Update app.py

app.py

@@ -61,6 +61,31 @@ CURRENT_INFO_RE = re.compile(r"\b(current|currently|latest|today|recent|recently
 RESEARCH_CUE_RE = re.compile(r"\b(find|search|look for|show me|get me|give me|locate|recommend|suggest|articles?|papers?|books?|journals?|studies|literature|research|systematic review|evidence|sources|database|databases|peer reviewed|open access)\b", re.IGNORECASE)
 LIBRARY_CUE_RE = re.compile(r"\b(library|librarian|borrow|loan|renew|fine|study room|room booking|reserve a room|account|my library|interlibrary|ill|khazna|orcid|open access|apc|refworks|libkey|hours|location|contact|visitor|alumni|database access|off campus|remote access)\b", re.IGNORECASE)
 MEDICAL_SEARCH_RE = re.compile(r"\b(pubmed|embase|cinahl|clinicalkey|cochrane|uptodate|medline|systematic review|clinical trial|biomedical literature|medical literature)\b", re.IGNORECASE)
+SOCIAL_RE = re.compile(r"^(hi|hello|hey|good morning|good afternoon|good evening|how are you|thanks|thank you|ok|okay|bye|goodbye|lol|haha|hehe|you are silly|are you silly|are you dumb|stupid bot|joke|tell me a joke)\b", re.IGNORECASE)
+PURE_GREETING_RE = re.compile(r"^(hi|hello|hey|good morning|good afternoon|good evening)[!.\s]*$", re.IGNORECASE)
+HOURS_RE = re.compile(r"\b(library hours|hours|opening hours|closing hours|open today|closed today|open now|when .*open|when .*close|what time .*open|what time .*close|opening time|closing time|is .*library open|is .*campus library open|habshan .*open|san .*open|main campus .*open|ramadan hours|weekend hours|friday hours|saturday hours)\b", re.IGNORECASE)
+KU_GENERAL_RE = re.compile(r"\b(khalifa university|ku)\b.*\b(admission|admissions|program|programs|degree|degrees|college|colleges|school|schools|tuition|fees|scholarship|scholarships|hostel|housing|transport|ranking|rankings|president|vice president|chancellor|application|apply|registrar|academic calendar|semester dates|campus map)\b|\b(admission|admissions|program|programs|degree|degrees|college|colleges|school|schools|tuition|fees|scholarship|scholarships|hostel|housing|transport|ranking|rankings|president|vice president|chancellor|application|apply|registrar|academic calendar|semester dates|campus map)\b.*\b(khalifa university|ku)\b", re.IGNORECASE)
+LIBRARY_HOURS_URL = "https://library.ku.ac.ae/hours"
+KU_MAIN_URL = "https://www.ku.ac.ae/"
+
+GROUNDED_LIBRARY_MAP = {
+    "ill": "interlibrary loan ILL document delivery full text unavailable article not available borrow from another library",
+    "fulltext": "full text libkey nomad article access pdf unavailable interlibrary loan",
+    "primo": "PRIMO discovery catalog holdings publication finder library has this journal books articles",
+    "circulation": "borrowing circulation renew loan period due date hold request fines my library account",
+    "research_help": "Ask a Librarian research consultation reference help subject guides research skills",
+    "orcid_oa": "ORCID open access APC publishing research impact Scopus SciVal bibliometrics Nikesh Narayanan",
+    "database_access": "database access e-resources remote access off campus login vendor issue Rani Anand",
+    "medical_help": "medical librarian Jason Fetty PubMed Embase CINAHL Cochrane UpToDate systematic review",
+    "systems_help": "Walter Brian Hall systems website technology digital services library systems",
+    "acquisitions": "Alia Al-Harrasi acquisitions request title suggest a book collection development",
+    "standards": "ASTM Compass IEEE standards ASME ASCE engineering standards",
+    "theses": "ProQuest Dissertations theses Khazna institutional repository dissertations theses",
+    "metrics": "impact factor journal citation reports JCR CiteScore Scopus SciVal metrics",
+    "chemistry": "ACS SciFindern RSC Reaxys chemistry database",
+    "physics": "APS AIP IOPScience physics database",
+    "engineering": "IEEE Xplore ACM ASCE ASME Knovel engineering databases",
+}
 
 # ===== GLOBALS =====
 vectorstore = None
@@ -119,15 +144,6 @@ def set_config(key, value):
     conn.close()
 
 
-def get_behavior_instructions():
-    base = get_config("bot_personality", "").strip()
-    extra = get_config("custom_instructions", "").strip()
-    if base and extra:
-        return base + "\n\nAdditional instructions:\n" + extra
-    return base or extra or "You are a helpful, friendly, and knowledgeable library assistant at Khalifa University, Abu Dhabi, UAE. KU = Khalifa University, NOT Kuwait University. Be concise and include relevant URLs when useful."
-
-
-
 # ===== ADMIN AUTH =====
 # ADMIN_PASSWORD must be set as HF Space Secret — no insecure fallback
 ADMIN_PASSWORD = os.environ.get("ADMIN_PASSWORD", "")
@@ -416,111 +432,11 @@ async def tool_library_info(question, history=None, model="gpt"):
         history_text = "\n".join(f"{'User' if m['role']=='user' else 'Assistant'}: {m['content']}" for m in history[-3:])
         base_query = f"{history_text}\n{question}"
 
-    # ── Semantic query expansion ──
-    # Common synonym mappings for library staff/service terms
-    # This improves FAISS retrieval when users use informal terms
-    SYNONYMS = {
-        "research librarian": "Research and Access Services Librarian Nikesh Narayanan research support",
-        "who is the librarian": "library staff contacts librarian",
-        "subject librarian": "Research and Access Services Librarian Nikesh Narayanan",
-        "medical librarian": "Jason Fetty Medical Librarian PubMed Embase CINAHL UpToDate",
-        "systems librarian": "Walter Brian Hall Digital Technology Services Librarian website systems technology",
-        "website issue": "Walter Brian Hall website systems technology",
-        "technology issue": "Walter Brian Hall website systems technology",
-        "acquisitions librarian": "Alia Al-Harrasi acquisitions collection development request title",
-        "public services": "Muna Ahmad Al Blooshi public services circulation general library services",
-        "e-resources librarian": "Rani Anand e-resources databases access problems vendor issues",
-        "database access": "Rani Anand e-resources databases access problems",
-        "open access": "Nikesh Narayanan open access APC scholarly communication",
-        "orcid": "Nikesh Narayanan ORCID researcher identifier profile",
-        "research impact": "Nikesh Narayanan research impact SciVal Scopus bibliometrics",
-        "library director": "Dr Abdulla Al Hefeiti Assistant Provost Libraries",
-        "ebook": "ebook central proquest download ebooks",
-        "ebooks": "ebook central proquest download ebooks",
-        "how to borrow": "borrowing loan period renew circulation",
-        "borrow books": "borrowing loan period renew circulation",
-        "renew book": "renew borrowing circulation My Library Account",
-        "renew loan": "renew borrowing circulation My Library Account",
-        "hold item": "request hold checked out item My Library Account",
-        "reserve item": "request hold checked out item My Library Account",
-        "access from home": "remote access off campus proxy no VPN",
-        "off campus": "remote access off campus proxy no VPN",
-        "remote access": "remote access off campus proxy no VPN",
-        "cite": "RefWorks citation reference management bibliography",
-        "citation": "RefWorks citation reference management bibliography",
-        "reference manager": "RefWorks citation bibliography",
-        "researcher id": "ORCID researcher identifier profile",
-        "impact factor": "journal citation reports JCR Scopus CiteScore SciVal",
-        "systematic review": "Cochrane Embase CINAHL PICO PubMed evidence based medicine",
-        "evidence based medicine": "Cochrane PubMed Embase CINAHL UpToDate",
-        "ai tools": "LeapSpace Scopus AI ScienceDirect AI EBSCO Research AI PRIMO AI",
-        "inter library loan": "interlibrary loan ILL document delivery article request",
-        "interlibrary loan": "interlibrary loan ILL document delivery article request",
-        "ill": "interlibrary loan ILL document delivery article request",
-        "document delivery": "interlibrary loan ILL document delivery article request",
-        "borrow from another library": "interlibrary loan ILL document delivery",
-        "request from another library": "interlibrary loan ILL document delivery",
-        "article not available": "full text article not available interlibrary loan LibKey Nomad",
-        "can't get article": "full text article not available interlibrary loan LibKey Nomad",
-        "cannot get article": "full text article not available interlibrary loan LibKey Nomad",
-        "don't get article": "full text article not available interlibrary loan LibKey Nomad",
-        "full text unavailable": "full text not available interlibrary loan LibKey Nomad",
-        "no full text": "full text not available interlibrary loan LibKey Nomad",
-        "full text": "full text LibKey Nomad article access PDF download",
-        "pdf": "full text LibKey Nomad article access PDF download",
-        "get article": "full text article access LibKey Nomad interlibrary loan",
-        "catalog": "PRIMO library catalog discovery holdings publication finder",
-        "library catalog": "PRIMO library catalog discovery holdings publication finder",
-        "discovery": "PRIMO library catalog discovery holdings publication finder",
-        "holdings": "PRIMO library catalog discovery holdings publication finder",
-        "does the library have": "PRIMO library catalog holdings publication finder",
-        "find journal": "PRIMO publication finder journal access holdings",
-        "publication finder": "PRIMO publication finder journal access holdings",
-        "circulation": "borrowing renew return hold loan period My Library Account",
-        "due date": "borrowing renew due date My Library Account",
-        "reserve room": "study room reserve rooms booking",
-        "book room": "study room reserve rooms booking",
-        "study room": "study room reserve rooms booking",
-        "ask librarian": "Ask a Librarian research help reference consultation",
-        "research help": "Ask a Librarian research consultation reference help",
-        "consultation": "Ask a Librarian research consultation reference help",
-        "peer reviewed": "peer reviewed scholarly journal article database filters",
-        "scholarly article": "peer reviewed scholarly journal article database filters",
-        "abstract": "article abstract citation database search results",
-        "doi": "DOI article identifier full text citation",
-        "call number": "PRIMO call number browse book location",
-        "course reserve": "reserve course materials reserve collection",
-        "visitor": "visitors external visitors access library",
-        "alumni": "alumni services library access alumni",
-        "civil engineering": "ASCE Library Knovel ScienceDirect Scopus ASTM Compass",
-        "mechanical engineering": "ASME Digital Library Knovel ScienceDirect ASTM Compass",
-        "electrical engineering": "IEEE Xplore INSPEC ScienceDirect",
-        "computer science": "ACM Digital Library IEEE Xplore arXiv ScienceDirect",
-        "artificial intelligence": "ACM Digital Library IEEE Xplore arXiv ScienceDirect Scopus",
-        "medicine": "PubMed Embase Cochrane UpToDate CINAHL",
-        "nursing": "CINAHL PubMed nursing allied health",
-        "chemistry": "ACS SciFindern RSC Journals Reaxys",
-        "physics": "APS Journals AIP IOPScience",
-        "business": "Business Source Complete Emerald ProQuest",
-        "dissertations": "ProQuest Dissertations Khazna theses",
-        "theses": "ProQuest Dissertations Khazna theses",
-        "standards": "ASTM Compass IEEE standards ASME ASCE",
-    }
-    question_lower = question.lower()
-    expanded_query = base_query
-    matched_expansions = []
-    for term, expansion in SYNONYMS.items():
-        if term in question_lower:
-            matched_expansions.append(expansion)
-    if matched_expansions:
-        unique_expansions = []
-        seen_exp = set()
-        for exp in matched_expansions:
-            if exp not in seen_exp:
-                seen_exp.add(exp)
-                unique_expansions.append(exp)
-        expanded_query = f"{base_query} {' '.join(unique_expansions[:4])}"
-        print(f"Query expanded with {len(unique_expansions[:4])} glossary hints")
+    # ── Semantic query expansion via lightweight LLM interpreter + grounded institutional map ──
+    interp = await _interpret_semantics(question, history or [])
+    grounding_text = " ".join(GROUNDED_LIBRARY_MAP.get(k, "") for k in interp.get("grounding_keys", []))
+    canonical_text = " ".join(interp.get("canonical_terms", []))
+    expanded_query = " ".join(x for x in [base_query, canonical_text, grounding_text] if x).strip()
 
     # ── FAISS scored search ──
     docs_with_scores = vectorstore.similarity_search_with_score(expanded_query, k=TOP_K)
@@ -548,10 +464,7 @@ async def tool_library_info(question, history=None, model="gpt"):
   start your answer with: "Did you mean [exact title from context]? If so, here is the information:"
   then give the answer.""" if moderate_match else ""
 
-    behavior = get_behavior_instructions()
-    prompt = f"""{behavior}
-
-You are the Khalifa University Library AI Assistant in Abu Dhabi, UAE.
+    prompt = f"""You are the Khalifa University Library AI Assistant in Abu Dhabi, UAE.
 KU means Khalifa University, NOT Kuwait University.
 
 RULES — follow exactly:
@@ -827,6 +740,31 @@ def _looks_medical_search(question: str) -> bool:
         return False
     return bool(MEDICAL_SEARCH_RE.search(q) or _looks_research_question(q))
 
+
+def _looks_library_hours_question(question: str) -> bool:
+    q = question.strip().lower()
+    return bool(HOURS_RE.search(q)) and ('library' in q or 'campus' in q or 'habshan' in q or 'san' in q or 'main campus' in q or 'hours' in q or 'open' in q or 'close' in q)
+
+def _looks_nonlibrary_ku_question(question: str) -> bool:
+    q = question.strip().lower()
+    if 'library' in q or 'librarian' in q or 'primo' in q or 'database' in q or 'book' in q or 'article' in q:
+        return False
+    return bool(KU_GENERAL_RE.search(question))
+
+def _hours_redirect_answer() -> str:
+    return (
+        f"For the most accurate opening and closing times, please check the official Library Hours page: "
+        f"<a href=\"{LIBRARY_HOURS_URL}\" target=\"_blank\">{LIBRARY_HOURS_URL}</a>. "
+        "Hours may vary by campus and during exams, holidays, and Ramadan."
+    )
+
+def _ku_general_redirect_answer() -> str:
+    return (
+        f"I’m <strong>LibBee</strong>, the Khalifa University <strong>Library</strong> AI Assistant, so I’m best for library resources, services, databases, research help, and staff contacts.<br><br>"
+        f"For general Khalifa University questions, please visit the main KU website: <a href=\"{KU_MAIN_URL}\" target=\"_blank\">{KU_MAIN_URL}</a>. "
+        "A broader university chatbot is available there for non-library questions."
+    )
+
 @app.post("/correct")
 async def correct_query(req: CorrectRequest):
     """
@@ -853,12 +791,38 @@ class GeneralRequest(BaseModel):
 
 async def _answer_general(question: str, history=None) -> dict:
     """Answer general knowledge questions using live web search when available."""
+    if _looks_library_hours_question(question):
+        return {"answer": _hours_redirect_answer(), "sources": [], "model": "library-hours-redirect"}
+    if _looks_nonlibrary_ku_question(question):
+        return {"answer": _ku_general_redirect_answer(), "sources": [], "model": "ku-general-redirect"}
+    if _looks_social_or_greeting(question):
+        if PURE_GREETING_RE.match(question.strip()):
+            guided = (
+                "Hi! I’m <strong>LibBee</strong>, the Khalifa University Library AI Assistant.<br><br>"
+                "I can help you with articles and books, databases, full text, Interlibrary Loan (ILL), "
+                "library services, staff contacts, ORCID, RefWorks, and Open Access.<br><br>"
+                "Are you looking for one of these? You can also type your question directly."
+            )
+            return {"answer": guided, "sources": [], "model": "libbee-greeting"}
+        try:
+            behavior = get_behavior_instructions()
+            llm = ChatOpenAI(model="gpt-4o-mini", temperature=0.4, max_tokens=220)
+            msgs = []
+            if behavior:
+                msgs.append({"role": "system", "content": behavior + "\nIf the user greets you, jokes, or says something mildly mocking, reply politely, warmly, and briefly. Do not be defensive. Keep it light and helpful."})
+            for m in (history or [])[-4:]:
+                if m.get("role") in ("user", "assistant"):
+                    msgs.append({"role": m["role"], "content": m.get("content", "")})
+            msgs.append({"role": "user", "content": question})
+            response = llm.invoke(msgs)
+            return {"answer": response.content.strip(), "sources": [], "model": "gpt-4o-mini-social"}
+        except Exception:
+            return {"answer": "Hi! I’m <strong>LibBee</strong>, the Khalifa University Library AI Assistant. I can help with articles, books, databases, full text, ILL, library services, and staff contacts. What would you like help with?", "sources": [], "model": "fallback-social"}
     try:
         import openai
         client = openai.OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
 
-        behavior = get_behavior_instructions()
-        messages = [{"role": "system", "content": behavior}]
+        messages = []
         if history:
             for m in history[-5:]:
                 role = m.get("role", "user")
@@ -893,7 +857,7 @@ async def _answer_general(question: str, history=None) -> dict:
         print(f"Web search failed, falling back to plain GPT: {e}")
         try:
             llm = ChatOpenAI(model="gpt-4o-mini", temperature=0.3, max_tokens=500)
-            msgs = [{"role": "system", "content": get_behavior_instructions()}]
+            msgs = []
             for m in (history or [])[-5:]:
                 if m.get("role") in ("user", "assistant"):
                     msgs.append({"role": m["role"], "content": m.get("content", "")})
@@ -1001,50 +965,83 @@ async def agent_query(req: AgentRequest):
     history = [{"role": m.role, "content": m.content} for m in req.history] if req.history else []
     use_claude = req.model == "claude" and bool(os.environ.get("ANTHROPIC_API_KEY"))
 
+    # ---- Early greeting handling ----
+    if PURE_GREETING_RE.match(question):
+        answer = (
+            "Hi! I’m <strong>LibBee</strong>, the Khalifa University Library AI Assistant.<br><br>"
+            "I can help you find articles and books, search databases, access full text, request Interlibrary Loan (ILL), "
+            "and answer questions about library services and staff.<br><br>"
+            "Are you looking for one of these? You can also type your question directly."
+        )
+        elapsed = time.time() - start
+        return {
+            "answer": answer,
+            "intent": "social_greeting",
+            "tools_used": [],
+            "search_results": [],
+            "sources": [],
+            "model_used": req.model,
+            "response_time": round(elapsed, 2),
+            "corrected_query": question,
+            "natural_query": question,
+            "database_query": question,
+            "original_question": question,
+            "is_follow_up": False,
+            "source_mode": "social",
+        }
+
+    if _looks_library_hours_question(question):
+        elapsed = time.time() - start
+        return {
+            "answer": _hours_redirect_answer(),
+            "intent": "library_info",
+            "tools_used": ["hours_redirect"],
+            "search_results": [],
+            "sources": [{"title": "Library Hours", "source": LIBRARY_HOURS_URL}],
+            "model_used": req.model,
+            "response_time": round(elapsed, 2),
+            "corrected_query": question,
+            "natural_query": question,
+            "database_query": question,
+        }
+
+    if _looks_nonlibrary_ku_question(question):
+        elapsed = time.time() - start
+        return {
+            "answer": _ku_general_redirect_answer(),
+            "intent": "general",
+            "tools_used": ["ku_general_redirect"],
+            "search_results": [],
+            "sources": [],
+            "model_used": req.model,
+            "response_time": round(elapsed, 2),
+            "corrected_query": question,
+            "natural_query": question,
+            "database_query": question,
+        }
+
     intent = "general"
     natural_query = question
     database_query = question
     search_plan = None
 
     try:
-        if _looks_library_question(question):
-            intent = "library_info"
-        elif _looks_current_question(question):
-            intent = "general_recent"
-        elif _looks_medical_search(question):
-            intent = "search_medical"
-        elif _looks_research_question(question):
-            intent = "search_academic"
-        else:
-            classifier_prompt = f"""You are routing a question for a university library assistant.
-
-Return ONLY valid JSON with one intent: library_info, search_academic, search_medical, general, or general_recent.
-
-Rules:
-- library_info: KU Library services, librarians, borrowing, rooms, accounts, policies, databases access
-- search_medical: user wants medical/clinical literature, articles, books, evidence, or databases
-- search_academic: user wants literature, books, articles, papers, evidence, or academic search on any non-medical topic
-- general_recent: the answer depends on current or recent information, news, or current office-holders
-- general: simple general-knowledge or conversational questions not asking for academic search
-
-Question: "{question}"
-
-Return JSON like: {{"intent":"general_recent"}}"""
-            if use_claude:
-                from langchain_anthropic import ChatAnthropic
-                clf_llm = ChatAnthropic(model="claude-haiku-4-5-20251001", temperature=0, max_tokens=120)
-            else:
-                clf_llm = ChatOpenAI(model="gpt-4o-mini", temperature=0, max_tokens=120)
-            clf_resp = clf_llm.invoke(classifier_prompt)
-            clf = _extract_json_object(clf_resp.content.strip())
-            intent = clf.get("intent", "general")
-
+        interp = await _interpret_semantics(question, history)
+        intent = interp.get("intent_hint", "general")
+        if interp.get("social"):
+            intent = "general"
         if intent in ("search_academic", "search_medical"):
             search_plan = await _build_search_plan(question)
             natural_query = search_plan["natural"]
             database_query = search_plan["database_query"] or search_plan["corrected"]
+        elif intent == "library_info" and interp.get("grounding_keys"):
+            # strengthen RAG using interpreted canonical library terms
+            resolved = " ".join([question] + interp.get("canonical_terms", []) + [GROUNDED_LIBRARY_MAP.get(k, "") for k in interp.get("grounding_keys", []) if GROUNDED_LIBRARY_MAP.get(k)])
+            question = re.sub(r"\s+", " ", resolved).strip()
     except Exception:
-        if _looks_research_question(question) or _looks_medical_search(question):
+        if _looks_social_or_greeting(question):
+            intent = "general"
+        elif _looks_research_question(question) or _looks_medical_search(question):
             intent = "search_medical" if _looks_medical_search(question) else "search_academic"
             search_plan = await _build_search_plan(question)
             natural_query = search_plan["natural"]
@@ -1114,7 +1111,10 @@ Return JSON like: {{"intent":"general_recent"}}"""
         context_parts.append(f"Natural query for AI tools: {natural_query}")
         context_parts.append(f"Database query for PRIMO/PubMed: {database_query}")
 
-    synthesis_prompt = f"""You are the Khalifa University Library AI Assistant (Abu Dhabi, UAE). KU = Khalifa University.
+    behavior = get_behavior_instructions()
+    synthesis_prompt = f"""{behavior}
+
+You are the Khalifa University Library AI Assistant (Abu Dhabi, UAE). KU = Khalifa University.
 Be concise (3-5 sentences).
 - For library_info, answer from the library context and include URLs when useful.
 - For search intents, briefly summarise the search direction and mention the top 2-3 relevant results if present.
@@ -1368,8 +1368,6 @@ function st(el,t){{document.querySelectorAll('.tab').forEach(e=>e.classList.remo
 
 // Fetch analytics from Cloudflare D1
 fetch(W+'/analytics').then(r=>r.json()).then(d=>{{
-  const helpful = (d.feedback_summary||[]).find(x=>x.feedback==='up')?.c || 0;
-  const notHelpful = (d.feedback_summary||[]).find(x=>x.feedback==='down')?.c || 0;
   const el=document.getElementById('t-analytics');
   el.innerHTML=`
   <div class="grid">
@@ -1378,44 +1376,20 @@ fetch(W+'/analytics').then(r=>r.json()).then(d=>{{
     <div class="stat"><div class="n">${{d.week}}</div><div class="l">This Week</div></div>
     <div class="stat"><div class="n">${{(d.avg_time||0).toFixed(1)}}s</div><div class="l">Avg Time</div></div>
     <div class="stat"><div class="n">${{d.errors}}</div><div class="l">Errors</div></div>
-    <div class="stat"><div class="n">${{helpful}}</div><div class="l">👍 Helpful</div></div>
-    <div class="stat"><div class="n">${{notHelpful}}</div><div class="l">👎 Not Helpful</div></div>
-  </div>
-  <div style="display:flex;gap:8px;flex-wrap:wrap;margin-bottom:12px">
-    <a class="btn bp" href="${{W}}/analytics/export?kind=queries" target="_blank">⬇️ Download Queries CSV</a>
-    <a class="btn bp" href="${{W}}/analytics/export?kind=feedback" target="_blank">⬇️ Download Feedback CSV</a>
-    <a class="btn bp" href="${{W}}/analytics/export?kind=clicks" target="_blank">⬇️ Download Clicks CSV</a>
-    <button class="btn br" onclick="deleteOldData()">🗑️ Delete Old Data</button>
   </div>
   <div class="two">
-    <div class="card"><h2>Tool Usage</h2><table><tr><th>Tool</th><th>Count</th></tr>${{(d.tools||[]).map(t=>`<tr><td>${{t.tool_used}}</td><td>${{t.c}}</td></tr>`).join('')}}</table></div>
-    <div class="card"><h2>Model Usage</h2><table><tr><th>Model</th><th>Count</th></tr>${{(d.models||[]).map(m=>`<tr><td>${{m.model}}</td><td>${{m.c}}</td></tr>`).join('')}}</table></div>
+    <div class="card"><h2>Tool Usage</h2><table><tr><th>Tool</th><th>Count</th></tr>${{d.tools.map(t=>`<tr><td>${{t.tool_used}}</td><td>${{t.c}}</td></tr>`).join('')}}</table></div>
+    <div class="card"><h2>Model Usage</h2><table><tr><th>Model</th><th>Count</th></tr>${{d.models.map(m=>`<tr><td>${{m.model}}</td><td>${{m.c}}</td></tr>`).join('')}}</table></div>
   </div>
   <div class="two">
     <div class="card"><h2>Hourly</h2><canvas id="hc"></canvas></div>
     <div class="card"><h2>Daily (14d)</h2><canvas id="dc"></canvas></div>
   </div>
-  <div class="two">
-    <div class="card"><h2>Recent Feedback</h2><table><tr><th>Time</th><th>Question</th><th>Feedback</th></tr>${{(d.recent_feedback||[]).slice(0,15).map(f=>`<tr><td>${{(f.timestamp||'').substring(0,19)}}</td><td title="${{f.question||''}}">${{(f.question||'').substring(0,55)}}</td><td>${{f.feedback==='up'?'👍':'👎'}}</td></tr>`).join('') || '<tr><td colspan="3">No feedback yet</td></tr>'}}</table></div>
-    <div class="card"><h2>Top Weak Questions</h2><table><tr><th>Question</th><th>👎</th></tr>${{(d.weak_questions||[]).map(q=>`<tr><td title="${{q.question||''}}">${{(q.question||'').substring(0,70)}}</td><td>${{q.c}}</td></tr>`).join('') || '<tr><td colspan="2">No weak questions yet</td></tr>'}}</table></div>
-  </div>
-  <div class="two">
-    <div class="card"><h2>Top Clicks</h2><table><tr><th>Label</th><th>Type</th><th>Count</th></tr>${{(d.top_clicks||[]).map(c=>`<tr><td title="${{c.label||''}}">${{(c.label||'').substring(0,55)}}</td><td>${{c.target_type}}</td><td>${{c.c}}</td></tr>`).join('') || '<tr><td colspan="3">No clicks yet</td></tr>'}}</table></div>
-    <div class="card"><h2>Popular (Top 20)</h2><table><tr><th>Question</th><th>Count</th></tr>${{(d.popular||[]).map(p=>`<tr><td>${{(p.question||'').substring(0,70)}}</td><td>${{p.c}}</td></tr>`).join('')}}</table></div>
-  </div>`;
-  if((d.hourly||[]).length)new Chart(document.getElementById('hc'),{{type:'bar',data:{{labels:d.hourly.map(h=>h.hour+':00'),datasets:[{{label:'Q',data:d.hourly.map(h=>h.c),backgroundColor:'#003366'}}]}},options:{{responsive:true,plugins:{{legend:{{display:false}}}}}}}});
-  if((d.daily||[]).length)new Chart(document.getElementById('dc'),{{type:'line',data:{{labels:d.daily.map(x=>(x.day||'').slice(5)),datasets:[{{label:'Q',data:d.daily.map(x=>x.c),borderColor:'#003366',backgroundColor:'rgba(0,51,102,0.1)',fill:true,tension:.3}}]}},options:{{responsive:true,plugins:{{legend:{{display:false}}}}}}}});
+  <div class="card"><h2>Popular (Top 20)</h2><table><tr><th>Question</th><th>Count</th></tr>${{d.popular.map(p=>`<tr><td>${{(p.question||'').substring(0,70)}}</td><td>${{p.c}}</td></tr>`).join('')}}</table></div>`;
+  if(d.hourly.length)new Chart(document.getElementById('hc'),{{type:'bar',data:{{labels:d.hourly.map(h=>h.hour+':00'),datasets:[{{label:'Q',data:d.hourly.map(h=>h.c),backgroundColor:'#003366'}}]}},options:{{responsive:true,plugins:{{legend:{{display:false}}}}}}}});
+  if(d.daily.length)new Chart(document.getElementById('dc'),{{type:'line',data:{{labels:d.daily.map(x=>(x.day||'').slice(5)),datasets:[{{label:'Q',data:d.daily.map(x=>x.c),borderColor:'#003366',backgroundColor:'rgba(0,51,102,0.1)',fill:true,tension:.3}}]}},options:{{responsive:true,plugins:{{legend:{{display:false}}}}}}}});
 }}).catch(e=>{{document.getElementById('t-analytics').innerHTML='<div class="card" style="color:#dc2626">Failed to load analytics: '+e.message+'<br>Make sure D1 is initialized: <a href="'+W+'/analytics/init" target="_blank">Click here to init DB</a></div>';}});
 
-async function deleteOldData(){{
-  const days = prompt('Delete records older than how many days?', '90');
-  if(!days) return;
-  const r = await fetch(W+'/analytics/delete-old', {{method:'POST', headers:{{'Content-Type':'application/json'}}, body:JSON.stringify({{days}})}});
-  const d = await r.json();
-  alert(d.status==='ok' ? `Deleted data older than ${{d.deleted_older_than_days}} days.` : (d.error?.message || 'Delete failed'));
-  location.reload();
-}}
-
 // Fetch recent queries
 fetch(W+'/analytics/recent').then(r=>r.json()).then(d=>{{
   const el=document.getElementById('t-queries');
@@ -1615,4 +1589,4 @@ async def clear_logs():
     conn.execute("DELETE FROM queries")
     conn.commit()
     conn.close()
-    return {"status": "ok", "message": "All logs cleared"}
+    return {"status": "ok", "message": "All logs cleared"}