Update src/app.py

src/app.py

@@ -469,71 +469,126 @@ with tab2:
 
 # === TAB 3: QUIZ MODE ===
 with tab3:
-    st.header("⚡ Acronym Lightning Round")
-    st.caption("Test your knowledge against your own Acronym Dictionary.")
-
-    # Initialize Engine
+    st.header("⚓ Qualification Board Simulator")
+    
+    # 1. MODE SELECTION
+    col_mode, col_streak = st.columns([3, 1])
+    with col_mode:
+        quiz_mode = st.radio(
+            "Select Quiz Mode:", 
+            ["⚡ Acronym Lightning Round", "📖 Document Deep Dive"],
+            horizontal=True
+        )
+    
+    # Initialize State
     quiz = QuizEngine()
     qs = st.session_state.quiz_state
-
-    # METRICS ROW
-    m1, m2, m3 = st.columns(3)
-    m1.metric("Streak", qs["streak"])
     
-    # 1. START / NEXT QUESTION BUTTON
+    # Display Streak
+    with col_streak:
+        st.metric("Current Streak", qs["streak"])
+        if st.button("Reset"): qs["streak"] = 0
+
+    st.divider()
+
+    # 2. START BUTTON (Logic branches based on mode)
     if not qs["active"]:
-        if st.button("🚀 Start New Question", type="primary"):
-            q_data = quiz.get_random_acronym()
-            if q_data:
-                qs["active"] = True
-                qs["question_data"] = q_data
-                qs["feedback"] = None
-                qs["user_answer"] = ""
-                st.rerun()
-            else:
-                st.warning("Acronym Dictionary is empty! Please upload 'acronyms.json' or run the local extractor.")
+        if st.button("🚀 Generate New Question", type="primary"):
+            
+            with st.spinner("Consulting the Board..."):
+                # MODE A: ACRONYMS
+                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"]
+                        st.rerun()
+                    else:
+                        st.error("No acronyms found! Run the extractor first.")
 
-    # 2. QUESTION DISPLAY
-    if qs["active"] and qs["question_data"]:
-        st.divider()
-        st.markdown(f"### {qs['question_data']['question']}")
+                # MODE B: DOCUMENTS
+                else:
+                    q_ctx = quiz.get_document_context(st.session_state.username)
+                    if q_ctx:
+                        # We have the text, now we need the LLM to write the question
+                        prompt = quiz.construct_question_generation_prompt(q_ctx["context_text"])
+                        
+                        # Generate Question using your existing query helper
+                        msgs = [{"role": "user", "content": prompt}]
+                        question_text, usage = query_model_universal(
+                            msgs, 300, model_choice, st.session_state.get("user_openai_key")
+                        )
+                        
+                        qs["active"] = True
+                        qs["question_data"] = q_ctx
+                        qs["generated_question_text"] = question_text # Store the LLM-generated question
+                        qs["feedback"] = None
+                        st.rerun()
+                    else:
+                        st.error("No documents found! Upload some .txt or .md files first.")
+
+    # 3. QUIZ INTERFACE
+    if qs["active"]:
+        st.markdown(f"### {qs['generated_question_text']}")
         
-        # User Input
-        # We use a form so hitting 'Enter' submits the answer
-        with st.form(key="quiz_form"):
-            user_input = st.text_input("Your Definition:", value=qs.get("user_answer_temp", ""))
-            submit_btn = st.form_submit_button("Submit Answer")
+        # Hints for Doc Mode
+        if "document" in qs.get("question_data", {}).get("type", ""):
+            st.caption(f"Source: *{qs['question_data']['source_file']}*")
         
-        if submit_btn and user_input:
-            qs["user_answer"] = user_input
+        with st.form(key="quiz_response"):
+            user_ans = st.text_area("Your Answer:")
+            sub = st.form_submit_button("Submit Answer")
             
-            # 3. GRADING LOGIC
-            with st.spinner("The Board is deliberating..."):
-                # Construct Prompt
-                term = qs["question_data"]["term"]
-                correct_def = qs["question_data"]["correct_definition"]
-                prompt = quiz.construct_grading_prompt(term, correct_def, user_input)
+        if sub and user_ans:
+            with st.spinner("Grading..."):
+                data = qs["question_data"]
                 
-                # Call LLM (Reusing your existing query helper)
+                # BRANCH GRADING LOGIC
+                if data["type"] == "acronym":
+                    prompt = quiz.construct_acronym_grading_prompt(
+                        data["term"], data["correct_definition"], user_ans
+                    )
+                else:
+                    prompt = quiz.construct_grading_prompt(
+                        qs["generated_question_text"], user_ans, data["context_text"]
+                    )
+
+                # Get Grade
                 msgs = [{"role": "user", "content": prompt}]
-                grade, usage = query_model_universal(
-                    msgs, 300, model_choice, st.session_state.get("user_openai_key")
+                grade, _ = query_model_universal(
+                    msgs, 500, model_choice, st.session_state.get("user_openai_key")
                 )
                 
                 qs["feedback"] = grade
                 
-                # Simple Streak Logic (If LLM says PASS)
+                # Streak Logic
                 if "GRADE:** PASS" in grade or "GRADE:** Pass" in grade:
                     qs["streak"] += 1
                 elif "GRADE:** FAIL" in grade:
                     qs["streak"] = 0
                     
-                # Log usage
-                if usage:
-                    m_name = "GPT-4o" if "GPT-4o" in model_choice else model_choice.split()[0]
-                    tracker.log_usage(m_name, usage["input"], usage["output"])
-                    update_sidebar_metrics()
+                st.rerun()
+
+    # 4. FEEDBACK AREA
+    if qs["feedback"]:
+        if "PASS" in qs["feedback"]:
+            st.success("✅ CORRECT")
+        else:
+            st.warning("⚠️ NEEDS IMPROVEMENT")
             
+        st.markdown(qs["feedback"])
+        
+        # For documents, show the source text so you can learn
+        if qs["question_data"]["type"] == "document":
+            with st.expander("Show Source Text (Answer Key)"):
+                st.info(qs["question_data"]["context_text"])
+        
+        if st.button("Next Question ➡️"):
+            qs["active"] = False
+            qs["question_data"] = None
+            qs["feedback"] = None
             st.rerun()
 
     # 4. FEEDBACK DISPLAY