Update src/app.py

modified tab 3 to fit with the new quiz mode

src/app.py

@@ -408,103 +408,190 @@ with tab2:
 with tab3:
     st.header("⚓ Qualification Board Simulator")
     admin_panel.render_debug_overlay("Quiz Tab")
+    
     col_mode, col_streak = st.columns([3, 1])
-    with col_mode: quiz_mode = st.radio("Mode:", ["⚡ Acronym Lightning Round", "📖 Document Deep Dive"], horizontal=True)
-    if "Document" in quiz_mode: focus_topic = st.text_input("🎯 Focus Topic", placeholder="e.g., PPBE...", help="Leave empty for random.")
-    else: focus_topic = None
+    with col_mode: 
+        quiz_mode = st.radio("Mode:", ["⚡ Acronym Lightning Round", "📖 Document Deep Dive"], horizontal=True)
+    
+    if "Document" in quiz_mode: 
+        focus_topic = st.text_input("🎯 Focus Topic", placeholder="e.g., PPBE...", help="Leave empty for random.")
+    else: 
+        focus_topic = None
 
     if "last_quiz_mode" not in st.session_state: st.session_state.last_quiz_mode = quiz_mode
     if "quiz_trigger" not in st.session_state: st.session_state.quiz_trigger = False
+    
     if st.session_state.last_quiz_mode != quiz_mode:
-        st.session_state.quiz_state["active"] = False; st.session_state.quiz_state["question_data"] = None; st.session_state.quiz_state["feedback"] = None; st.session_state.quiz_state["generated_question_text"] = ""
-        st.session_state.last_quiz_mode = quiz_mode; st.rerun()
+        st.session_state.quiz_state["active"] = False
+        st.session_state.quiz_state["question_data"] = None
+        st.session_state.quiz_state["feedback"] = None
+        st.session_state.quiz_state["generated_question_text"] = ""
+        st.session_state.last_quiz_mode = quiz_mode
+        st.rerun()
     
-    quiz = QuizEngine(); qs = st.session_state.quiz_state
-    with col_streak: st.metric("Streak", qs["streak"]); 
-    if st.button("Reset"): qs["streak"] = 0
+    quiz = QuizEngine()
+    qs = st.session_state.quiz_state
+    
+    with col_streak: 
+        st.metric("Streak", qs["streak"])
+        if st.button("Reset"): qs["streak"] = 0
+        
     if st.session_state.quiz_history:
         with st.expander(f"📚 Review Study Guide ({len(st.session_state.quiz_history)})"):
-            st.download_button("📥 Download Markdown", generate_study_guide_md(st.session_state.quiz_history), f"StudyGuide_{datetime.now().strftime('%Y%m%d')}.md")
+            st.download_button(
+                "📥 Download Markdown", 
+                generate_study_guide_md(st.session_state.quiz_history), 
+                f"StudyGuide_{datetime.now().strftime('%Y%m%d')}.md"
+            )
     st.divider()
 
     def generate_question():
         with st.spinner("Consulting Board..."):
             st.session_state.last_context_used = "" 
+            
             if "Acronym" in quiz_mode:
                 q_data = quiz.get_random_acronym()
-                if q_data: qs["active"]=True; qs["question_data"]=q_data; qs["feedback"]=None; qs["generated_question_text"]=q_data["question"]
-                else: st.error("No acronyms.")
+                if q_data: 
+                    qs["active"]=True
+                    qs["question_data"]=q_data
+                    qs["feedback"]=None
+                    qs["generated_question_text"]=q_data["question"]
+                else: 
+                    st.error("No acronyms.")
             else:
-                valid_question_found = False; attempts = 0; last_error = None
+                valid_question_found = False
+                attempts = 0
+                last_error = None
+                
                 while not valid_question_found and attempts < 5:
                     attempts += 1
                     q_ctx = quiz.get_document_context(st.session_state.username, topic_filter=focus_topic)
-                    if q_ctx and "error" in q_ctx: last_error = q_ctx["error"]; break
+                    
+                    if q_ctx and "error" in q_ctx: 
+                        last_error = q_ctx["error"]
+                        break
+                        
                     if q_ctx:
-                        prompt = quiz.construct_question_generation_prompt(q_ctx["context_text"])
+                        # NEW: Use the Scenario Prompt
+                        prompt = quiz.construct_scenario_prompt(q_ctx["context_text"])
                         st.session_state.last_context_used = q_ctx["context_text"]
-                        question_text, usage = query_model_universal([{"role": "user", "content": prompt}], 300, model_choice, st.session_state.get("user_openai_key"))
-                        if "UNABLE" not in question_text and len(question_text) > 10:
-                            valid_question_found = True; qs["active"]=True; qs["question_data"]=q_ctx; qs["generated_question_text"]=question_text; qs["feedback"]=None
-                if not valid_question_found:
-                    if last_error == "topic_not_found": st.warning(f"Topic '{focus_topic}' not found.")
-                    elif focus_topic: st.warning(f"Found '{focus_topic}' but could not generate question.")
-                    else: st.warning("Could not generate question. Try Resync.")
+                        
+                        # Generate
+                        response_text, usage = query_model_universal([{"role": "user", "content": prompt}], 600, model_choice, st.session_state.get("user_openai_key"))
+                        
+                        # PARSE OUTPUT (Scenario vs Solution)
+                        if "SCENARIO:" in response_text and "SOLUTION:" in response_text:
+                            parts = response_text.split("SOLUTION:")
+                            scenario_text = parts[0].replace("SCENARIO:", "").strip()
+                            solution_text = parts[1].strip()
+                            
+                            valid_question_found = True
+                            qs["active"] = True
+                            qs["question_data"] = q_ctx
+                            qs["generated_question_text"] = scenario_text
+                            qs["hidden_solution"] = solution_text 
+                            qs["feedback"] = None
+                        else:
+                            # Fallback if model ignores format
+                            valid_question_found = True
+                            qs["active"] = True
+                            qs["question_data"] = q_ctx
+                            qs["generated_question_text"] = response_text
+                            qs["hidden_solution"] = "Refer to Source Text."
+                            qs["feedback"] = None
 
-    if st.session_state.quiz_trigger: st.session_state.quiz_trigger = False; generate_question(); st.rerun()
+                if not valid_question_found:
+                    if last_error == "topic_not_found": 
+                        st.warning(f"Topic '{focus_topic}' not found.")
+                    elif focus_topic: 
+                        st.warning(f"Found '{focus_topic}' but could not generate question.")
+                    else: 
+                        st.warning("Could not generate question. Try Resync.")
+
+    if st.session_state.quiz_trigger: 
+        st.session_state.quiz_trigger = False
+        generate_question()
+        st.rerun()
+        
     if not qs["active"]:
-        if st.button("🚀 New Question", type="primary"): generate_question(); st.rerun()
+        if st.button("🚀 New Question", type="primary"): 
+            generate_question()
+            st.rerun()
 
     if qs["active"]:
         st.markdown(f"### {qs['generated_question_text']}")
-        if "document" in qs.get("question_data", {}).get("type", ""): st.caption(f"Source: *{qs['question_data']['source_file']}*")
+        
+        if "document" in qs.get("question_data", {}).get("type", ""): 
+            st.caption(f"Source: *{qs['question_data']['source_file']}*")
+            
         with st.form(key="quiz_response"):
             user_ans = st.text_area("Answer:")
             sub = st.form_submit_button("Submit")
+            
         if sub and user_ans:
-            with st.spinner("Grading..."):
+            with st.spinner("Board is deliberating..."):
                 data = qs["question_data"]
+                
                 if data["type"] == "acronym":
                     prompt = quiz.construct_acronym_grading_prompt(data["term"], data["correct_definition"], user_ans)
                     final_context_for_history = data["correct_definition"]
+                    msgs = [{"role": "user", "content": prompt}]
+                    grade, _ = query_model_universal(msgs, 1000, model_choice, st.session_state.get("user_openai_key"))
+                    qs["feedback"] = grade
                 else:
-                    combined_context = f"--- PRIMARY SOURCE ---\n{data['context_text']}\n\n"
-                    if st.session_state.active_index and st.session_state.get("active_embed_model"):
-                        try:
-                            related_docs = rag_engine.search_knowledge_base(
-                                query=qs["generated_question_text"], 
-                                username=st.session_state.username,
-                                index_name=st.session_state.active_index,
-                                embed_model_name=st.session_state.active_embed_model,
-                                k=15, final_k=5
-                            )
-                            if related_docs:
-                                combined_context += "--- RELATED ---\n"
-                                for i, doc in enumerate(related_docs): combined_context += f"[Source {i+1}]: {doc.page_content}\n\n"
-                        except Exception as e: print(f"Search failed: {e}")
-                    prompt = quiz.construct_grading_prompt(qs["generated_question_text"], user_ans, combined_context)
-                    final_context_for_history = combined_context
-                    st.session_state.last_context_used = combined_context
-
-                msgs = [{"role": "user", "content": prompt}]
-                grade, _ = query_model_universal(msgs, 1000, model_choice, st.session_state.get("user_openai_key"))
-                qs["feedback"] = grade
-                is_pass = "PASS" in grade
-                if is_pass: qs["streak"] += 1
-                elif "FAIL" in grade: qs["streak"] = 0
-                st.session_state.quiz_history.append({"question": qs["generated_question_text"], "user_answer": user_ans, "grade": "PASS" if is_pass else "FAIL", "context": final_context_for_history})
+                    # NEW: Scenario Grading Logic
+                    scenario = qs["generated_question_text"]
+                    solution = qs.get("hidden_solution", "")
+                    context_ref = data["context_text"]
+                    
+                    prompt = quiz.construct_scenario_grading_prompt(scenario, user_ans, solution, context_ref)
+                    st.session_state.last_context_used = f"SCENARIO: {scenario}\n\nSOLUTION: {solution}\n\nREF: {context_ref}"
+
+                    msgs = [{"role": "user", "content": prompt}]
+                    grade, _ = query_model_universal(msgs, 1000, model_choice, st.session_state.get("user_openai_key"))
+                    qs["feedback"] = grade
+                
+                # Logic to determine PASS/FAIL
+                is_pass = False
+                if "10/10" in grade or "9/10" in grade or "8/10" in grade or "7/10" in grade or "PASS" in grade:
+                    is_pass = True
+                    qs["streak"] += 1
+                elif "FAIL" in grade or " 6/" in grade or " 5/" in grade: 
+                    qs["streak"] = 0
+                else:
+                    is_pass = True
+                    qs["streak"] += 1 
+
+                # Save history
+                st.session_state.quiz_history.append({
+                    "question": qs["generated_question_text"], 
+                    "user_answer": user_ans, 
+                    "grade": "PASS" if is_pass else "FAIL", # Simplified for history list
+                    "context": f"**Official Solution:** {qs.get('hidden_solution', '')}\n\n**Source Text:** {data.get('context_text', '')[:500]}..."
+                })
+                
                 st.rerun()
 
     if qs["feedback"]:
         st.divider()
-        if "PASS" in qs["feedback"]: st.success("✅ CORRECT")
+        if "PASS" in qs["feedback"] or "7/10" in qs["feedback"] or "8/10" in qs["feedback"] or "9/10" in qs["feedback"] or "10/10" in qs["feedback"]: 
+            st.success("✅ CORRECT / PASSING")
         else:
             if "FAIL" in qs["feedback"]: st.error("❌ INCORRECT")
-            else: st.warning("⚠️ PARTIAL")
+            else: st.warning("⚠️ PARTIAL / CRITIQUE")
+            
         st.markdown(qs["feedback"])
+        
         data = qs["question_data"]
-        if data["type"] == "acronym": st.info(f"**Definition:** {data['correct_definition']}")
+        if data["type"] == "acronym": 
+            st.info(f"**Definition:** {data['correct_definition']}")
         elif data["type"] == "document":
-            with st.expander("Show Answer Key"): st.info(data["context_text"])
+            with st.expander("Show Official Solution"): 
+                st.info(qs.get("hidden_solution", "No solution generated."))
+            
         if st.button("Next Question ➡️"):
-            st.session_state.quiz_trigger = True; qs["active"]=False; qs["question_data"]=None; qs["feedback"]=None; st.rerun()
+            st.session_state.quiz_trigger = True
+            qs["active"] = False
+            qs["question_data"] = None
+            qs["feedback"] = None
+            st.rerun()