Update app.py

app.py

@@ -1,10 +1,9 @@
+# app.py
 import os
 import time
-import json
-
 import base64
 from collections import defaultdict
-from typing import List, Dict
+from typing import List, Dict, Tuple, Optional
 
 import gradio as gr
 from langsmith import Client  # LangSmith 客户端
@@ -19,12 +18,9 @@ from clare_core import (
     update_weaknesses_from_message,
     update_cognitive_state_from_message,
     render_session_status,
-    find_similar_past_question,
     detect_language,
     chat_with_clare,
     export_conversation,
-    generate_quiz_from_history,
-    get_empty_input_prompt,
     summarize_conversation,
 )
 from rag_engine import (
@@ -181,10 +177,10 @@ No. Clare supplements your learning by providing on-demand guidance.
 
 **Q: What languages does Clare support?**
 Currently: English & 简体中文.
-"""
+""",
 }
 
-# ================== CSS 样式表 ==================
+# ================== CSS ==================
 CUSTOM_CSS = """
 /* --- Main Header --- */
 .header-container { padding: 10px 20px; background-color: #ffffff; border-bottom: 2px solid #f3f4f6; margin-bottom: 15px; display: flex; align-items: center; }
@@ -228,8 +224,6 @@ CUSTOM_CSS = """
 
 /* Action Buttons */
 .action-btn { font-weight: bold !important; font-size: 0.9rem !important; position: relative; overflow: visible !important; }
-.action-btn:hover::before { content: "See User Guide for details"; position: absolute; bottom: 110%; left: 50%; transform: translateX(-50%); background-color: #333; color: #fff; padding: 5px 10px; border-radius: 5px; font-size: 12px; white-space: nowrap; z-index: 1000; pointer-events: none; opacity: 0; animation: fadeIn 0.2s forwards; }
-.action-btn:hover::after { content: ""; position: absolute; bottom: 100%; left: 50%; margin-left: -5px; border-width: 5px; border-style: solid; border-color: #333 transparent transparent transparent; opacity: 0; animation: fadeIn 0.2s forwards; }
 
 /* Tooltips & Memory Line */
 .html-tooltip { border-bottom: 1px dashed #999; cursor: help; position: relative; }
@@ -239,8 +233,6 @@ CUSTOM_CSS = """
 /* Results Box Style */
 .result-box { border: 1px solid #e5e7eb; background: #ffffff; padding: 10px; border-radius: 8px; height: 100%; }
 .result-box .prose { font-size: 0.9rem; }
-
-@keyframes fadeIn { to { opacity: 1; } }
 """
 
 # ========== Preload Module 10 PDF ==========
@@ -252,12 +244,8 @@ preloaded_chunks: List[Dict] = []
 
 if os.path.exists(MODULE10_PATH):
     try:
-        preloaded_topics = extract_course_topics_from_file(
-            MODULE10_PATH, MODULE10_DOC_TYPE
-        )
-        preloaded_chunks = build_rag_chunks_from_file(
-            MODULE10_PATH, MODULE10_DOC_TYPE
-        )
+        preloaded_topics = extract_course_topics_from_file(MODULE10_PATH, MODULE10_DOC_TYPE)
+        preloaded_chunks = build_rag_chunks_from_file(MODULE10_PATH, MODULE10_DOC_TYPE)
         print("Module 10 PDF preloaded successfully.")
     except Exception as e:
         print("Module 10 preload failed:", e)
@@ -278,7 +266,6 @@ def log_event(data: Dict):
             "question": data.get("question"),
             "student_id": data.get("student_id"),
         }
-        # ✅ event_type 等字段作为 metadata，这样在 Dataset 列表里能直接看到 / 过滤
         metadata = {k: v for k, v in data.items() if k not in ("question", "answer")}
 
         ls_client.create_example(
@@ -292,9 +279,7 @@ def log_event(data: Dict):
 
 
 # ===== Reference Formatting Helper =====
-def format_references(
-    rag_chunks: List[Dict], max_files: int = 2, max_sections_per_file: int = 3
-) -> str:
+def format_references(rag_chunks: List[Dict], max_files: int = 2, max_sections_per_file: int = 3) -> str:
     if not rag_chunks:
         return ""
 
@@ -320,28 +305,18 @@ def format_references(
         else:
             lines.append(f"- *{file_name}*")
 
-    if len(lines) == 1:
-        return ""
-    return "\n".join(lines)
+    return "\n".join(lines) if len(lines) > 1 else ""
 
 
 def is_academic_query(message: str) -> bool:
     if not message:
         return False
-
     m = message.strip().lower()
     if not m:
         return False
-
     m = " ".join(m.split())
 
-    smalltalk_tokens = {
-        "hi", "hello", "hey", "yo",
-        "thanks", "thank", "thank you",
-        "ok", "okay",
-        "bye", "goodbye", "see you",
-        "haha", "lol"
-    }
+    smalltalk_tokens = {"hi", "hello", "hey", "yo", "thanks", "thank", "thank you", "ok", "okay", "bye", "goodbye", "see you", "haha", "lol"}
     tokens = m.split()
 
     if "?" not in m and all(t in smalltalk_tokens for t in tokens):
@@ -372,11 +347,15 @@ def is_academic_query(message: str) -> bool:
     return True
 
 
-# ================== Gradio App ==================
-with gr.Blocks(
-    title="Clare – Hanbridge AI Teaching Assistant", css=CUSTOM_CSS
-) as demo:
+def normalize_lang_pref(lang_pref: str) -> str:
+    # UI uses "简体中文", core uses "中文"
+    if lang_pref == "简体中文":
+        return "中文"
+    return lang_pref
 
+
+# ================== Gradio App ==================
+with gr.Blocks(title="Clare ��� Hanbridge AI Teaching Assistant", css=CUSTOM_CSS) as demo:
     # 全局状态
     course_outline_state = gr.State(preloaded_topics or DEFAULT_COURSE_TOPICS)
     weakness_state = gr.State([])
@@ -389,12 +368,8 @@ with gr.Blocks(
     user_name_state = gr.State("")
     user_id_state = gr.State("")
 
-    # ✅ 当前“最近一次回答”是否已经被点赞/点踩（只允许一次）
     feedback_used_state = gr.State(False)
 
-    # ✅ 性能输出
-    perf_state = gr.State({})
-
     # --- Header ---
     with gr.Row(elem_classes="header-container"):
         with gr.Column(scale=3):
@@ -417,20 +392,12 @@ with gr.Blocks(
 
     # --- Main Layout ---
     with gr.Row():
-
         # === Left Sidebar ===
         with gr.Column(scale=1, min_width=200):
-            clear_btn = gr.Button(
-                "Reset Conversation", variant="stop", interactive=False
-            )
+            clear_btn = gr.Button("Reset Conversation", variant="stop", interactive=False)
 
             gr.Markdown("### Model Settings")
-            model_name = gr.Textbox(
-                label="Model",
-                value="gpt-4.1-mini",
-                interactive=False,
-                lines=1,
-            )
+            model_name = gr.Textbox(label="Model", value="gpt-4.1-mini", interactive=False, lines=1)
             language_preference = gr.Radio(
                 choices=["Auto", "English", "简体中文"],
                 value="Auto",
@@ -446,64 +413,26 @@ with gr.Blocks(
                 interactive=False,
             )
 
-            with gr.Accordion(
-                "User Guide", open=True, elem_classes="main-user-guide"
-            ):
-                with gr.Accordion(
-                    "Getting Started",
-                    open=False,
-                    elem_classes="clean-accordion",
-                ):
+            with gr.Accordion("User Guide", open=True, elem_classes="main-user-guide"):
+                with gr.Accordion("Getting Started", open=False, elem_classes="clean-accordion"):
                     gr.Markdown(USER_GUIDE_SECTIONS["getting_started"])
-                with gr.Accordion(
-                    "Mode Definition",
-                    open=False,
-                    elem_classes="clean-accordion",
-                ):
+                with gr.Accordion("Mode Definition", open=False, elem_classes="clean-accordion"):
                     gr.Markdown(USER_GUIDE_SECTIONS["mode_definition"])
-                with gr.Accordion(
-                    "How Clare Works",
-                    open=False,
-                    elem_classes="clean-accordion",
-                ):
+                with gr.Accordion("How Clare Works", open=False, elem_classes="clean-accordion"):
                     gr.Markdown(USER_GUIDE_SECTIONS["how_clare_works"])
-                with gr.Accordion(
-                    "What is Memory Line",
-                    open=False,
-                    elem_classes="clean-accordion",
-                ):
+                with gr.Accordion("What is Memory Line", open=False, elem_classes="clean-accordion"):
                     gr.Markdown(USER_GUIDE_SECTIONS["memory_line"])
-                with gr.Accordion(
-                    "Learning Progress Report",
-                    open=False,
-                    elem_classes="clean-accordion",
-                ):
+                with gr.Accordion("Learning Progress Report", open=False, elem_classes="clean-accordion"):
                     gr.Markdown(USER_GUIDE_SECTIONS["learning_progress"])
-                with gr.Accordion(
-                    "How Clare Uses Your Files",
-                    open=False,
-                    elem_classes="clean-accordion",
-                ):
+                with gr.Accordion("How Clare Uses Your Files", open=False, elem_classes="clean-accordion"):
                     gr.Markdown(USER_GUIDE_SECTIONS["how_files"])
-                with gr.Accordion(
-                    "Micro-Quiz", open=False, elem_classes="clean-accordion"
-                ):
+                with gr.Accordion("Micro-Quiz", open=False, elem_classes="clean-accordion"):
                     gr.Markdown(USER_GUIDE_SECTIONS["micro_quiz"])
-                with gr.Accordion(
-                    "Summarization",
-                    open=False,
-                    elem_classes="clean-accordion",
-                ):
+                with gr.Accordion("Summarization", open=False, elem_classes="clean-accordion"):
                     gr.Markdown(USER_GUIDE_SECTIONS["summarization"])
-                with gr.Accordion(
-                    "Export Conversation",
-                    open=False,
-                    elem_classes="clean-accordion",
-                ):
+                with gr.Accordion("Export Conversation", open=False, elem_classes="clean-accordion"):
                     gr.Markdown(USER_GUIDE_SECTIONS["export_conversation"])
-                with gr.Accordion(
-                    "FAQ", open=False, elem_classes="clean-accordion"
-                ):
+                with gr.Accordion("FAQ", open=False, elem_classes="clean-accordion"):
                     gr.Markdown(USER_GUIDE_SECTIONS["faq"])
 
             gr.Markdown("---")
@@ -519,7 +448,6 @@ with gr.Blocks(
 
         # === Center Main ===
         with gr.Column(scale=3):
-
             gr.Markdown(
                 """
                 <div style="background-color:#f9fafb; padding:10px; border-radius:5px; margin-top:10px; font-size:0.9em; color:#555;">
@@ -538,18 +466,10 @@ with gr.Blocks(
                 type="tuples",
             )
 
-            # ✅ profiling output JSON (shows your TTFT / tokens/sec etc.)
-            perf_output = gr.JSON(label="Output", value={})
-
-            # Rating bar (last answer)
             gr.Markdown("#### Rate Clare’s last answer")
             with gr.Row():
-                thumb_up_btn = gr.Button(
-                    "👍 Helpful", size="sm", interactive=False
-                )
-                thumb_down_btn = gr.Button(
-                    "👎 Not helpful", size="sm", interactive=False
-                )
+                thumb_up_btn = gr.Button("👍 Helpful", size="sm", interactive=False)
+                thumb_down_btn = gr.Button("👎 Not helpful", size="sm", interactive=False)
 
             feedback_toggle_btn = gr.Button(
                 "Give detailed feedback", size="sm", variant="secondary", interactive=False
@@ -591,12 +511,7 @@ with gr.Blocks(
                         interactive=False,
                     )
                     gr.HTML("<div style='height:5px'></div>")
-                    docs_btn = gr.Button(
-                        "📂 Loaded Docs",
-                        size="sm",
-                        variant="secondary",
-                        interactive=False,
-                    )
+                    docs_btn = gr.Button("📂 Loaded Docs", size="sm", variant="secondary", interactive=False)
                 with gr.Column(scale=2):
                     with gr.Group(elem_classes="memory-line-box"):
                         gr.HTML(
@@ -615,12 +530,7 @@ with gr.Blocks(
                             </div>
                             """
                         )
-                        review_btn = gr.Button(
-                            "Review Now",
-                            size="sm",
-                            variant="primary",
-                            interactive=False,
-                        )
+                        review_btn = gr.Button("Review Now", size="sm", variant="primary", interactive=False)
                         session_status = gr.Markdown(visible=False)
 
         # === Right Sidebar ===
@@ -629,37 +539,21 @@ with gr.Blocks(
                 gr.HTML(f"<img src='{image_to_base64(CLARE_READING_PATH)}'>")
 
                 with gr.Group(visible=True) as login_state_1:
-                    login_start_btn = gr.Button(
-                        "Student Login", elem_classes="login-main-btn"
-                    )
+                    login_start_btn = gr.Button("Student Login", elem_classes="login-main-btn")
 
                 with gr.Group(visible=False) as login_state_2:
-                    name_input = gr.Textbox(
-                        label="Student Name", placeholder="Name", container=True
-                    )
-                    id_input = gr.Textbox(
-                        label="Email/ID", placeholder="ID", container=True
-                    )
-                    login_confirm_btn = gr.Button(
-                        "Enter", variant="primary", size="sm"
-                    )
+                    name_input = gr.Textbox(label="Student Name", placeholder="Name", container=True)
+                    id_input = gr.Textbox(label="Email/ID", placeholder="ID", container=True)
+                    login_confirm_btn = gr.Button("Enter", variant="primary", size="sm")
 
                 with gr.Group(visible=False) as login_state_3:
                     student_info_html = gr.HTML()
-                    logout_btn = gr.Button(
-                        "Log out", elem_classes="logout-btn", size="sm"
-                    )
+                    logout_btn = gr.Button("Log out", elem_classes="logout-btn", size="sm")
 
             gr.Markdown("### Actions")
-            export_btn = gr.Button(
-                "Export Conversation", size="sm", elem_classes="action-btn", interactive=False
-            )
-            quiz_btn = gr.Button(
-                "Let's Try (Micro-Quiz)", size="sm", elem_classes="action-btn", interactive=False
-            )
-            summary_btn = gr.Button(
-                "Summarization", size="sm", elem_classes="action-btn", interactive=False
-            )
+            export_btn = gr.Button("Export Conversation", size="sm", elem_classes="action-btn", interactive=False)
+            quiz_btn = gr.Button("Let's Try (Micro-Quiz)", size="sm", elem_classes="action-btn", interactive=False)
+            summary_btn = gr.Button("Summarization", size="sm", elem_classes="action-btn", interactive=False)
 
             gr.Markdown("### Results")
             with gr.Group(elem_classes="result-box"):
@@ -669,7 +563,6 @@ with gr.Blocks(
                 )
 
     # ================== Login Flow ==================
-
     def show_inputs():
         return {
             login_state_1: gr.update(visible=False),
@@ -677,9 +570,7 @@ with gr.Blocks(
             login_state_3: gr.update(visible=False),
         }
 
-    login_start_btn.click(
-        show_inputs, outputs=[login_state_1, login_state_2, login_state_3]
-    )
+    login_start_btn.click(show_inputs, outputs=[login_state_1, login_state_2, login_state_3])
 
     def confirm_login(name, id_val):
         if not name or not id_val:
@@ -710,8 +601,6 @@ with gr.Blocks(
                 feedback_toggle_btn: gr.update(interactive=False),
                 feedback_text: gr.update(visible=False, value=""),
                 feedback_submit_btn: gr.update(interactive=False, visible=False),
-                perf_state: {},                         # ✅
-                perf_output: gr.update(value={}),        # ✅
             }
 
         info_html = f"""
@@ -743,14 +632,11 @@ with gr.Blocks(
             learning_mode: gr.update(interactive=True),
             model_name: gr.update(interactive=False),
             docs_btn: gr.update(interactive=True),
-            # ✅ 登录后仍然不允许点赞点踩，必须“有回答”才解锁
             thumb_up_btn: gr.update(interactive=False, value="👍 Helpful"),
             thumb_down_btn: gr.update(interactive=False, value="👎 Not helpful"),
             feedback_toggle_btn: gr.update(interactive=True),
             feedback_text: gr.update(visible=False, value=""),
             feedback_submit_btn: gr.update(interactive=True, visible=False),
-            perf_state: {},                         # ✅
-            perf_output: gr.update(value={}),        # ✅
         }
 
     login_confirm_btn.click(
@@ -781,8 +667,6 @@ with gr.Blocks(
             feedback_toggle_btn,
             feedback_text,
             feedback_submit_btn,
-            perf_state,         # ✅
-            perf_output,        # ✅
         ],
     )
 
@@ -817,8 +701,6 @@ with gr.Blocks(
             feedback_toggle_btn: gr.update(interactive=False),
             feedback_text: gr.update(visible=False, value=""),
             feedback_submit_btn: gr.update(interactive=False, visible=False),
-            perf_state: {},                          # ✅
-            perf_output: gr.update(value={}),         # ✅
         }
 
     logout_btn.click(
@@ -849,13 +731,10 @@ with gr.Blocks(
             feedback_toggle_btn,
             feedback_text,
             feedback_submit_btn,
-            perf_state,        # ✅
-            perf_output,       # ✅
         ],
     )
 
     # ================== Main Logic ==================
-
     def update_course_and_rag(file, doc_type_val):
         local_topics = preloaded_topics or []
         local_chunks = preloaded_chunks or []
@@ -914,15 +793,6 @@ with gr.Blocks(
         user_id_val,
         feedback_used,
     ):
-        # perf container
-        t0 = time.perf_counter()
-        marks = {"start": 0}
-        segs = {}
-
-        def mark(name: str):
-            marks[name] = (time.perf_counter() - t0) * 1000.0
-
-        # 未登录：不解锁按钮
         if not user_id_val:
             out_msg = (
                 "🔒 Please log in with your Student Name and Email/ID on the right "
@@ -934,7 +804,6 @@ with gr.Blocks(
                 weaknesses or [],
                 cognitive_state or {"confusion": 0, "mastery": 0},
             )
-            perf = {"marks_ms": marks, "segments_ms": segs, "total_ms": (time.perf_counter() - t0) * 1000.0}
             return (
                 "",
                 new_history,
@@ -946,23 +815,17 @@ with gr.Blocks(
                 feedback_used,
                 gr.update(interactive=False, value="👍 Helpful"),
                 gr.update(interactive=False, value="👎 Not helpful"),
-                perf,                      # ✅ perf_state
-                perf,                      # ✅ perf_output
             )
 
-        # language detect
-        resolved_lang = detect_language(message or "", lang_pref)
-        mark("language_detect_done")
-        segs["language_detect_done"] = marks["language_detect_done"] - marks["start"]
+        lang_pref_norm = normalize_lang_pref(lang_pref)
+        resolved_lang = detect_language(message or "", lang_pref_norm)
 
-        # 空输入：不改变按钮状态
         if not message or not message.strip():
             new_status = render_session_status(
                 mode_val or "Concept Explainer",
                 weaknesses or [],
                 cognitive_state or {"confusion": 0, "mastery": 0},
             )
-            perf = {"marks_ms": marks, "segments_ms": segs, "total_ms": (time.perf_counter() - t0) * 1000.0}
             return (
                 "",
                 chat_history,
@@ -974,36 +837,18 @@ with gr.Blocks(
                 feedback_used,
                 gr.update(),
                 gr.update(),
-                perf,                      # ✅
-                perf,                      # ✅
             )
 
-        # weakness/cognitive updates
-        t_w0 = time.perf_counter()
         weaknesses = update_weaknesses_from_message(message, weaknesses or [])
-        mark("weakness_update_done")
-        segs["weakness_update_done"] = (time.perf_counter() - t_w0) * 1000.0
-
-        t_c0 = time.perf_counter()
         cognitive_state = update_cognitive_state_from_message(message, cognitive_state)
-        mark("cognitive_update_done")
-        segs["cognitive_update_done"] = (time.perf_counter() - t_c0) * 1000.0
 
-        # RAG
-        t_r0 = time.perf_counter()
         if is_academic_query(message):
-            rag_context_text, rag_used_chunks = retrieve_relevant_chunks(
-                message, rag_chunks or []
-            )
+            rag_context_text, rag_used_chunks = retrieve_relevant_chunks(message, rag_chunks or [])
         else:
             rag_context_text, rag_used_chunks = "", []
-        mark("rag_retrieve_done")
-        segs["rag_retrieve_done"] = (time.perf_counter() - t_r0) * 1000.0
 
-        # LLM (chat_with_clare must return 3 values)
         start_ts = time.time()
-
-        answer, new_history, llm_stats = chat_with_clare(
+        answer, new_history = chat_with_clare(
             message=message,
             history=chat_history,
             model_name=model_name_val,
@@ -1015,43 +860,10 @@ with gr.Blocks(
             cognitive_state=cognitive_state,
             rag_context=rag_context_text,
         )
-        
         end_ts = time.time()
         latency_ms = (end_ts - start_ts) * 1000.0
-        
-        # === optional: print profiled metrics locally ===
-        try:
-            print("[LLM_PROFILE] " + json.dumps(llm_stats, ensure_ascii=False))
-        except Exception:
-            pass
-
-        # merge llm_stats into perf
-        perf = {
-            "marks_ms": marks,
-            "segments_ms": segs,
-            "total_ms": marks.get("llm_done", (time.perf_counter() - t0) * 1000.0),
-        }
-
-        # llm_stats may contain marks_ms/segments_ms/llm_profile
-        if isinstance(llm_stats, dict):
-            if "llm_profile" in llm_stats:
-                perf.update({"llm_profile": llm_stats.get("llm_profile", {})})
-            # merge marks/segments from llm_stats if present
-            ms2 = llm_stats.get("marks_ms") if isinstance(llm_stats.get("marks_ms"), dict) else {}
-            sg2 = llm_stats.get("segments_ms") if isinstance(llm_stats.get("segments_ms"), dict) else {}
-            for k, v in ms2.items():
-                if v is not None:
-                    perf["marks_ms"][k] = v
-            for k, v in sg2.items():
-                if v is not None:
-                    perf["segments_ms"][k] = v
-
-        # References formatting
-        if is_academic_query(message) and rag_used_chunks:
-            ref_text = format_references(rag_used_chunks)
-        else:
-            ref_text = ""
 
+        ref_text = format_references(rag_used_chunks) if (is_academic_query(message) and rag_used_chunks) else ""
         if ref_text and new_history:
             last_user, last_assistant = new_history[-1]
             if "References (RAG context used):" not in (last_assistant or ""):
@@ -1059,7 +871,6 @@ with gr.Blocks(
                 new_history[-1] = [last_user, last_assistant]
                 answer = last_assistant
 
-        # LangSmith event
         student_id = user_id_val or "ANON"
         experiment_id = "RESP_AI_W10"
         try:
@@ -1068,18 +879,13 @@ with gr.Blocks(
                     "experiment_id": experiment_id,
                     "student_id": student_id,
                     "event_type": "chat_turn",
-                    "timestamp": time.time(),
-                    "latency_ms": perf["total_ms"],
+                    "timestamp": end_ts,
+                    "latency_ms": latency_ms,
                     "question": message,
                     "answer": answer,
                     "model_name": model_name_val,
                     "language": resolved_lang,
                     "learning_mode": mode_val,
-                    # ✅ extra profiling fields if available
-                    "ttft_ms": perf.get("segments_ms", {}).get("llm_ttft_ms"),
-                    "tokens_per_sec": (perf.get("llm_profile", {}) or {}).get("tokens_per_sec_est"),
-                    "output_tokens_est": (perf.get("llm_profile", {}) or {}).get("output_tokens_est"),
-                    "streaming_used": (perf.get("llm_profile", {}) or {}).get("streaming_used"),
                 }
             )
         except Exception as e:
@@ -1087,7 +893,6 @@ with gr.Blocks(
 
         new_status = render_session_status(mode_val, weaknesses, cognitive_state)
 
-        # ✅ 有新回答：重置 feedback_used=False，并解锁按钮（恢复文案）
         return (
             "",
             new_history,
@@ -1099,8 +904,6 @@ with gr.Blocks(
             False,
             gr.update(interactive=True, value="👍 Helpful"),
             gr.update(interactive=True, value="👎 Not helpful"),
-            perf,                 # ✅ perf_state
-            perf,                 # ✅ perf_output
         )
 
     user_input.submit(
@@ -1130,8 +933,6 @@ with gr.Blocks(
             feedback_used_state,
             thumb_up_btn,
             thumb_down_btn,
-            perf_state,      # ✅
-            perf_output,     # ✅
         ],
     )
 
@@ -1148,16 +949,8 @@ with gr.Blocks(
         doc_type_val,
         user_id_val,
     ):
-        t0 = time.perf_counter()
-        marks = {"start": 0}
-        segs = {}
-
-        def mark(name: str):
-            marks[name] = (time.perf_counter() - t0) * 1000.0
-
         if not user_id_val:
             gr.Info("Please log in first to start a micro-quiz.", title="Login required")
-            perf = {"marks_ms": marks, "segments_ms": segs, "total_ms": (time.perf_counter() - t0) * 1000.0}
             return (
                 chat_history,
                 weaknesses,
@@ -1167,8 +960,6 @@ with gr.Blocks(
                     weaknesses or [],
                     cognitive_state or {"confusion": 0, "mastery": 0},
                 ),
-                perf,
-                perf,
             )
 
         quiz_instruction = (
@@ -1183,8 +974,7 @@ with gr.Blocks(
             "• Do NOT start a content question until I have answered 1 or 2.\n\n"
             "Step 2 – After I choose the style:\n"
             "• If I choose 1 (multiple-choice):\n"
-            "  - Ask ONE multiple-choice question at a time, based on Module 10 concepts "
-            "(Responsible AI definition, risk types, mitigation layers, EU AI Act, etc.).\n"
+            "  - Ask ONE multiple-choice question at a time, based on Module 10 concepts.\n"
             "  - Provide 3–4 options (A, B, C, D) and make only one option clearly correct.\n"
             "• If I choose 2 (short-answer):\n"
             "  - Ask ONE short-answer question at a time, also based on Module 10 concepts.\n"
@@ -1198,19 +988,11 @@ with gr.Blocks(
             "Do not ask any content question before I choose."
         )
 
-        resolved_lang = lang_pref
-        mark("language_detect_done")
-        segs["language_detect_done"] = marks["language_detect_done"]
+        resolved_lang = normalize_lang_pref(lang_pref)
 
-        t_r0 = time.perf_counter()
-        quiz_ctx_text, _quiz_ctx_chunks = retrieve_relevant_chunks(
-            "Module 10 quiz", rag_chunks or []
-        )
-        mark("rag_retrieve_done")
-        segs["rag_retrieve_done"] = (time.perf_counter() - t_r0) * 1000.0
-
-        t_llm0 = time.perf_counter()
-        answer, new_history, llm_stats = chat_with_clare(
+        start_ts = time.time()
+        quiz_ctx_text, _ = retrieve_relevant_chunks("Module 10 quiz", rag_chunks or [])
+        answer, new_history = chat_with_clare(
             message=quiz_instruction,
             history=chat_history,
             model_name=model_name_val,
@@ -1222,50 +1004,32 @@ with gr.Blocks(
             cognitive_state=cognitive_state,
             rag_context=quiz_ctx_text,
         )
-        mark("llm_done")
-        segs["llm_done"] = (time.perf_counter() - t_llm0) * 1000.0
-
-        perf = {
-            "marks_ms": marks,
-            "segments_ms": segs,
-            "total_ms": marks.get("llm_done", (time.perf_counter() - t0) * 1000.0),
-        }
-        if isinstance(llm_stats, dict):
-            if "llm_profile" in llm_stats:
-                perf.update({"llm_profile": llm_stats.get("llm_profile", {})})
-            ms2 = llm_stats.get("marks_ms") if isinstance(llm_stats.get("marks_ms"), dict) else {}
-            sg2 = llm_stats.get("segments_ms") if isinstance(llm_stats.get("segments_ms"), dict) else {}
-            for k, v in ms2.items():
-                if v is not None:
-                    perf["marks_ms"][k] = v
-            for k, v in sg2.items():
-                if v is not None:
-                    perf["segments_ms"][k] = v
+        end_ts = time.time()
+        latency_ms = (end_ts - start_ts) * 1000.0
 
         student_id = user_id_val or "ANON"
         experiment_id = "RESP_AI_W10"
+
         try:
             log_event(
                 {
                     "experiment_id": experiment_id,
                     "student_id": student_id,
-                    "event_type": "chat_turn",
+                    "event_type": "micro_quiz_start",
                     "timestamp": end_ts,
                     "latency_ms": latency_ms,
-                    "question": message,
+                    "question": quiz_instruction,
                     "answer": answer,
                     "model_name": model_name_val,
                     "language": resolved_lang,
                     "learning_mode": mode_val,
-                    "llm_stats": llm_stats,
                 }
             )
-
         except Exception as e:
             print("log_event error:", e)
 
         new_status = render_session_status(mode_val, weaknesses, cognitive_state)
-        return new_history, weaknesses, cognitive_state, new_status, perf, perf
+        return new_history, weaknesses, cognitive_state, new_status
 
     quiz_btn.click(
         start_micro_quiz,
@@ -1281,48 +1045,27 @@ with gr.Blocks(
             doc_type,
             user_id_state,
         ],
-        [chatbot, weakness_state, cognitive_state_state, session_status, perf_state, perf_output],
+        [chatbot, weakness_state, cognitive_state_state, session_status],
     )
 
-    # ===== Feedback Handlers (thumb + detailed) =====
+    # ===== Feedback UI =====
     def show_feedback_box():
         return {
             feedback_text: gr.update(visible=True),
             feedback_submit_btn: gr.update(visible=True),
         }
 
-    feedback_toggle_btn.click(
-        show_feedback_box,
-        None,
-        [feedback_text, feedback_submit_btn],
-    )
+    feedback_toggle_btn.click(show_feedback_box, None, [feedback_text, feedback_submit_btn])
 
-    def send_thumb_up(
-        last_q,
-        last_a,
-        user_id_val,
-        mode_val,
-        model_name_val,
-        lang_pref,
-        feedback_used,
-    ):
-        # 没有可评价回答：保持禁用
+    def send_thumb_up(last_q, last_a, user_id_val, mode_val, model_name_val, lang_pref, feedback_used):
         if not last_q and not last_a:
-            print("No last QA to log for thumbs_up.")
             return (
                 feedback_used,
                 gr.update(interactive=False, value="👍 Helpful"),
                 gr.update(interactive=False, value="👎 Not helpful"),
             )
-
-        # 已经反馈过：直接禁用
         if feedback_used:
-            print("Feedback already sent for this answer (thumb_up).")
-            return (
-                feedback_used,
-                gr.update(interactive=False),
-                gr.update(interactive=False),
-            )
+            return (feedback_used, gr.update(interactive=False), gr.update(interactive=False))
 
         try:
             log_event(
@@ -1334,45 +1077,24 @@ with gr.Blocks(
                     "question": last_q,
                     "answer": last_a,
                     "model_name": model_name_val,
-                    "language": lang_pref,
+                    "language": normalize_lang_pref(lang_pref),
                     "learning_mode": mode_val,
                 }
             )
-            print("[Feedback] thumbs_up logged to LangSmith.")
         except Exception as e:
             print("thumb_up log error:", e)
 
-        # 点完一次：置 True + 按钮置灰 + 文案 sent
-        return (
-            True,
-            gr.update(interactive=False, value="👍 Helpful (sent)"),
-            gr.update(interactive=False),
-        )
+        return (True, gr.update(interactive=False, value="👍 Helpful (sent)"), gr.update(interactive=False))
 
-    def send_thumb_down(
-        last_q,
-        last_a,
-        user_id_val,
-        mode_val,
-        model_name_val,
-        lang_pref,
-        feedback_used,
-    ):
+    def send_thumb_down(last_q, last_a, user_id_val, mode_val, model_name_val, lang_pref, feedback_used):
         if not last_q and not last_a:
-            print("No last QA to log for thumbs_down.")
             return (
                 feedback_used,
                 gr.update(interactive=False, value="👍 Helpful"),
                 gr.update(interactive=False, value="👎 Not helpful"),
             )
-
         if feedback_used:
-            print("Feedback already sent for this answer (thumb_down).")
-            return (
-                feedback_used,
-                gr.update(interactive=False),
-                gr.update(interactive=False),
-            )
+            return (feedback_used, gr.update(interactive=False), gr.update(interactive=False))
 
         try:
             log_event(
@@ -1384,19 +1106,14 @@ with gr.Blocks(
                     "question": last_q,
                     "answer": last_a,
                     "model_name": model_name_val,
-                    "language": lang_pref,
+                    "language": normalize_lang_pref(lang_pref),
                     "learning_mode": mode_val,
                 }
             )
-            print("[Feedback] thumbs_down logged to LangSmith.")
         except Exception as e:
             print("thumb_down log error:", e)
 
-        return (
-            True,
-            gr.update(interactive=False),
-            gr.update(interactive=False, value="👎 Not helpful (sent)"),
-        )
+        return (True, gr.update(interactive=False), gr.update(interactive=False, value="👎 Not helpful (sent)"))
 
     thumb_up_btn.click(
         send_thumb_up,
@@ -1426,14 +1143,9 @@ with gr.Blocks(
         [feedback_used_state, thumb_up_btn, thumb_down_btn],
     )
 
-    def submit_detailed_feedback(
-        text, last_q, last_a, user_id_val, mode_val, model_name_val, lang_pref
-    ):
+    def submit_detailed_feedback(text, last_q, last_a, user_id_val, mode_val, model_name_val, lang_pref):
         if not text or not text.strip():
-            return gr.update(
-                value="",
-                placeholder="Please enter some feedback before submitting.",
-            )
+            return gr.update(value="", placeholder="Please enter some feedback before submitting.")
 
         try:
             log_event(
@@ -1446,18 +1158,14 @@ with gr.Blocks(
                     "answer": last_a,
                     "feedback_text": text.strip(),
                     "model_name": model_name_val,
-                    "language": lang_pref,
+                    "language": normalize_lang_pref(lang_pref),
                     "learning_mode": mode_val,
                 }
             )
-            print("[Feedback] detailed_feedback logged to LangSmith.")
         except Exception as e:
             print("detailed_feedback log error:", e)
 
-        return gr.update(
-            value="",
-            placeholder="Thanks! Your feedback has been recorded.",
-        )
+        return gr.update(value="", placeholder="Thanks! Your feedback has been recorded.")
 
     feedback_submit_btn.click(
         submit_detailed_feedback,
@@ -1481,17 +1189,8 @@ with gr.Blocks(
     )
 
     summary_btn.click(
-        lambda h, c, w, cog, m, l: summarize_conversation(
-            h, c, w, cog, m, l
-        ),
-        [
-            chatbot,
-            course_outline_state,
-            weakness_state,
-            cognitive_state_state,
-            model_name,
-            language_preference,
-        ],
+        lambda h, c, w, cog, m, l: summarize_conversation(h, c, w, cog, m, normalize_lang_pref(l)),
+        [chatbot, course_outline_state, weakness_state, cognitive_state_state, model_name, language_preference],
         [result_display],
     )
 
@@ -1511,8 +1210,6 @@ with gr.Blocks(
             False,
             gr.update(interactive=False, value="👍 Helpful"),
             gr.update(interactive=False, value="👎 Not helpful"),
-            {},
-            {},
         )
 
     clear_btn.click(
@@ -1530,8 +1227,6 @@ with gr.Blocks(
             feedback_used_state,
             thumb_up_btn,
             thumb_down_btn,
-            perf_state,
-            perf_output,
         ],
         queue=False,
     )