
 
 # # app.py — Student Skill Radar (MongoDB, secrets-based, no CSV)
 # import os