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app.py

@@ -1,68 +1,88 @@
+"""
+PlayPulse Intelligence — Flask App  (v2)
+─────────────────────────────────────────
+Key improvements over v1
+  • Chat has conversation memory (per session, server-side deque)
+  • Intent router is enum-strict + falls back properly
+  • 6 inline chat tools (no agent needed for simple queries)
+  • Agent is one of those tools — called only for deep analysis
+  • /chat returns structured payload: reply + optional table / chart_data / agent_data
+  • "tabular format" requests produce real table JSON the frontend can render
+"""
+
 import urllib.parse
 import math
-import requests
 import re
-from flask import Flask, request, render_template, jsonify
+import json
+import requests
+from collections import deque, defaultdict
+from datetime import datetime
+from flask import Flask, request, render_template, jsonify, session
 from google_play_scraper import reviews, Sort, search, app as app_info
+import pandas as pd
+from utils.agents import run_agent, build_llm
+import os
 
 app = Flask(__name__)
+app.secret_key = os.getenv("FLASK_SECRET", "playpulse-secret-2026")
+
+# ── Per-session conversation memory (server-side, max 20 turns) ───────────
+# key: session_id → deque of {"role": "user"|"assistant", "content": str}
+_CONV_MEMORY: dict[str, deque] = defaultdict(lambda: deque(maxlen=20))
+
+MAX_HISTORY_FOR_LLM = 6   # last N turns sent to LLM for context
+
+
+# ═══════════════════════════════════════════════════════════════════════════
+#  SCRAPER HELPERS  (unchanged from v1)
+# ═══════════════════════════════════════════════════════════════════════════
 
-# --- The Rock-Solid Old App Logic ---
 def extract_app_id(url_or_name: str) -> str:
     url_or_name = url_or_name.strip()
-    
-    # 1. If it's a Play Store URL, parse out the 'id' param safely
     if "play.google.com" in url_or_name:
         parsed = urllib.parse.urlparse(url_or_name)
-        query_params = urllib.parse.parse_qs(parsed.query)
-        if 'id' in query_params:
-            return query_params['id'][0]
-            
-    # 2. If it's a raw package name (e.g. com.whatsapp) with no spaces
+        qp = urllib.parse.parse_qs(parsed.query)
+        if 'id' in qp:
+            return qp['id'][0]
     if "." in url_or_name and " " not in url_or_name:
         return url_or_name
-        
     return ""
 
+
 def scrape_store_ids(query: str, n_hits: int = 5):
-    """Directly scrape Play Store search results page to get valid package IDs."""
     try:
         url = f"https://play.google.com/store/search?q={urllib.parse.quote(query)}&c=apps"
-        headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"}
+        headers = {"User-Agent": "Mozilla/5.0"}
         resp = requests.get(url, headers=headers, timeout=10)
-        if resp.status_code != 200: return []
-        
-        # Regex to find package names like details?id=com.something
+        if resp.status_code != 200:
+            return []
         pids = re.findall(r'details\?id=([a-zA-Z0-9._]+)', resp.text)
-        
-        # Remove duplicates and known junk
-        unique_pids = []
+        unique: list[str] = []
         for p in pids:
-            if p not in unique_pids and "None" not in p:
-                unique_pids.append(p)
-        return unique_pids[:n_hits]
-    except:
+            if p not in unique and "None" not in p:
+                unique.append(p)
+        return unique[:n_hits]
+    except Exception:
         return []
 
+
 def serialize_review(r: dict) -> dict:
-    """Return all useful fields from a review, with dates serialized to ISO strings."""
     return {
         "reviewId":             r.get("reviewId", ""),
         "userName":             r.get("userName", ""),
-        "userImage":            r.get("userImage", ""),        # avatar URL
+        "userImage":            r.get("userImage", ""),
         "content":              r.get("content", ""),
-        "score":                r.get("score", 0),             # 1-5 stars
-        "thumbsUpCount":        r.get("thumbsUpCount", 0),     # helpful votes
-        "reviewCreatedVersion": r.get("reviewCreatedVersion", ""),  # app version when review was written
-        "at":                   r["at"].isoformat() if r.get("at") else "",          # review date
-        "replyContent":         r.get("replyContent", "") or "",     # developer reply text
-        "repliedAt":            r["repliedAt"].isoformat() if r.get("repliedAt") else "",  # dev reply date
+        "score":                r.get("score", 0),
+        "thumbsUpCount":        r.get("thumbsUpCount", 0),
+        "reviewCreatedVersion": r.get("reviewCreatedVersion", ""),
+        "at":                   r["at"].isoformat() if r.get("at") else "",
+        "replyContent":         r.get("replyContent", "") or "",
+        "repliedAt":            r["repliedAt"].isoformat() if r.get("repliedAt") else "",
     }
 
+
 def fetch_app_reviews(app_id, review_count, sort_order, star_ratings_input):
-    """Core helper to fetch reviews for a single app ID."""
     info = app_info(app_id, lang='en', country='us')
-    
     sort_map = {
         'MOST_RELEVANT': Sort.MOST_RELEVANT,
         'NEWEST':        Sort.NEWEST,
@@ -78,69 +98,600 @@ def fetch_app_reviews(app_id, review_count, sort_order, star_ratings_input):
             reverse=True
         )
 
-    per_bucket = math.ceil(review_limit_val(review_count) / len(star_filters))
-    all_reviews = []
-    seen_ids = set()
+    per_bucket = math.ceil(_review_limit(review_count) / len(star_filters))
+    all_reviews: list[dict] = []
+    seen_ids: set[str] = set()
 
     for star in star_filters:
         result, _ = reviews(
-            app_id,
-            lang='en',
-            country='us',
-            sort=selected_sort,
-            count=per_bucket,
+            app_id, lang='en', country='us',
+            sort=selected_sort, count=per_bucket,
             filter_score_with=star,
         )
         for r in result:
             rid = r.get('reviewId', '')
             if rid not in seen_ids:
                 seen_ids.add(rid)
-                serialized = serialize_review(r)
-                serialized['appTitle'] = info['title']
-                serialized['appId'] = app_id
-                all_reviews.append(serialized)
-                
+                s = serialize_review(r)
+                s['appTitle'] = info['title']
+                s['appId']    = app_id
+                all_reviews.append(s)
+
     return info, all_reviews
 
-def review_limit_val(count):
-    try: return int(count)
-    except: return 150
+
+def _review_limit(count):
+    try:
+        return int(count)
+    except Exception:
+        return 150
+
+
+# ═══════════════════════════════════════════════════════════════════════════
+#  INLINE CHAT TOOLS  (fast, no heavy agent needed for simple queries)
+# ═══════════════════════════════════════════════════════════════════════════
+
+def _tool_rating_breakdown(df: pd.DataFrame) -> dict:
+    """Star rating distribution across all reviews."""
+    dist = df["score"].value_counts().sort_index()
+    total = max(1, len(df))
+    rows = [
+        {
+            "Stars":      f"{'★' * int(s)} ({int(s)})",
+            "Count":      int(c),
+            "Percentage": f"{round(c/total*100,1)}%",
+        }
+        for s, c in dist.items()
+    ]
+    return {
+        "table": {
+            "title":   "Rating Distribution",
+            "columns": ["Stars", "Count", "Percentage"],
+            "rows":    rows,
+        },
+        "summary": f"{len(df)} reviews: avg {round(df['score'].mean(),2)}/5",
+    }
+
+
+def _tool_app_comparison(df: pd.DataFrame) -> dict:
+    """Per-app avg rating + negative % table."""
+    if "appId" not in df.columns and "appTitle" not in df.columns:
+        return {"error": "No app column in data"}
+
+    app_col = "appTitle" if "appTitle" in df.columns else "appId"
+    rows = []
+    for app_name, grp in df.groupby(app_col):
+        sc = pd.to_numeric(grp["score"], errors="coerce")
+        rows.append({
+            "App":        str(app_name),
+            "Reviews":    len(grp),
+            "Avg Rating": f"{round(float(sc.mean()),2)} ★",
+            "% Negative": f"{round(float((sc <= 2).mean()*100),1)}%",
+            "% Positive": f"{round(float((sc >= 4).mean()*100),1)}%",
+        })
+    rows.sort(key=lambda x: x["Avg Rating"])
+    return {
+        "table": {
+            "title":   "App Comparison",
+            "columns": ["App", "Reviews", "Avg Rating", "% Negative", "% Positive"],
+            "rows":    rows,
+        },
+        "summary": f"Compared {len(rows)} apps",
+    }
+
+
+def _tool_top_reviews(df: pd.DataFrame, min_stars: int = 1,
+                       max_stars: int = 2, n: int = 5,
+                       app_filter: str = "") -> dict:
+    """Filtered review list as table."""
+    sc = pd.to_numeric(df["score"], errors="coerce")
+    mask = (sc >= min_stars) & (sc <= max_stars)
+    if app_filter:
+        app_col = "appTitle" if "appTitle" in df.columns else "appId"
+        mask &= df[app_col].astype(str).str.lower().str.contains(
+            re.escape(app_filter.lower()), na=False)
+
+    subset = df[mask].head(n)
+    tc = "content" if "content" in df.columns else df.columns[0]
+    app_col = "appTitle" if "appTitle" in df.columns else ("appId" if "appId" in df.columns else None)
+
+    rows = []
+    for _, r in subset.iterrows():
+        row = {
+            "User":   str(r.get("userName", ""))[:20],
+            "Stars":  "★" * int(r.get("score", 0)),
+            "Review": str(r.get(tc, ""))[:120],
+        }
+        if app_col:
+            row["App"] = str(r.get(app_col, ""))
+        if "thumbsUpCount" in df.columns:
+            row["Helpful"] = int(r.get("thumbsUpCount", 0))
+        rows.append(row)
+
+    label = f"{min_stars}–{max_stars} star"
+    cols = list(rows[0].keys()) if rows else []
+    return {
+        "table": {
+            "title":   f"Top {label} Reviews" + (f" — {app_filter}" if app_filter else ""),
+            "columns": cols,
+            "rows":    rows,
+        },
+        "summary": f"Showing {len(rows)} of {int(mask.sum())} matching reviews",
+    }
+
+
+def _tool_top_helpful(df: pd.DataFrame, n: int = 5) -> dict:
+    """Most helpful reviews."""
+    if "thumbsUpCount" not in df.columns:
+        return {"error": "No helpful votes column"}
+    df2   = df.copy()
+    df2["__h"] = pd.to_numeric(df2["thumbsUpCount"], errors="coerce").fillna(0)
+    subset = df2.nlargest(n, "__h")
+    tc = "content" if "content" in df.columns else df.columns[0]
+    app_col = "appTitle" if "appTitle" in df.columns else None
+
+    rows = []
+    for _, r in subset.iterrows():
+        row = {
+            "Stars":   "★" * int(r.get("score", 0)),
+            "Helpful": int(r.get("thumbsUpCount", 0)),
+            "Review":  str(r.get(tc, ""))[:120],
+        }
+        if app_col:
+            row["App"] = str(r.get(app_col, ""))
+        rows.append(row)
+    return {
+        "table": {
+            "title":   "Most Helpful Reviews",
+            "columns": list(rows[0].keys()) if rows else [],
+            "rows":    rows,
+        },
+        "summary": f"Top {len(rows)} most helpful reviews",
+    }
+
+
+def _tool_keyword_search(df: pd.DataFrame, keyword: str, n: int = 8) -> dict:
+    """Search review text for keyword."""
+    tc = "content" if "content" in df.columns else df.columns[0]
+    mask = df[tc].astype(str).str.lower().str.contains(
+        re.escape(keyword.lower()), na=False)
+    subset = df[mask].head(n)
+    app_col = "appTitle" if "appTitle" in df.columns else None
+
+    rows = []
+    for _, r in subset.iterrows():
+        row = {
+            "Stars":  "★" * int(r.get("score", 0)),
+            "Review": str(r.get(tc, ""))[:150],
+        }
+        if app_col:
+            row["App"] = str(r.get(app_col, ""))
+        rows.append(row)
+    return {
+        "table": {
+            "title":   f'Reviews mentioning "{keyword}"',
+            "columns": list(rows[0].keys()) if rows else [],
+            "rows":    rows,
+        },
+        "summary": f"Found {int(mask.sum())} reviews mentioning '{keyword}'",
+    }
+
+
+# ═══════════════════════════════════════════════════════════════════════════
+#  INTENT CLASSIFIER  (enum-strict, multi-class)
+# ═══════════════════════════════════════════════════════════════════════════
+
+INTENT_SYSTEM = """You are an intent classifier for a game-review chat assistant.
+Classify the user message into EXACTLY ONE of these intents:
+
+  TABLE        — user wants data in tabular / structured / list format
+  COMPARISON   — comparing apps / games against each other
+  KEYWORD      — wants to search for a specific word/phrase in reviews
+  HELPFUL      — wants the most helpful / upvoted reviews
+  ANALYSIS     — deep insight, summary, cluster analysis, sentiment, recommendations
+  FILTER       — filtering the visible table (show only X stars, only app Y)
+  GREETING     — hi, hello, thanks, small talk
+  GENERAL      — questions about features, how to use the tool, unrelated
+
+Return ONLY one word from the list above. No explanation."""
+
+
+def classify_intent(message: str, llm) -> str:
+    from langchain_core.messages import HumanMessage, SystemMessage
+    try:
+        resp = llm.invoke([
+            SystemMessage(content=INTENT_SYSTEM),
+            HumanMessage(content=f'Message: "{message}"'),
+        ])
+        raw = getattr(resp, "content", str(resp)).strip().upper().split()[0]
+        valid = {"TABLE","COMPARISON","KEYWORD","HELPFUL","ANALYSIS","FILTER","GREETING","GENERAL"}
+        return raw if raw in valid else "ANALYSIS"
+    except Exception:
+        return "ANALYSIS"
+
+
+# ═════════════════════════════════���═════════════════════════════════════════
+#  PARAMETER EXTRACTOR  (LLM extracts structured params from natural language)
+# ═══════════════════════════════════════════════════════════════════════════
+
+def extract_params(message: str, intent: str, llm, apps: list[str]) -> dict:
+    """Extract structured parameters from a message given its intent."""
+    app_list_str = ", ".join(apps[:10]) if apps else "none"
+
+    system = f"""Extract parameters from the user message for intent={intent}.
+Known app names in dataset: [{app_list_str}]
+
+Return ONLY valid JSON (no markdown):
+{{
+  "min_stars": 1-5 or null,
+  "max_stars": 1-5 or null,
+  "n": integer count or 5,
+  "app_filter": "exact app name or title from known list, or empty string",
+  "keyword": "search term or empty string",
+  "metric": "avg_rating|pct_negative|pct_positive|count or empty"
+}}"""
+
+    from langchain_core.messages import HumanMessage, SystemMessage
+    try:
+        resp = llm.invoke([
+            SystemMessage(content=system),
+            HumanMessage(content=message),
+        ])
+        raw = getattr(resp, "content", str(resp)).strip()
+        raw = re.sub(r"^```(?:json)?", "", raw).strip().rstrip("```")
+        return json.loads(raw)
+    except Exception:
+        return {"min_stars": None, "max_stars": None, "n": 5,
+                "app_filter": "", "keyword": "", "metric": ""}
+
+
+# ═══════════════════════════════════════════════════════════════════════════
+#  RESPONSE FORMATTER  (converts tool output + agent report → rich reply)
+# ═══════════════════════════════════════════════════════════════════════════
+
+def _format_agent_report(report: dict) -> str:
+    """Convert agent report dict into a well-structured markdown-like text reply."""
+    parts = []
+
+    if report.get("direct_answer"):
+        parts.append(report["direct_answer"])
+
+    problems = report.get("top_problems", [])
+    if problems:
+        parts.append("\n**Top Issues:**")
+        for i, p in enumerate(problems[:4], 1):
+            sev   = p.get("severity","").upper()
+            issue = p.get("issue","")
+            desc  = p.get("description","")
+            ev    = p.get("evidence","")
+            parts.append(f"{i}. **{issue}** [{sev}] — {desc}" + (f' _"{ev}"_' if ev else ""))
+
+    strengths = report.get("key_strengths", [])
+    if strengths:
+        parts.append("\n**What Users Love:**")
+        for s in strengths[:3]:
+            parts.append(f"• **{s.get('strength','')}** — {s.get('description','')}")
+
+    recs = report.get("recommendations", [])
+    if recs:
+        parts.append("\n**Recommendations:**")
+        for i, r in enumerate(recs[:3], 1):
+            parts.append(f"{i}. [{r.get('priority','').upper()}] {r.get('action','')} — {r.get('rationale','')}")
+
+    return "\n".join(parts) if parts else report.get("executive_summary", "Analysis complete.")
+
+
+def _build_agent_table(report: dict, app_breakdown: list) -> dict | None:
+    """If agent ran app_comparison tool, surface it as a table."""
+    if not app_breakdown:
+        return None
+    rows = [
+        {
+            "App":        a.get("app",""),
+            "Reviews":    a.get("count",""),
+            "Avg Rating": f"{a.get('avg_rating','?')} ★",
+            "% Negative": f"{a.get('pct_negative','?')}%",
+            "% Positive": f"{a.get('pct_positive','?')}%",
+        }
+        for a in app_breakdown
+    ]
+    return {
+        "title":   "App Breakdown",
+        "columns": ["App","Reviews","Avg Rating","% Negative","% Positive"],
+        "rows":    rows,
+    }
+
+
+# ═══════════════════════════════════════════════════════════════════════════
+#  /chat ENDPOINT  — the core of PlayPulse Intelligence
+# ═══════════════════════════════════════════════════════════════════════════
+
+@app.route('/chat', methods=['POST'])
+def chat():
+    try:
+        data           = request.json or {}
+        user_message   = data.get('message', '').strip()
+        current_reviews = data.get('reviews', [])
+        session_id     = data.get('session_id') or request.remote_addr or "default"
+
+        if not user_message:
+            return jsonify({"error": "No message provided"}), 400
+
+        llm = build_llm()
+        if not llm:
+            return jsonify({"reply": "AI service unavailable — no API key configured.", "type": "error"})
+
+        # ── Conversation memory ────────────────────────────────────────────
+        memory = _CONV_MEMORY[session_id]
+        memory.append({"role": "user", "content": user_message})
+
+        # ── Build context from reviews ─────────────────────────────────────
+        df = pd.DataFrame(current_reviews) if current_reviews else pd.DataFrame()
+        has_data = not df.empty
+
+        # Detected app names for parameter extraction
+        apps: list[str] = []
+        if has_data:
+            for col in ["appTitle", "appId"]:
+                if col in df.columns:
+                    apps = df[col].dropna().astype(str).unique().tolist()
+                    break
+
+        # ── Classify intent ────────────────────────────────────────────────
+        intent = classify_intent(user_message, llm)
+        print(f"[ChatRouter] Intent: {intent} | has_data: {has_data} | apps: {apps[:3]}")
+
+        # ── Handle GREETING / GENERAL ──────────────────────────────────────
+        if intent in ("GREETING", "GENERAL"):
+            from langchain_core.messages import HumanMessage, SystemMessage
+            history_msgs = []
+            for turn in list(memory)[-MAX_HISTORY_FOR_LLM:]:
+                if turn["role"] == "user":
+                    history_msgs.append(HumanMessage(content=turn["content"]))
+                else:
+                    from langchain_core.messages import AIMessage
+                    history_msgs.append(AIMessage(content=turn["content"]))
+
+            sys_msg = SystemMessage(content=(
+                "You are PlayPulse Intelligence, a friendly AI assistant for analyzing "
+                "Google Play Store reviews. Be helpful, concise, and conversational. "
+                "If the user greets you, greet back briefly. "
+                "If they ask what you can do, explain you can analyze reviews, compare apps, "
+                "find issues, show ratings, and answer questions about the scraped data."
+            ))
+            resp = llm.invoke([sys_msg] + history_msgs)
+            reply = getattr(resp, "content", str(resp)).strip()
+            memory.append({"role": "assistant", "content": reply})
+            return jsonify({"reply": reply, "type": "general"})
+
+        # ── No data loaded — ask user to scrape first ─────────────────────
+        if not has_data and intent not in ("GREETING","GENERAL"):
+            reply = ("No reviews loaded yet. Please scrape an app first using the search bar, "
+                     "then I can analyze the data for you! 🎮")
+            memory.append({"role": "assistant", "content": reply})
+            return jsonify({"reply": reply, "type": "general"})
+
+        # ── FILTER intent ─────────────────────────────────────────────────
+        if intent == "FILTER":
+            params = extract_params(user_message, intent, llm, apps)
+            filter_payload: dict = {}
+            if params.get("min_stars"):
+                stars = list(range(
+                    int(params.get("min_stars",1)),
+                    int(params.get("max_stars",params.get("min_stars",1)))+1
+                ))
+                filter_payload["stars"] = stars
+            if params.get("app_filter"):
+                filter_payload["app"] = params["app_filter"]
+            if params.get("keyword"):
+                filter_payload["query"] = params["keyword"]
+
+            # Also show a summary table via TABLE tool
+            result = _tool_top_reviews(
+                df,
+                min_stars=int(params.get("min_stars") or 1),
+                max_stars=int(params.get("max_stars") or 5),
+                n=int(params.get("n") or 8),
+                app_filter=params.get("app_filter",""),
+            )
+            reply  = result.get("summary","Filters applied.")
+            table  = result.get("table")
+            memory.append({"role": "assistant", "content": reply})
+            return jsonify({
+                "reply":   reply,
+                "filters": filter_payload,
+                "table":   table,
+                "type":    "filter",
+            })
+
+        # ── COMPARISON intent ─────────────────────────────────────────────
+        if intent == "COMPARISON":
+            result = _tool_app_comparison(df)
+            if "error" in result:
+                reply = result["error"]
+                memory.append({"role": "assistant", "content": reply})
+                return jsonify({"reply": reply, "type": "general"})
+
+            # Also ask LLM to narrate
+            narration_prompt = (
+                f"Here is a comparison table of apps by rating:\n"
+                f"{json.dumps(result['table']['rows'], indent=2)}\n\n"
+                f"User asked: '{user_message}'\n"
+                f"Write a 2-3 sentence natural language summary highlighting "
+                f"the worst and best performing apps."
+            )
+            from langchain_core.messages import HumanMessage
+            narr_resp = llm.invoke([HumanMessage(content=narration_prompt)])
+            narration = getattr(narr_resp, "content", str(narr_resp)).strip()
+
+            memory.append({"role": "assistant", "content": narration})
+            return jsonify({
+                "reply": narration,
+                "table": result["table"],
+                "type":  "comparison",
+            })
+
+        # ── TABLE intent ──────────────────────────────────────────────────
+        if intent == "TABLE":
+            # Check what the PREVIOUS assistant message was about
+            # so "get me this in tabular format" works correctly
+            prev_context = ""
+            history = list(memory)
+            for turn in reversed(history[:-1]):   # skip current user msg
+                if turn["role"] == "assistant":
+                    prev_context = turn["content"]
+                    break
+
+            # If previous answer was about app comparison / ratings → show comparison table
+            comp_keywords = ["rating","low rating","negative","ranked","comparison","games"]
+            if any(k in prev_context.lower() for k in comp_keywords) or "tabular" in user_message.lower():
+                result = _tool_app_comparison(df)
+                if "table" in result:
+                    reply  = f"Here's the comparison table. {result['summary']}"
+                    memory.append({"role": "assistant", "content": reply})
+                    return jsonify({
+                        "reply": reply,
+                        "table": result["table"],
+                        "type":  "table",
+                    })
+
+            # Otherwise extract params and show filtered reviews table
+            params = extract_params(user_message, "TABLE", llm, apps)
+            result = _tool_top_reviews(
+                df,
+                min_stars=int(params.get("min_stars") or 1),
+                max_stars=int(params.get("max_stars") or 5),
+                n=int(params.get("n") or 10),
+                app_filter=params.get("app_filter",""),
+            )
+            reply  = result.get("summary","")
+            memory.append({"role": "assistant", "content": reply})
+            return jsonify({
+                "reply": reply,
+                "table": result.get("table"),
+                "type":  "table",
+            })
+
+        # ── KEYWORD intent ────────────────────────────────────────────────
+        if intent == "KEYWORD":
+            params = extract_params(user_message, intent, llm, apps)
+            kw = params.get("keyword","")
+            if not kw:
+                # Ask LLM to extract keyword from message
+                from langchain_core.messages import HumanMessage
+                kw_resp = llm.invoke([HumanMessage(content=(
+                    f'Extract the search keyword or phrase from: "{user_message}". '
+                    f'Return ONLY the keyword, nothing else.'
+                ))])
+                kw = getattr(kw_resp, "content", str(kw_resp)).strip().strip('"')
+
+            result = _tool_keyword_search(df, kw, n=10)
+            reply  = result.get("summary","")
+            memory.append({"role": "assistant", "content": reply})
+            return jsonify({
+                "reply": reply,
+                "table": result.get("table"),
+                "type":  "keyword",
+            })
+
+        # ── HELPFUL intent ────────────────────────────────────────────────
+        if intent == "HELPFUL":
+            params = extract_params(user_message, intent, llm, apps)
+            result = _tool_top_helpful(df, n=int(params.get("n") or 5))
+            if "error" in result:
+                reply = result["error"]
+            else:
+                reply = result.get("summary","")
+            memory.append({"role": "assistant", "content": reply})
+            return jsonify({
+                "reply": reply,
+                "table": result.get("table"),
+                "type":  "helpful",
+            })
+
+        # ── ANALYSIS intent (deep — calls LangGraph agent) ────────────────
+        #    Also used as fallback for everything not caught above
+        # Build conversation context string for agent
+        history_context = "\n".join(
+            f"{'User' if t['role']=='user' else 'Assistant'}: {t['content']}"
+            for t in list(memory)[-MAX_HISTORY_FOR_LLM:]
+        )
+        enriched_query = (
+            f"Conversation so far:\n{history_context}\n\n"
+            f"User's current question: {user_message}"
+        ) if len(memory) > 2 else user_message
+
+        # Run the full LangGraph agent
+        agent_state = run_agent(enriched_query, df=df if has_data else None)
+        report      = agent_state.get("report", {})
+        breakdown   = agent_state.get("app_breakdown", [])
+
+        # Format the reply text
+        reply = _format_agent_report(report)
+        if not reply.strip():
+            reply = report.get("executive_summary","I've completed the analysis.")
+
+        # Build optional table from app breakdown
+        table = _build_agent_table(report, breakdown)
+
+        memory.append({"role": "assistant", "content": reply})
+        return jsonify({
+            "reply":      reply,
+            "table":      table,
+            "agent_data": {
+                "top_problems":      report.get("top_problems",[]),
+                "key_strengths":     report.get("key_strengths",[]),
+                "recommendations":   report.get("recommendations",[]),
+                "clusters":          agent_state.get("clusters",[]),
+                "sentiment":         agent_state.get("sentiment",{}),
+                "stats":             agent_state.get("stats",{}),
+            },
+            "type": "analysis",
+        })
+
+    except Exception as e:
+        import traceback
+        print(f"[Chat ERROR] {e}\n{traceback.format_exc()}")
+        return jsonify({"error": str(e)}), 500
+
+
+# ═══════════════════════════════════════════════════════════════════════════
+#  SCRAPE ROUTES  (unchanged from v1)
+# ═══════════════════════════════════════════════════════════════════════════
 
 @app.route('/scrape', methods=['POST'])
 def scrape():
     try:
-        data = request.json
-        identifier = data.get('identifier', '').strip()
-        count_type = data.get('review_count_type', 'fixed')
-        count = 100000 if count_type == 'all' else data.get('review_count', 150)
-        
+        data        = request.json
+        identifier  = data.get('identifier', '').strip()
+        count_type  = data.get('review_count_type', 'fixed')
+        count       = 100000 if count_type == 'all' else data.get('review_count', 150)
+
         app_id = extract_app_id(identifier)
         if not app_id:
-            # Fallback 1: Library search
             results = search(identifier, lang="en", country="us", n_hits=1)
             if results and results[0].get('appId'):
                 app_id = results[0]['appId']
             else:
-                # Fallback 2: Direct HTML scrape (Rock-solid)
                 pids = scrape_store_ids(identifier, n_hits=1)
-                if pids: app_id = pids[0]
-                else: return jsonify({"error": f"App '{identifier}' not found"}), 404
+                if pids:
+                    app_id = pids[0]
+                else:
+                    return jsonify({"error": f"App '{identifier}' not found"}), 404
 
         info, all_reviews = fetch_app_reviews(
-            app_id, count, data.get('sort_order'), data.get('star_ratings')
-        )
+            app_id, count, data.get('sort_order'), data.get('star_ratings'))
 
         return jsonify({
             "app_info": {
-                "title": info['title'],
-                "icon": info['icon'],
-                "score": info['score'],
+                "title":   info['title'],
+                "icon":    info['icon'],
+                "score":   info['score'],
                 "reviews": info['reviews'],
-                "appId": app_id,
+                "appId":   app_id,
             },
             "reviews": all_reviews,
         })
-
     except Exception as e:
         return jsonify({"error": str(e)}), 500
 
@@ -148,116 +699,121 @@ def scrape():
 @app.route('/find-apps', methods=['POST'])
 def find_apps():
     try:
-        data = request.json
-        query = data.get('query', '').strip()
+        data      = request.json
+        query     = data.get('query', '').strip()
         app_count = int(data.get('app_count', 10))
-        
-        # Using the robust scraper to get IDs
-        app_ids = scrape_store_ids(query, n_hits=app_count)
-        
+        app_ids   = scrape_store_ids(query, n_hits=app_count)
         if not app_ids:
-            # Fallback to library
-            hits = search(query, lang="en", country="us", n_hits=app_count)
+            hits    = search(query, lang="en", country="us", n_hits=app_count)
             app_ids = [h['appId'] for h in hits if h.get('appId')]
-            
+
         results = []
         for aid in app_ids:
             try:
                 info = app_info(aid, lang='en', country='us')
                 results.append({
-                    "appId": aid,
-                    "title": info['title'],
-                    "icon": info['icon'],
-                    "score": info['score'],
-                    "developer": info.get('developer', 'Unknown'),
-                    "installs": info.get('installs', '0+')
+                    "appId":     aid,
+                    "title":     info['title'],
+                    "icon":      info['icon'],
+                    "score":     info['score'],
+                    "developer": info.get('developer','Unknown'),
+                    "installs":  info.get('installs','0+'),
                 })
-            except: continue
-                
+            except Exception:
+                continue
+
         return jsonify({"results": results})
     except Exception as e:
         return jsonify({"error": str(e)}), 500
 
+
 @app.route('/scrape-batch', methods=['POST'])
 def scrape_batch():
     try:
-        data = request.json
-        app_ids = data.get('app_ids', [])
-        count_type = data.get('review_count_type', 'fixed')
+        data            = request.json
+        app_ids         = data.get('app_ids', [])
+        count_type      = data.get('review_count_type', 'fixed')
         reviews_per_app = 100000 if count_type == 'all' else int(data.get('reviews_per_app', 100))
-        
+
         if not app_ids:
             return jsonify({"error": "No app IDs provided"}), 400
-            
-        batch_results = []
-        all_combined_reviews = []
-        
+
+        batch_results: list[dict] = []
+        all_combined:  list[dict] = []
+
         for app_id in app_ids:
             try:
                 info, app_reviews = fetch_app_reviews(
-                    app_id, reviews_per_app, data.get('sort_order'), data.get('star_ratings')
-                )
+                    app_id, reviews_per_app, data.get('sort_order'), data.get('star_ratings'))
                 batch_results.append({
                     "title": info['title'],
-                    "icon": info['icon'],
+                    "icon":  info['icon'],
                     "score": info['score'],
-                    "appId": app_id
+                    "appId": app_id,
                 })
-                all_combined_reviews.extend(app_reviews)
-            except:
+                all_combined.extend(app_reviews)
+            except Exception:
                 continue
-                
-        return jsonify({
-            "apps": batch_results,
-            "reviews": all_combined_reviews
-        })
+
+        return jsonify({"apps": batch_results, "reviews": all_combined})
     except Exception as e:
         return jsonify({"error": str(e)}), 500
 
 
-
 @app.route("/search-suggestions", methods=["POST"])
 def search_suggestions():
-    """Return top app matches for a keyword — used by the UI search dropdown."""
     try:
-        query = (request.json or {}).get("query", "").strip()
+        query = (request.json or {}).get("query","").strip()
         if not query or len(query) < 2:
             return jsonify({"results": []})
 
         hits = search(query, lang="en", country="us", n_hits=6)
         results = []
         for h in hits:
-            aid = h.get("appId", "")
+            aid = h.get("appId","")
             if not aid or aid == "None" or "." not in aid:
                 continue
             results.append({
                 "appId":     aid,
                 "storeUrl":  f"https://play.google.com/store/apps/details?id={aid}",
-                "title":     h.get("title", ""),
-                "icon":      h.get("icon", ""),
+                "title":     h.get("title",""),
+                "icon":      h.get("icon",""),
                 "score":     round(h.get("score") or 0, 1),
-                "developer": h.get("developer", ""),
-                "installs":  h.get("installs", ""),
+                "developer": h.get("developer",""),
+                "installs":  h.get("installs",""),
             })
-
         return jsonify({"results": results[:5]})
     except Exception as e:
         return jsonify({"error": str(e)}), 500
 
 
+# ═════════════════════════════════════════════════════════════════════════���═
+#  CLEAR CHAT MEMORY  (optional endpoint for "New Chat" button)
+# ═══════════════════════════════════════════════════════════════════════════
+
+@app.route('/chat/clear', methods=['POST'])
+def clear_chat():
+    session_id = (request.json or {}).get('session_id') or request.remote_addr or "default"
+    _CONV_MEMORY[session_id].clear()
+    return jsonify({"ok": True})
+
+
+# ═══════════════════════════════════════════════════════════════════════════
+#  PAGE ROUTES
+# ═══════════════════════════════════════════════════════════════════════════
+
 @app.route('/scraper')
 def scraper():
     return render_template('index.html')
 
-
 @app.route('/batch')
 def batch():
     return render_template('batch.html')
 
-
 @app.route('/')
 def landing():
     return render_template('landing.html')
 
+
 if __name__ == "__main__":
     app.run(host="0.0.0.0", debug=True, port=7860)

templates/batch.html

@@ -6,90 +6,78 @@
   <title>Batch Intelligence | PlayPulse</title>
   <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&display=swap" rel="stylesheet" />
   <style>
-    :root {
-      --bg: #0b0e14;
-      --surface: #151921;
-      --surface2: #1c2333;
-      --border: #232a35;
-      --accent: #3b82f6;
-      --accent-dim: rgba(59,130,246,0.12);
-      --green: #22c55e;
-      --green-dim: rgba(34,197,94,0.12);
-      --amber: #f59e0b;
-      --text: #f1f5f9;
-      --muted: #64748b;
-      --muted2: #94a3b8;
-    }
-    * { box-sizing: border-box; margin: 0; padding: 0; }
-    
-    /* Modern Scrollbar */
-    ::-webkit-scrollbar { width: 6px; height: 6px; }
-    ::-webkit-scrollbar-track { background: transparent; }
-    ::-webkit-scrollbar-thumb { background: rgba(255,255,255,0.1); border-radius: 10px; }
-    ::-webkit-scrollbar-thumb:hover { background: rgba(255,255,255,0.2); }
-    * { scrollbar-width: thin; scrollbar-color: rgba(255,255,255,0.1) transparent; }
-
-    body { font-family: 'Inter', sans-serif; background: var(--bg); color: var(--text); height: 100vh; overflow: hidden; display: flex; flex-direction: column; }
-
-    .btn-sm { background: var(--surface2); border: 1px solid var(--border); color: white; padding: 4px 10px; border-radius: 6px; font-size: 10px; cursor: pointer; transition: 0.2s; }
-    .btn-sm:hover { border-color: var(--accent); }
-
-    .header { height: 60px; background: var(--surface); border-bottom: 1px solid var(--border); display: flex; align-items: center; padding: 0 20px; gap: 20px; }
-    .main { flex: 1; display: flex; overflow: hidden; }
-    .sidebar { width: 300px; background: var(--surface); border-right: 1px solid var(--border); padding: 15px; display: flex; flex-direction: column; gap: 15px; overflow-y: auto; }
-    .content { flex: 1; background: var(--bg); position: relative; display: flex; flex-direction: column; }
-
-    .mode-toggle { display: grid; grid-template-columns: 1fr 1fr; background: var(--bg); padding: 4px; border-radius: 10px; border: 1px solid var(--border); margin-bottom: 5px; }
-    .mode-btn { padding: 8px; border-radius: 7px; text-align: center; cursor: pointer; font-size: 11px; font-weight: 700; color: var(--muted); transition: 0.2s; }
-    .mode-btn.active { background: var(--surface2); color: white; box-shadow: 0 2px 4px rgba(0,0,0,0.2); }
-
-    .logo { font-weight: 800; font-size: 18px; color: var(--accent); display: flex; align-items: center; gap: 8px; text-decoration: none; }
-    .input-group { display: flex; flex-direction: column; gap: 6px; }
-    .label { font-size: 10px; font-weight: 700; text-transform: uppercase; color: var(--muted); letter-spacing: 0.5px; }
-    input, select { background: var(--bg); border: 1px solid var(--border); color: white; padding: 10px; border-radius: 8px; font-size: 12px; outline: none; width: 100%; }
-    input:focus { border-color: var(--accent); }
-
-    .btn-main { background: var(--accent); color: white; border: none; padding: 14px; border-radius: 10px; font-weight: 800; cursor: pointer; display: flex; align-items: center; justify-content: center; gap: 8px; transition: 0.2s; width: 100%; border-bottom: 3px solid rgba(0,0,0,0.2); }
-    .btn-main:hover { transform: translateY(-1px); box-shadow: 0 4px 12px rgba(59,130,246,0.3); }
-    .btn-main:disabled { opacity: 0.5; cursor: not-allowed; }
-
-    .scroll-view { flex: 1; overflow-y: auto; padding: 20px; display: flex; flex-direction: column; gap: 15px; }
-
-    /* Results Header */
-    .batch-summary { background: var(--surface); border: 1px solid var(--border); border-radius: 16px; padding: 20px; display: flex; flex-direction: column; gap: 15px; }
-    .apps-grid { display: grid; grid-template-columns: repeat(auto-fill, minmax(200px, 1fr)); gap: 12px; }
-    .app-mini-card { background: var(--surface2); border: 1px solid var(--border); border-radius: 10px; padding: 10px; display: flex; align-items: center; gap: 10px; }
-    .app-mini-card img { width: 32px; height: 32px; border-radius: 6px; }
-    .app-mini-info { flex: 1; min-width: 0; }
-    .app-mini-title { font-size: 12px; font-weight: 700; white-space: nowrap; overflow: hidden; text-overflow: ellipsis; }
-    .app-mini-score { font-size: 10px; color: var(--amber); }
-
-    /* Table Styles */
-    .table-container { background: var(--surface); border: 1px solid var(--border); border-radius: 16px; overflow: hidden; }
-    table { width: 100%; border-collapse: collapse; font-size: 13px; }
-    th { text-align: left; background: var(--surface2); padding: 12px 16px; color: var(--muted2); font-weight: 700; font-size: 11px; text-transform: uppercase; border-bottom: 1px solid var(--border); }
-    td { padding: 14px 16px; border-bottom: 1px solid var(--border); vertical-align: top; }
-    tr:last-child td { border-bottom: none; }
-    tr:hover td { background: rgba(255,255,255,0.02); }
-
-    .app-tag { display: inline-flex; align-items: center; gap: 6px; background: var(--accent-dim); color: var(--accent); padding: 4px 8px; border-radius: 6px; font-weight: 700; font-size: 10px; margin-bottom: 6px; border: 1px solid rgba(59,130,246,0.2); }
-    .score-stars { color: var(--amber); white-space: nowrap; }
-    .review-content { color: #cbd5e1; line-height: 1.5; max-width: 400px; }
-
-    /* Overlays */
-    .star-filter-grid { display: flex; flex-direction: column; gap: 6px; }
-    .star-row { display: flex; align-items: center; gap: 10px; padding: 8px 10px; border-radius: 8px; border: 1px solid var(--border); background: var(--bg); cursor: pointer; transition: border-color 0.15s; user-select: none; }
-    .star-row:hover { border-color: var(--accent); }
-    .star-row input[type="checkbox"] { width: 15px; height: 15px; accent-color: var(--accent); cursor: pointer; padding: 0; border: none; background: transparent; flex-shrink: 0; }
-    .star-label { display: flex; align-items: center; gap: 5px; font-size: 13px; font-weight: 600; flex: 1; }
-    .stars-on { color: var(--amber); letter-spacing: -1px; }
-    .stars-off { color: var(--border); letter-spacing: -1px; }
-
-    .loader-overlay { position: absolute; inset: 0; background: var(--bg); display: flex; flex-direction: column; align-items: center; justify-content: center; gap: 15px; z-index: 10; }
-    .spinner { width: 40px; height: 40px; border: 4px solid var(--border); border-top-color: var(--accent); border-radius: 50%; animation: spin .8s linear infinite; }
-    @keyframes spin { to { transform: rotate(360deg); } }
-
-    .hidden { display: none !important; }
+    :root { --bg:#0b0e14; --surface:#151921; --surface2:#1c2333; --border:#232a35; --accent:#3b82f6; --accent-dim:rgba(59,130,246,0.12); --green:#22c55e; --green-dim:rgba(34,197,94,0.12); --amber:#f59e0b; --text:#f1f5f9; --muted:#64748b; --muted2:#94a3b8; }
+    * { box-sizing:border-box; margin:0; padding:0; }
+    ::-webkit-scrollbar{width:6px;height:6px;} ::-webkit-scrollbar-track{background:transparent;} ::-webkit-scrollbar-thumb{background:rgba(255,255,255,0.1);border-radius:10px;} ::-webkit-scrollbar-thumb:hover{background:rgba(255,255,255,0.2);} *{scrollbar-width:thin;scrollbar-color:rgba(255,255,255,0.1) transparent;}
+    body{font-family:'Inter',sans-serif;background:var(--bg);color:var(--text);height:100vh;overflow:hidden;display:flex;flex-direction:column;}
+    .btn-sm{background:var(--surface2);border:1px solid var(--border);color:white;padding:4px 10px;border-radius:6px;font-size:10px;cursor:pointer;transition:0.2s;} .btn-sm:hover{border-color:var(--accent);}
+    .header{height:60px;background:var(--surface);border-bottom:1px solid var(--border);display:flex;align-items:center;padding:0 20px;gap:20px;}
+    .main{flex:1;display:flex;overflow:hidden;}
+    .sidebar{width:300px;background:var(--surface);border-right:1px solid var(--border);padding:15px;display:flex;flex-direction:column;gap:15px;overflow-y:auto;}
+    .content{flex:1;background:var(--bg);position:relative;display:flex;flex-direction:column;}
+    .mode-toggle{display:grid;grid-template-columns:1fr 1fr;background:var(--bg);padding:4px;border-radius:10px;border:1px solid var(--border);margin-bottom:5px;}
+    .mode-btn{padding:8px;border-radius:7px;text-align:center;cursor:pointer;font-size:11px;font-weight:700;color:var(--muted);transition:0.2s;} .mode-btn.active{background:var(--surface2);color:white;box-shadow:0 2px 4px rgba(0,0,0,0.2);}
+    .logo{font-weight:800;font-size:18px;color:var(--accent);display:flex;align-items:center;gap:8px;text-decoration:none;}
+    .input-group{display:flex;flex-direction:column;gap:6px;}
+    .label{font-size:10px;font-weight:700;text-transform:uppercase;color:var(--muted);letter-spacing:0.5px;}
+    input,select{background:var(--bg);border:1px solid var(--border);color:white;padding:10px;border-radius:8px;font-size:12px;outline:none;width:100%;} input:focus{border-color:var(--accent);}
+    .btn-main{background:var(--accent);color:white;border:none;padding:14px;border-radius:10px;font-weight:800;cursor:pointer;display:flex;align-items:center;justify-content:center;gap:8px;transition:0.2s;width:100%;border-bottom:3px solid rgba(0,0,0,0.2);} .btn-main:hover{transform:translateY(-1px);box-shadow:0 4px 12px rgba(59,130,246,0.3);} .btn-main:disabled{opacity:0.5;cursor:not-allowed;}
+    .scroll-view{flex:1;overflow-y:auto;padding:30px;display:flex;flex-direction:column;gap:25px;}
+    .batch-summary{background:var(--surface);border:1px solid var(--border);border-radius:16px;padding:20px;display:flex;flex-direction:column;gap:15px;}
+    .apps-grid{display:grid;grid-template-columns:repeat(auto-fill,minmax(200px,1fr));gap:12px;}
+    .app-mini-card{background:var(--surface2);border:1px solid var(--border);border-radius:10px;padding:10px;display:flex;align-items:center;gap:10px;}
+    .app-mini-card img{width:32px;height:32px;border-radius:6px;}
+    .app-mini-info{flex:1;min-width:0;} .app-mini-title{font-size:12px;font-weight:700;white-space:nowrap;overflow:hidden;text-overflow:ellipsis;} .app-mini-score{font-size:10px;color:var(--amber);}
+    .table-container{background:var(--surface);border:1px solid var(--border);border-radius:16px;overflow:hidden;}
+    table{width:100%;border-collapse:collapse;font-size:13px;}
+    th{text-align:left;background:var(--surface2);padding:12px 16px;color:var(--muted2);font-weight:700;font-size:11px;text-transform:uppercase;border-bottom:1px solid var(--border);}
+    td{padding:14px 16px;border-bottom:1px solid var(--border);vertical-align:top;} tr:last-child td{border-bottom:none;} tr:hover td{background:rgba(255,255,255,0.02);}
+    .app-tag{display:inline-flex;align-items:center;gap:6px;background:var(--accent-dim);color:var(--accent);padding:4px 8px;border-radius:6px;font-weight:700;font-size:10px;margin-bottom:6px;border:1px solid rgba(59,130,246,0.2);}
+    .score-stars{color:var(--amber);white-space:nowrap;}
+    .review-content{color:#cbd5e1;line-height:1.5;max-width:500px;word-wrap:break-word;}
+    .dev-reply{margin-top:8px;padding:8px 12px;background:rgba(59,130,246,0.05);border-left:2px solid var(--accent);border-radius:0 6px 6px 0;font-size:11px;color:var(--muted2);}
+    .dev-reply-label{font-weight:700;color:var(--accent);font-size:9px;text-transform:uppercase;margin-bottom:3px;display:block;}
+    .helpful-pill{display:inline-flex;align-items:center;gap:4px;background:var(--surface2);padding:4px 8px;border-radius:12px;font-size:10px;color:var(--muted2);border:1px solid var(--border);}
+    .helpful-pill svg{width:10px;height:10px;color:var(--accent);}
+    .star-filter-grid{display:flex;flex-direction:column;gap:6px;}
+    .star-row{display:flex;align-items:center;gap:10px;padding:8px 10px;border-radius:8px;border:1px solid var(--border);background:var(--bg);cursor:pointer;transition:border-color 0.15s;user-select:none;} .star-row:hover{border-color:var(--accent);}
+    .star-row input[type="checkbox"]{width:15px;height:15px;accent-color:var(--accent);cursor:pointer;padding:0;border:none;background:transparent;flex-shrink:0;}
+    .star-label{display:flex;align-items:center;gap:5px;font-size:13px;font-weight:600;flex:1;} .stars-on{color:var(--amber);letter-spacing:-1px;} .stars-off{color:var(--border);letter-spacing:-1px;}
+    .loader-overlay{position:absolute;inset:0;background:var(--bg);display:flex;flex-direction:column;align-items:center;justify-content:center;gap:15px;z-index:10;}
+    .spinner{width:40px;height:40px;border:4px solid var(--border);border-top-color:var(--accent);border-radius:50%;animation:spin .8s linear infinite;}
+    @keyframes spin{to{transform:rotate(360deg);}}
+    .hidden{display:none!important;}
+
+    /* ── Chat styles ── */
+    #chat-dialer{position:fixed;bottom:24px;right:24px;width:56px;height:56px;background:var(--accent);border-radius:50%;display:flex;align-items:center;justify-content:center;box-shadow:0 8px 32px rgba(59,130,246,0.4);cursor:pointer;z-index:1000;transition:0.3s cubic-bezier(0.175,0.885,0.32,1.275);border:2px solid rgba(255,255,255,0.1);}
+    #chat-dialer:hover{transform:scale(1.1) rotate(5deg);box-shadow:0 12px 40px rgba(59,130,246,0.6);}
+    #chat-dialer svg{width:24px;height:24px;color:white;fill:none;stroke:currentColor;stroke-width:2.5;}
+    #chat-window{position:fixed;bottom:90px;right:24px;width:420px;height:600px;background:var(--surface);border:1px solid var(--border);border-radius:20px;display:flex;flex-direction:column;box-shadow:0 20px 50px rgba(0,0,0,0.5);z-index:1001;overflow:hidden;transform:translateY(20px) scale(0.95);opacity:0;pointer-events:none;transition:0.3s cubic-bezier(0.4,0,0.2,1);backdrop-filter:blur(20px);}
+    #chat-window.open{transform:translateY(0) scale(1);opacity:1;pointer-events:auto;}
+    .chat-header{padding:14px 18px;background:var(--accent);color:white;display:flex;align-items:center;gap:12px;flex-shrink:0;}
+    .chat-header-info{flex:1;} .chat-header-title{font-weight:800;font-size:15px;} .chat-header-status{font-size:10px;opacity:0.8;display:flex;align-items:center;gap:4px;} .status-dot{width:6px;height:6px;background:#22c55e;border-radius:50%;}
+    .chat-header-actions{display:flex;gap:8px;align-items:center;}
+    .chat-clear-btn{background:rgba(255,255,255,0.15);border:none;color:white;font-size:11px;padding:4px 10px;border-radius:8px;cursor:pointer;transition:0.2s;} .chat-clear-btn:hover{background:rgba(255,255,255,0.25);}
+    .chat-messages{flex:1;overflow-y:auto;padding:16px;display:flex;flex-direction:column;gap:12px;background-image:radial-gradient(var(--border) 1px,transparent 1px);background-size:20px 20px;}
+    .msg-row{display:flex;flex-direction:column;gap:4px;} .msg-row.user{align-items:flex-end;} .msg-row.bot{align-items:flex-start;}
+    .message{max-width:88%;padding:11px 15px;border-radius:16px;font-size:13px;line-height:1.6;}
+    .message.user{background:var(--accent);color:white;border-bottom-right-radius:4px;}
+    .message.bot{background:var(--surface2);color:var(--text);border:1px solid var(--border);border-bottom-left-radius:4px;white-space:pre-wrap;word-break:break-word;}
+    .msg-section{margin-top:10px;font-weight:700;font-size:11px;color:var(--accent);letter-spacing:0.05em;text-transform:uppercase;}
+    .msg-item{display:flex;gap:8px;margin-top:5px;} .msg-item-num{font-weight:700;color:var(--accent);min-width:16px;} .msg-bullet{color:var(--accent);min-width:14px;}
+    .chat-table-wrap{max-width:100%;overflow-x:auto;border:1px solid var(--border);border-radius:12px;background:var(--surface2);margin-top:4px;}
+    .chat-table-title{padding:8px 12px;font-size:11px;font-weight:700;color:var(--accent);border-bottom:1px solid var(--border);letter-spacing:0.05em;text-transform:uppercase;}
+    .chat-table{width:100%;border-collapse:collapse;font-size:12px;}
+    .chat-table th{padding:7px 12px;text-align:left;font-weight:700;font-size:11px;color:var(--muted2);background:var(--bg);border-bottom:1px solid var(--border);white-space:nowrap;}
+    .chat-table td{padding:7px 12px;border-bottom:1px solid var(--border);color:var(--text);max-width:180px;overflow:hidden;text-overflow:ellipsis;white-space:nowrap;} .chat-table tr:last-child td{border-bottom:none;} .chat-table tr:hover td{background:var(--surface);}
+    .typing-indicator{display:flex;gap:4px;padding:12px 16px;background:var(--surface2);border:1px solid var(--border);border-radius:16px;width:fit-content;}
+    .dot{width:6px;height:6px;background:var(--muted);border-radius:50%;animation:bounce 1.4s infinite;} .dot:nth-child(2){animation-delay:0.2s;} .dot:nth-child(3){animation-delay:0.4s;}
+    @keyframes bounce{0%,80%,100%{transform:translateY(0)}40%{transform:translateY(-6px)}}
+    .chat-input-area{padding:14px 16px;background:var(--surface);border-top:1px solid var(--border);display:flex;gap:10px;flex-shrink:0;}
+    #chat-input{flex:1;background:var(--bg);border:1px solid var(--border);color:white;padding:10px 14px;border-radius:12px;font-size:13px;outline:none;} #chat-input:focus{border-color:var(--accent);}
+    .btn-send{width:40px;height:40px;background:var(--accent);color:white;border:none;border-radius:10px;display:flex;align-items:center;justify-content:center;cursor:pointer;transition:0.2s;flex-shrink:0;} .btn-send:hover{transform:scale(1.05);} .btn-send svg{width:18px;height:18px;fill:none;stroke:currentColor;stroke-width:2.5;}
+    .chat-suggestions{display:flex;flex-wrap:wrap;gap:6px;padding:0 16px 10px;}
+    .sug-chip{font-size:11px;padding:5px 10px;border-radius:20px;background:var(--surface2);border:1px solid var(--border);color:var(--muted2);cursor:pointer;transition:0.2s;} .sug-chip:hover{border-color:var(--accent);color:var(--accent);}
   </style>
 </head>
 <body>
@@ -99,13 +87,13 @@
     <svg width="22" height="22" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="3" stroke-linecap="round" stroke-linejoin="round"><path d="M12 2v20M17 5H9.5a3.5 3.5 0 0 0 0 7h5a3.5 3.5 0 0 1 0 7H6"/></svg>
     BATCH INTEL
   </a>
-  <nav style="margin-left: 30px; display: flex; gap: 20px;">
-    <a href="/" style="color: var(--muted2); text-decoration: none; font-size: 13px; font-weight: 600; transition: 0.2s;" onmouseover="this.style.color='var(--text)'" onmouseout="this.style.color='var(--muted2)'">Home</a>
-    <a href="/scraper" style="color: var(--muted2); text-decoration: none; font-size: 13px; font-weight: 600; transition: 0.2s;" onmouseover="this.style.color='var(--text)'" onmouseout="this.style.color='var(--muted2)'">Single Explorer</a>
-    <a href="/batch" style="color: var(--text); text-decoration: none; font-size: 13px; font-weight: 700; border-bottom: 2px solid var(--accent); padding-bottom: 4px;">Batch Intelligence</a>
+  <nav style="margin-left:30px;display:flex;gap:20px;">
+    <a href="/" style="color:var(--muted2);text-decoration:none;font-size:13px;font-weight:600;" onmouseover="this.style.color='var(--text)'" onmouseout="this.style.color='var(--muted2)'">Home</a>
+    <a href="/scraper" style="color:var(--muted2);text-decoration:none;font-size:13px;font-weight:600;" onmouseover="this.style.color='var(--text)'" onmouseout="this.style.color='var(--muted2)'">Single Explorer</a>
+    <a href="/batch" style="color:var(--text);text-decoration:none;font-size:13px;font-weight:700;border-bottom:2px solid var(--accent);padding-bottom:4px;">Batch Intelligence</a>
   </nav>
   <div style="flex:1"></div>
-  <button class="btn-icon" onclick="downloadCSV()" style="background:var(--surface2); border:1px solid var(--border); color:white; padding:8px 16px; border-radius:8px; cursor:pointer; font-size:12px; font-weight:700;">Export Combined CSV</button>
+  <button onclick="downloadCSV()" style="background:var(--surface2);border:1px solid var(--border);color:white;padding:8px 16px;border-radius:8px;cursor:pointer;font-size:12px;font-weight:700;">Export Combined CSV</button>
 </div>
 
 <div class="main">
@@ -114,55 +102,37 @@
       <div class="label">Step 1: Discover Apps</div>
       <div style="display:flex;gap:8px;">
         <input type="text" id="query" placeholder="e.g. Multiplayer Games..." value="Multiplayer Games" style="flex:1">
-        <button onclick="findApps()" id="btnFind" style="background:var(--accent); border:none; color:white; padding:0 15px; border-radius:8px; cursor:pointer; font-weight:700;">Find</button>
+        <button onclick="findApps()" id="btnFind" style="background:var(--accent);border:none;color:white;padding:0 15px;border-radius:8px;cursor:pointer;font-weight:700;">Find</button>
       </div>
     </div>
-
     <div class="input-group">
       <div class="label">Discovery Limit</div>
       <input type="number" id="app_count" value="10" min="1" max="50">
-      <div style="font-size:10px; color:var(--muted); margin-top:4px;">How many apps to search for initially.</div>
     </div>
-
-    <div id="selectionArea" class="hidden" style="background:var(--surface2); border:1px solid var(--border); border-radius:12px; padding:10px; display:flex; flex-direction:column; gap:8px;">
-      <div class="label" style="display:flex; justify-content:space-between; align-items:center;">
-        <span>Select Apps</span>
-        <span id="selectedCount" style="color:var(--accent); font-size:9px;">0 selected</span>
-      </div>
-      <div id="appList" style="max-height:160px; overflow-y:auto; overflow-x:hidden; display:flex; flex-direction:column; gap:4px; padding-right:4px;">
-        <!-- Compact apps list -->
-      </div>
-      <div style="display:flex; gap:5px;">
-        <button onclick="toggleAllApps(true)" class="btn-sm" style="flex:1">All</button>
-        <button onclick="toggleAllApps(false)" class="btn-sm" style="flex:1">None</button>
-      </div>
+    <div id="selectionArea" class="hidden" style="background:var(--surface2);border:1px solid var(--border);border-radius:12px;padding:10px;display:flex;flex-direction:column;gap:8px;">
+      <div class="label" style="display:flex;justify-content:space-between;align-items:center;"><span>Select Apps</span><span id="selectedCount" style="color:var(--accent);font-size:9px;">0 selected</span></div>
+      <div id="appList" style="max-height:160px;overflow-y:auto;overflow-x:hidden;display:flex;flex-direction:column;gap:4px;padding-right:4px;"></div>
+      <div style="display:flex;gap:5px;"><button onclick="toggleAllApps(true)" class="btn-sm" style="flex:1">All</button><button onclick="toggleAllApps(false)" class="btn-sm" style="flex:1">None</button></div>
     </div>
-
     <div class="input-group">
       <div class="label">Step 2: Scrape Settings</div>
-      <div class="label" style="font-size:10px; margin-top:10px;">Reviews Per App</div>
+      <div class="label" style="font-size:10px;margin-top:10px;">Reviews Per App</div>
       <div class="mode-toggle">
         <div class="mode-btn active" id="btn-fixed" onclick="setMode('fixed')">Custom</div>
         <div class="mode-btn" id="btn-all" onclick="setMode('all')">Fetch All</div>
       </div>
       <input type="number" id="reviews_per_app" value="50" min="10" step="10">
     </div>
-
     <div class="input-group">
       <div class="label">Sort Method</div>
-      <select id="sort">
-        <option value="MOST_RELEVANT">Most Relevant</option>
-        <option value="NEWEST">Newest</option>
-        <option value="RATING">Top Ratings</option>
-      </select>
+      <select id="sort"><option value="MOST_RELEVANT">Most Relevant</option><option value="NEWEST">Newest</option><option value="RATING">Top Ratings</option></select>
     </div>
-
     <div class="input-group">
-      <div class="label">
+      <div class="label" style="display:flex;justify-content:space-between;align-items:center;">
         <span>Star Rating Filter</span>
-        <div style="display:flex;gap:5px">
-          <button class="quick-btn" style="font-size:9px; padding:2px 5px; cursor:pointer; background:var(--surface2); border:1px solid var(--border); color:white; border-radius:4px;" onclick="selectAllStars(true)">All</button>
-          <button class="quick-btn" style="font-size:9px; padding:2px 5px; cursor:pointer; background:var(--surface2); border:1px solid var(--border); color:white; border-radius:4px;" onclick="selectAllStars(false)">None</button>
+        <div style="display:flex;gap:5px;">
+          <button class="quick-btn" style="font-size:9px;padding:2px 5px;cursor:pointer;background:var(--surface2);border:1px solid var(--border);color:white;border-radius:4px;" onclick="selectAllStars(true)">All</button>
+          <button class="quick-btn" style="font-size:9px;padding:2px 5px;cursor:pointer;background:var(--surface2);border:1px solid var(--border);color:white;border-radius:4px;" onclick="selectAllStars(false)">None</button>
         </div>
       </div>
       <div class="star-filter-grid">
@@ -173,16 +143,11 @@
         <label class="star-row"><input type="checkbox" class="star-cb" value="1" checked><span class="star-label"><span class="stars-on">★</span><span class="stars-off">★★★★</span></span></label>
       </div>
     </div>
-
     <button class="btn-main" id="go" onclick="runBatch()">
       <svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="3"><rect x="3" y="3" width="7" height="7"/><rect x="14" y="3" width="7" height="7"/><rect x="14" y="14" width="7" height="7"/><rect x="3" y="14" width="7" height="7"/></svg>
       RUN BATCH ANALYSIS
     </button>
-    
-    <div style="background:var(--bg); padding:15px; border-radius:12px; border:1px solid var(--border); font-size:11px; color:var(--muted); line-height:1.6;">
-      <strong style="color:var(--text)">About Batch Mode</strong><br>
-      This will search for apps matching your query, scrape reviews for each, and combine them into a single comparison set.
-    </div>
+    <div style="background:var(--bg);padding:15px;border-radius:12px;border:1px solid var(--border);font-size:11px;color:var(--muted);line-height:1.6;"><strong style="color:var(--text)">About Batch Mode</strong><br>Search for apps, scrape reviews for each, and compare them side-by-side with AI chat support.</div>
   </aside>
 
   <div class="content">
@@ -191,21 +156,24 @@
         <svg width="48" height="48" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.5"><rect x="3" y="3" width="7" height="7"/><rect x="14" y="3" width="7" height="7"/><rect x="14" y="14" width="7" height="7"/><rect x="3" y="14" width="7" height="7"/></svg>
         <p>Run batch analysis to compare app data</p>
       </div>
-      
       <div id="results" class="hidden">
         <div class="batch-summary">
           <div class="label">Comparing These Apps:</div>
           <div class="apps-grid" id="appsGrid"></div>
         </div>
-
+        <div style="display:flex;align-items:center;justify-content:space-between;margin-bottom:10px;">
+          <div class="label">Reviews Comparison</div>
+          <div style="font-size:11px;color:var(--muted);" id="resultStats"></div>
+        </div>
         <div class="table-container">
           <table id="reviewsTable">
             <thead>
               <tr>
-                <th style="width:160px">Application</th>
-                <th style="width:80px">Score</th>
-                <th>Review Snippet</th>
-                <th style="width:120px">Date</th>
+                <th style="width:180px">Application / User</th>
+                <th style="width:90px">Score</th>
+                <th>Feedback & Developer Response</th>
+                <th style="width:100px">Helpful</th>
+                <th style="width:110px">Date</th>
               </tr>
             </thead>
             <tbody id="reviewsBody"></tbody>
@@ -213,7 +181,6 @@
         </div>
       </div>
     </div>
-
     <div id="loader" class="loader-overlay hidden">
       <div class="spinner"></div>
       <p style="color:var(--muted);font-size:14px" id="loaderMsg">Searching for apps...</p>
@@ -221,176 +188,196 @@
   </div>
 </div>
 
-<script>
-  let currentData = null;
-  let currentMode = 'fixed';
-
-  function setMode(m) {
-    currentMode = m;
-    document.querySelectorAll('.mode-btn').forEach(b => b.classList.remove('active'));
-    document.getElementById('btn-' + m).classList.add('active');
-    document.getElementById('reviews_per_app').classList.toggle('hidden', m === 'all');
-  }
-
-  function selectAllStars(check) {
-    document.querySelectorAll('.star-cb').forEach(cb => cb.checked = check);
-  }
-
-  let foundApps = [];
-
-  async function findApps() {
-    const q = document.getElementById('query').value.trim();
-    if (!q) return;
-
-    const btn = document.getElementById('btnFind');
-    btn.disabled = true;
-    btn.innerText = 'Searching...';
+<!-- Chat bubble -->
+<div id="chat-dialer" onclick="toggleChat()">
+  <svg viewBox="0 0 24 24"><path d="M21 15a2 2 0 0 1-2 2H7l-4 4V5a2 2 0 0 1 2-2h14a2 2 0 0 1 2 2z"/></svg>
+</div>
 
-    try {
-      const res = await fetch('/find-apps', {
-        method: 'POST',
-        headers: { 'Content-Type': 'application/json' },
-        body: JSON.stringify({ query: q, app_count: document.getElementById('app_count').value })
-      });
-      const data = await res.json();
-      if (!res.ok) throw new Error(data.error || 'Discovery failed');
+<div id="chat-window">
+  <div class="chat-header">
+    <svg width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="white" stroke-width="2.5"><circle cx="12" cy="12" r="3"/><path d="M12 1v4M12 19v4M4.22 4.22l2.83 2.83M16.95 16.95l2.83 2.83M1 12h4M19 12h4M4.22 19.78l2.83-2.83M16.95 7.05l2.83-2.83"/></svg>
+    <div class="chat-header-info">
+      <div class="chat-header-title">PlayPulse Intelligence</div>
+      <div class="chat-header-status"><span class="status-dot"></span> Agent Online</div>
+    </div>
+    <div class="chat-header-actions">
+      <button class="chat-clear-btn" onclick="clearChat()">Clear</button>
+      <div style="cursor:pointer;opacity:0.7;" onclick="toggleChat()">
+        <svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="white" stroke-width="2.5"><line x1="18" y1="6" x2="6" y2="18"/><line x1="6" y1="6" x2="18" y2="18"/></svg>
+      </div>
+    </div>
+  </div>
+  <div class="chat-messages" id="chat-messages">
+    <div class="msg-row bot">
+      <div class="message bot">👋 Hi! I'm PlayPulse Intelligence. Run a batch scrape, then ask me to compare apps, find issues, show tables, or analyze sentiment!</div>
+    </div>
+  </div>
+  <div class="chat-suggestions" id="chat-sug">
+    <div class="sug-chip" onclick="fillChat('Compare all apps by rating')">Compare apps</div>
+    <div class="sug-chip" onclick="fillChat('Which app has the most complaints?')">Most complaints</div>
+    <div class="sug-chip" onclick="fillChat('Show 1 star reviews in table')">1★ table</div>
+    <div class="sug-chip" onclick="fillChat('What are the common issues?')">Common issues</div>
+  </div>
+  <div class="chat-input-area">
+    <input type="text" id="chat-input" placeholder="Ask about the batch analysis…" onkeydown="if(event.key==='Enter') sendChatMessage()">
+    <button class="btn-send" onclick="sendChatMessage()">
+      <svg viewBox="0 0 24 24"><line x1="22" y1="2" x2="11" y2="13"/><polygon points="22 2 15 22 11 13 2 9 22 2"/></svg>
+    </button>
+  </div>
+</div>
 
-      foundApps = data.results;
-      renderAppSelection();
-    } catch(e) {
-      alert(e.message);
-    } finally {
-      btn.disabled = false;
-      btn.innerText = 'Find';
-    }
+<script>
+  let currentData=null;
+  let currentMode='fixed';
+
+  function setMode(m){currentMode=m;document.querySelectorAll('.mode-btn').forEach(b=>b.classList.remove('active'));document.getElementById('btn-'+m).classList.add('active');document.getElementById('reviews_per_app').classList.toggle('hidden',m==='all');}
+  function selectAllStars(check){document.querySelectorAll('.star-cb').forEach(cb=>cb.checked=check);}
+
+  let foundApps=[];
+  async function findApps(){
+    const q=document.getElementById('query').value.trim();if(!q)return;
+    const btn=document.getElementById('btnFind');btn.disabled=true;btn.innerText='Searching...';
+    try{
+      const res=await fetch('/find-apps',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({query:q,app_count:document.getElementById('app_count').value})});
+      const data=await res.json();if(!res.ok)throw new Error(data.error||'Discovery failed');
+      foundApps=data.results;renderAppSelection();
+    }catch(e){alert(e.message);}finally{btn.disabled=false;btn.innerText='Find';}
   }
 
-  function renderAppSelection() {
-    const list = document.getElementById('appList');
+  function renderAppSelection(){
+    const list=document.getElementById('appList');
     document.getElementById('selectionArea').classList.remove('hidden');
-    
-    list.innerHTML = foundApps.map(a => `
-      <label style="display:flex; align-items:center; gap:8px; padding:6px; background:var(--bg); border-radius:6px; border:1px solid var(--border); cursor:pointer; min-width:0;">
-        <input type="checkbox" class="app-cb" value="${a.appId}" onchange="updateSelectionCount()" checked style="width:14px; height:14px; margin:0;">
-        <img src="${a.icon}" style="width:20px; height:20px; border-radius:4px; flex-shrink:0;">
-        <div style="flex:1; min-width:0;">
-          <div style="font-size:10px; font-weight:700; white-space:nowrap; overflow:hidden; text-overflow:ellipsis; color:var(--text);">${a.title}</div>
-          <div style="font-size:9px; color:var(--muted); white-space:nowrap; overflow:hidden; text-overflow:ellipsis;">${a.developer}</div>
-        </div>
-      </label>
-    `).join('');
-    updateSelectionCount();
-  }
-
-  function toggleAllApps(check) {
-    document.querySelectorAll('.app-cb').forEach(cb => cb.checked = check);
+    list.innerHTML=foundApps.map(a=>`<label style="display:flex;align-items:center;gap:8px;padding:6px;background:var(--bg);border-radius:6px;border:1px solid var(--border);cursor:pointer;min-width:0;"><input type="checkbox" class="app-cb" value="${a.appId}" onchange="updateSelectionCount()" checked style="width:14px;height:14px;margin:0;"><img src="${a.icon}" style="width:20px;height:20px;border-radius:4px;flex-shrink:0;"><div style="flex:1;min-width:0;"><div style="font-size:10px;font-weight:700;white-space:nowrap;overflow:hidden;text-overflow:ellipsis;color:var(--text);">${a.title}</div><div style="font-size:9px;color:var(--muted);white-space:nowrap;overflow:hidden;text-overflow:ellipsis;">${a.developer}</div></div></label>`).join('');
     updateSelectionCount();
   }
 
-  function updateSelectionCount() {
-    const count = document.querySelectorAll('.app-cb:checked').length;
-    document.getElementById('selectedCount').innerText = `${count} selected`;
-    document.getElementById('go').disabled = count === 0;
+  function toggleAllApps(check){document.querySelectorAll('.app-cb').forEach(cb=>cb.checked=check);updateSelectionCount();}
+  function updateSelectionCount(){const count=document.querySelectorAll('.app-cb:checked').length;document.getElementById('selectedCount').innerText=`${count} selected`;document.getElementById('go').disabled=count===0;}
+
+  async function runBatch(){
+    const selectedAppIds=[...document.querySelectorAll('.app-cb:checked')].map(cb=>cb.value);
+    if(!selectedAppIds.length)return alert('Select at least one app');
+    const stars=[...document.querySelectorAll('.star-cb:checked')].map(cb=>parseInt(cb.value));
+    if(!stars.length)return alert('Select at least one star rating');
+    document.getElementById('welcome').classList.add('hidden');document.getElementById('results').classList.add('hidden');
+    document.getElementById('loader').classList.remove('hidden');document.getElementById('go').disabled=true;
+    try{
+      const res=await fetch('/scrape-batch',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({app_ids:selectedAppIds,review_count_type:currentMode,reviews_per_app:document.getElementById('reviews_per_app').value,sort_order:document.getElementById('sort').value,star_ratings:stars.length===5?'all':stars})});
+      const data=await res.json();if(!res.ok)throw new Error(data.error||'Batch failed');
+      currentData=data;render(data);
+    }catch(e){alert(e.message);}finally{document.getElementById('loader').classList.add('hidden');document.getElementById('go').disabled=false;}
   }
 
-  async function runBatch() {
-    const selectedAppIds = [...document.querySelectorAll('.app-cb:checked')].map(cb => cb.value);
-    if (!selectedAppIds.length) return alert('Select at least one app');
-
-    const stars = [...document.querySelectorAll('.star-cb:checked')].map(cb => parseInt(cb.value));
-    if (!stars.length) return alert('Select at least one star rating');
-
-    document.getElementById('welcome').classList.add('hidden');
-    document.getElementById('results').classList.add('hidden');
-    document.getElementById('loader').classList.remove('hidden');
-    document.getElementById('go').disabled = true;
-
-    try {
-      const res = await fetch('/scrape-batch', {
-        method: 'POST',
-        headers: { 'Content-Type': 'application/json' },
-        body: JSON.stringify({
-          app_ids: selectedAppIds,
-          review_count_type: currentMode,
-          reviews_per_app: document.getElementById('reviews_per_app').value,
-          sort_order: document.getElementById('sort').value,
-          star_ratings: stars.length === 5 ? 'all' : stars
-        })
-      });
-      
-      const data = await res.json();
-      if (!res.ok) throw new Error(data.error || 'Batch scraping failed');
-      
-      currentData = data;
-      render(data);
-    } catch(e) {
-      alert(e.message);
-    } finally {
-      document.getElementById('loader').classList.add('hidden');
-      document.getElementById('go').disabled = false;
-    }
-  }
-
-  function render(data) {
+  function render(data,customReviews){
+    const reviews=customReviews||data.reviews;
     document.getElementById('results').classList.remove('hidden');
+    document.getElementById('appsGrid').innerHTML=data.apps.map(a=>`<div class="app-mini-card"><img src="${a.icon}" alt=""><div class="app-mini-info"><div class="app-mini-title">${a.title}</div><div class="app-mini-score">${a.score.toFixed(1)} ★</div></div></div>`).join('');
+    document.getElementById('resultStats').innerText=`Found ${reviews.length} reviews`;
     
-    // Render apps list
-    document.getElementById('appsGrid').innerHTML = data.apps.map(a => `
-      <div class="app-mini-card">
-        <img src="${a.icon}" alt="">
-        <div class="app-mini-info">
-          <div class="app-mini-title">${a.title}</div>
-          <div class="app-mini-score">${a.score.toFixed(1)} ★</div>
-        </div>
-      </div>
-    `).join('');
-
-    // Render reviews table
-    document.getElementById('reviewsBody').innerHTML = data.reviews.map(r => {
-      const app = data.apps.find(a => a.appId === r.appId) || {title: r.appTitle};
+    document.getElementById('reviewsBody').innerHTML=reviews.map(r=>{
+      const app=data.apps.find(a=>a.appId===r.appId)||{title:r.appTitle};
+      const replyHtml = r.replyContent ? `<div class="dev-reply"><span class="dev-reply-label">Developer Reply</span>${r.replyContent}</div>` : '';
+      const helpfulHtml = `<div class="helpful-pill"><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="3" stroke-linecap="round" stroke-linejoin="round"><path d="M14 9V5a3 3 0 0 0-3-3l-4 9v11h11.28a2 2 0 0 0 2-1.7l1.38-9a2 2 0 0 0-2-2.3zM7 22H4a2 2 0 0 1-2-2v-7a2 2 0 0 1 2-2h3"/></svg>${r.thumbsUpCount || 0}</div>`;
+      
       return `
         <tr>
           <td>
             <div class="app-tag">${app.title}</div>
-            <div style="font-size:11px; font-weight:700;">${r.userName}</div>
+            <div style="font-size:11px;font-weight:700;color:var(--text);">${r.userName}</div>
           </td>
           <td>
-            <div class="score-stars">${'★'.repeat(r.score)}</div>
+            <div class="score-stars">${'★'.repeat(r.score)}<span style="color:var(--border)">${'★'.repeat(5-r.score)}</span></div>
           </td>
           <td>
             <div class="review-content">${r.content}</div>
+            ${replyHtml}
           </td>
-          <td>
-            <div style="color:var(--muted); font-size:11px;">${new Date(r.at).toLocaleDateString()}</div>
-          </td>
+          <td>${helpfulHtml}</td>
+          <td><div style="color:var(--muted);font-size:11px;">${new Date(r.at).toLocaleDateString(undefined, {month:'short', day:'numeric', year:'numeric'})}</div></td>
         </tr>
       `;
     }).join('');
   }
 
-  function downloadCSV() {
-    if (!currentData) return;
-    const esc = v => `"${String(v||'').replace(/"/g,'""')}"`;
-    const hdr = ['App Name', 'App ID', 'User', 'Score', 'Date', 'Content', 'Thumbs Up'];
-    
-    const rows = currentData.reviews.map(r => [
-      esc(r.appTitle),
-      esc(r.appId),
-      esc(r.userName),
-      r.score,
-      esc(r.at.slice(0,10)),
-      esc(r.content),
-      r.thumbsUpCount
-    ].join(','));
+  function downloadCSV(){
+    if(!currentData)return;
+    const esc=v=>`"${String(v||'').replace(/"/g,'""')}"`;
+    const hdr=['App Name','App ID','User','Score','Date','Content','Thumbs Up','Developer Reply'];
+    const rows=currentData.reviews.map(r=>[esc(r.appTitle),esc(r.appId),esc(r.userName),r.score,esc(r.at.slice(0,10)),esc(r.content),r.thumbsUpCount,esc(r.replyContent)].join(','));
+    const blob=new Blob([[hdr.join(','),...rows].join('\n')],{type:'text/csv'});
+    const a=Object.assign(document.createElement('a'),{href:URL.createObjectURL(blob),download:`batch_${Date.now()}.csv`});a.click();
+  }
+
+  // ══════════════════════════════════════════════════════════════════════
+  //  CHAT (shared logic identical to index.html)
+  // ══════════════════════════════════════════════════════════════════════
+  const SESSION_ID=(()=>{let id=sessionStorage.getItem('pp_sid');if(!id){id='sess_'+Math.random().toString(36).slice(2);sessionStorage.setItem('pp_sid',id);}return id;})();
+
+  function toggleChat(){document.getElementById('chat-window').classList.toggle('open');}
+  function fillChat(t){const i=document.getElementById('chat-input');i.value=t;i.focus();}
 
-    const blob = new Blob([[hdr.join(','), ...rows].join('\n')], { type: 'text/csv' });
-    const url = URL.createObjectURL(blob);
-    const a = document.createElement('a');
-    a.href = url;
-    a.download = `batch_comparison_${new Date().getTime()}.csv`;
-    a.click();
+  async function clearChat(){
+    document.getElementById('chat-messages').innerHTML=`<div class="msg-row bot"><div class="message bot">Chat cleared. Run a batch scrape then ask me anything!</div></div>`;
+    await fetch('/chat/clear',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({session_id:SESSION_ID})});
   }
-</script>
 
+  async function sendChatMessage(){
+    const input=document.getElementById('chat-input');const msg=input.value.trim();if(!msg)return;
+    appendUserMsg(msg);input.value='';
+    const container=document.getElementById('chat-messages');
+    const typing=document.createElement('div');typing.className='typing-indicator';typing.innerHTML='<div class="dot"></div><div class="dot"></div><div class="dot"></div>';
+    container.appendChild(typing);container.scrollTop=container.scrollHeight;
+    try{
+      const res=await fetch('/chat',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({message:msg,session_id:SESSION_ID,reviews:(currentData&&currentData.reviews)?currentData.reviews:[]})});
+      const data=await res.json();
+      if(typing.parentNode)container.removeChild(typing);
+      if(data.error){appendBotMsg('⚠️ '+data.error,null);return;}
+      appendBotMsg(data.reply||'',data.table||null);
+      if(data.type==='filter'&&data.filters)applyChatFilters(data.filters);
+    }catch(e){if(typing.parentNode)container.removeChild(typing);appendBotMsg('Connection error.',null);}
+  }
+
+  function appendUserMsg(text){const c=document.getElementById('chat-messages');const row=document.createElement('div');row.className='msg-row user';row.innerHTML=`<div class="message user">${escHtml(text)}</div>`;c.appendChild(row);c.scrollTop=c.scrollHeight;}
+
+  function appendBotMsg(text,table){
+    const c=document.getElementById('chat-messages');const row=document.createElement('div');row.className='msg-row bot';
+    if(text&&text.trim()){const b=document.createElement('div');b.className='message bot';b.innerHTML=renderMD(text);row.appendChild(b);}
+    if(table&&table.rows&&table.rows.length){row.appendChild(buildTable(table));}
+    c.appendChild(row);c.scrollTop=c.scrollHeight;
+  }
+
+  function renderMD(text){
+    const lines=text.split('\n');let html='',inList=false;
+    for(let raw of lines){
+      if(/^\*\*[^*]+\*\*:?$/.test(raw.trim())){if(inList){html+='</div>';inList=false;}html+=`<div class="msg-section">${escHtml(raw.trim().replace(/^\*\*/,'').replace(/\*\*:?$/,''))}</div>`;continue;}
+      const nm=raw.match(/^(\d+)\.\s+(.+)/);if(nm){if(!inList){html+='<div style="margin-top:6px">';inList=true;}html+=`<div class="msg-item"><span class="msg-item-num">${nm[1]}.</span><span>${inlineFmt(nm[2])}</span></div>`;continue;}
+      const bm=raw.match(/^[•\-\*]\s+(.+)/);if(bm){if(!inList){html+='<div style="margin-top:6px">';inList=true;}html+=`<div class="msg-item"><span class="msg-bullet">•</span><span>${inlineFmt(bm[1])}</span></div>`;continue;}
+      if(inList&&raw.trim()===''){html+='</div>';inList=false;}
+      if(raw.trim()===''){html+='<br>';}else{html+=`<span>${inlineFmt(raw)}</span><br>`;}
+    }
+    if(inList)html+='</div>';return html;
+  }
+  function inlineFmt(t){return escHtml(t).replace(/\*\*(.+?)\*\*/g,'<strong>$1</strong>').replace(/_(.+?)_/g,'<em style="color:var(--muted2)">$1</em>');}
+  function escHtml(s){return String(s).replace(/&/g,'&amp;').replace(/</g,'&lt;').replace(/>/g,'&gt;').replace(/"/g,'&quot;');}
+
+  function buildTable(td){
+    const{title,columns,rows}=td;const w=document.createElement('div');w.className='chat-table-wrap';
+    let h='';if(title)h+=`<div class="chat-table-title">${escHtml(title)}</div>`;
+    h+='<table class="chat-table"><thead><tr>';for(const c of columns)h+=`<th>${escHtml(c)}</th>`;h+='</tr></thead><tbody>';
+    for(const row of rows){h+='<tr>';for(const c of columns){const v=row[c]!==undefined?row[c]:'';h+=`<td title="${escHtml(String(v))}">${escHtml(String(v))}</td>`;}h+='</tr>';}
+    h+='</tbody></table>';w.innerHTML=h;return w;
+  }
+
+  function applyChatFilters(raw){
+    if(!currentData)return;
+    try{
+      const f=typeof raw==='string'?JSON.parse(raw):raw;let filtered=currentData.reviews;
+      if(f.stars&&f.stars.length)filtered=filtered.filter(r=>f.stars.includes(r.score));
+      if(f.app){const q=f.app.toLowerCase();filtered=filtered.filter(r=>{const app=currentData.apps.find(a=>a.appId===r.appId)||{title:r.appTitle||""};return(app.title||"").toLowerCase().includes(q)||r.appId.toLowerCase().includes(q);});}
+      if(f.query){const q=f.query.toLowerCase();filtered=filtered.filter(r=>(r.content||'').toLowerCase().includes(q)||(r.userName||'').toLowerCase().includes(q));}
+      render(currentData,filtered);
+    }catch(e){console.error('Filter error',e);}
+  }
+</script>
 </body>
-</html>
+</html>

templates/index.html

@@ -21,21 +21,16 @@
       --muted2: #94a3b8;
     }
     * { box-sizing: border-box; margin: 0; padding: 0; }
-    
-    /* Modern Scrollbar */
     ::-webkit-scrollbar { width: 6px; height: 6px; }
     ::-webkit-scrollbar-track { background: transparent; }
     ::-webkit-scrollbar-thumb { background: rgba(255,255,255,0.1); border-radius: 10px; }
     ::-webkit-scrollbar-thumb:hover { background: rgba(255,255,255,0.2); }
     * { scrollbar-width: thin; scrollbar-color: rgba(255,255,255,0.1) transparent; }
-
     body { font-family: 'Inter', sans-serif; background: var(--bg); color: var(--text); height: 100vh; overflow: hidden; display: flex; flex-direction: column; }
-
     .header { height: 60px; background: var(--surface); border-bottom: 1px solid var(--border); display: flex; align-items: center; padding: 0 20px; gap: 20px; }
     .main { flex: 1; display: flex; overflow: hidden; }
     .sidebar { width: 300px; background: var(--surface); border-right: 1px solid var(--border); padding: 20px; display: flex; flex-direction: column; gap: 20px; overflow-y: auto; }
     .content { flex: 1; background: var(--bg); position: relative; display: flex; flex-direction: column; }
-
     .logo { font-weight: 800; font-size: 18px; color: var(--accent); display: flex; align-items: center; gap: 8px; }
     .input-group { display: flex; flex-direction: column; gap: 8px; }
     .label { font-size: 11px; font-weight: 700; text-transform: uppercase; color: var(--muted); letter-spacing: 0.5px; display:flex; align-items:center; justify-content:space-between; }
@@ -50,12 +45,9 @@
     .btn-icon { width: 40px; height: 40px; border-radius: 10px; border: 1px solid var(--border); background: var(--bg); color: var(--muted); display: flex; align-items: center; justify-content: center; cursor: pointer; transition: 0.2s; flex-shrink: 0; }
     .btn-icon:hover { color: white; border-color: var(--accent); }
     .btn-icon svg { width: 18px; height: 18px; fill: none; stroke: currentColor; stroke-width: 2; }
-
     .view-tabs { display: flex; gap: 10px; }
     .tab { padding: 6px 16px; border-radius: 20px; font-size: 12px; font-weight: 700; cursor: pointer; border: 1px solid var(--border); color: var(--muted); transition: 0.2s; }
     .tab.active { background: var(--accent); color: white; border-color: var(--accent); }
-
-    /* Star Filter */
     .star-filter-grid { display: flex; flex-direction: column; gap: 6px; }
     .star-row { display: flex; align-items: center; gap: 10px; padding: 8px 10px; border-radius: 8px; border: 1px solid var(--border); background: var(--bg); cursor: pointer; transition: border-color 0.15s; user-select: none; }
     .star-row:hover { border-color: var(--accent); }
@@ -67,26 +59,18 @@
     .quick-btn:hover { color: white; border-color: var(--accent); }
     .filter-chips { display: flex; flex-wrap: wrap; gap: 6px; }
     .chip { font-size: 11px; font-weight: 700; padding: 3px 8px; border-radius: 20px; background: var(--accent-dim); color: var(--accent); border: 1px solid rgba(59,130,246,.3); }
-
-    /* Layout */
     .scroll-view { flex: 1; overflow-y: auto; padding: 20px; display: flex; flex-direction: column; gap: 15px; }
-
-    /* App header card */
     .app-card { background: var(--surface); border: 1px solid var(--border); padding: 20px; border-radius: 16px; display: flex; gap: 20px; }
     .app-card img { width: 80px; height: 80px; border-radius: 16px; object-fit: cover; }
     .app-stats { display: flex; gap: 10px; margin-top: 12px; flex-wrap: wrap; }
     .stat-pill { background: var(--surface2); border: 1px solid var(--border); border-radius: 8px; padding: 6px 12px; display: flex; flex-direction: column; align-items: center; min-width: 58px; }
     .stat-val { font-size: 15px; font-weight: 800; line-height: 1; }
     .stat-key { font-size: 9px; font-weight: 700; text-transform: uppercase; color: var(--muted); margin-top: 3px; letter-spacing: .5px; }
-
-    /* Summary bar */
     .summary-bar { background: var(--surface); border: 1px solid var(--border); border-radius: 12px; padding: 14px 18px; display: flex; gap: 20px; align-items: center; flex-wrap: wrap; }
     .star-dist { flex: 1; display: flex; flex-direction: column; gap: 5px; min-width: 160px; }
     .star-bar-row { display: flex; align-items: center; gap: 7px; font-size: 11px; }
     .star-bar-track { flex: 1; height: 5px; background: var(--border); border-radius: 3px; overflow: hidden; }
     .star-bar-fill { height: 100%; border-radius: 3px; background: var(--amber); }
-
-    /* Review card */
     .review-card { background: var(--surface); border: 1px solid var(--border); border-radius: 14px; overflow: hidden; transition: border-color .15s; }
     .review-card:hover { border-color: #2d3a4f; }
     .review-main { padding: 16px 18px; }
@@ -98,125 +82,37 @@
     .review-date { font-size: 11px; color: var(--muted); margin-top: 1px; }
     .review-stars { display: flex; gap: 1px; flex-shrink: 0; }
     .review-text { font-size: 13px; color: #cbd5e1; line-height: 1.6; }
-
-    /* Meta pills row */
     .review-footer { padding: 10px 18px; background: var(--bg); border-top: 1px solid var(--border); display: flex; flex-wrap: wrap; gap: 8px; align-items: center; }
     .meta-pill { display: inline-flex; align-items: center; gap: 5px; font-size: 11px; font-weight: 600; padding: 4px 9px; border-radius: 20px; border: 1px solid var(--border); color: var(--muted2); background: var(--surface); }
     .meta-pill svg { width: 11px; height: 11px; fill: none; stroke: currentColor; stroke-width: 2.5; flex-shrink: 0; }
     .meta-pill.thumbs { color: #3b82f6; border-color: rgba(59,130,246,.25); background: var(--accent-dim); }
     .meta-pill.version { color: #a78bfa; border-color: rgba(167,139,250,.25); background: rgba(167,139,250,.08); }
     .meta-pill.replied { color: var(--green); border-color: rgba(34,197,94,.25); background: var(--green-dim); }
-
-    /* Dev reply block */
     .dev-reply { margin: 0 18px 16px; background: var(--surface2); border: 1px solid var(--border); border-left: 3px solid var(--green); border-radius: 10px; padding: 12px 14px; }
     .dev-reply-header { font-size: 11px; font-weight: 700; color: var(--green); margin-bottom: 6px; display: flex; align-items: center; gap: 5px; }
     .dev-reply-text { font-size: 12px; color: var(--muted2); line-height: 1.55; }
     .dev-reply-date { font-size: 10px; color: var(--muted); margin-top: 5px; }
-
-    /* Overlays */
     .loader-overlay { position: absolute; inset: 0; background: var(--bg); display: flex; flex-direction: column; align-items: center; justify-content: center; gap: 15px; z-index: 10; }
     .spinner { width: 40px; height: 40px; border: 4px solid var(--border); border-top-color: var(--accent); border-radius: 50%; animation: spin .8s linear infinite; }
     .site-overlay { position: absolute; inset: 0; background: var(--bg); display: flex; flex-direction: column; align-items: center; justify-content: center; text-align: center; gap: 15px; padding: 40px; }
     .site-overlay h3 { font-size: 18px; font-weight: 700; }
     .site-overlay p { color: var(--muted); font-size: 14px; max-width: 400px; }
-
     .hidden { display: none !important; }
     @keyframes spin { to { transform: rotate(360deg); } }
-
-    /* Search Suggestions Overlay */
     .search-wrap { position: relative; }
-    .suggestions-box {
-      position: absolute;
-      top: calc(100% + 8px);
-      left: 0;
-      right: 0;
-      background: var(--surface2);
-      border: 1px solid var(--border);
-      border-radius: 12px;
-      z-index: 1000;
-      max-height: 400px;
-      overflow-y: auto;
-      box-shadow: 0 15px 40px rgba(0,0,0,0.6);
-      backdrop-filter: blur(10px);
-    }
-    .suggestion-item {
-      display: flex;
-      align-items: center;
-      padding: 12px 14px;
-      gap: 12px;
-      cursor: pointer;
-      transition: .2s cubic-bezier(0.4, 0, 0.2, 1);
-      border-bottom: 1px solid var(--border);
-    }
+    .suggestions-box { position: absolute; top: calc(100% + 8px); left: 0; right: 0; background: var(--surface2); border: 1px solid var(--border); border-radius: 12px; z-index: 1000; max-height: 400px; overflow-y: auto; box-shadow: 0 15px 40px rgba(0,0,0,0.6); backdrop-filter: blur(10px); }
+    .suggestion-item { display: flex; align-items: center; padding: 12px 14px; gap: 12px; cursor: pointer; transition: .2s; border-bottom: 1px solid var(--border); }
     .suggestion-item:last-child { border-bottom: none; }
-    .suggestion-item:hover { background: var(--accent-dim); border-color: var(--accent); }
-    .suggestion-item img {
-      width: 44px;
-      height: 44px;
-      border-radius: 10px;
-      object-fit: cover;
-      border: 1px solid var(--border);
-    }
+    .suggestion-item:hover { background: var(--accent-dim); }
+    .suggestion-item img { width: 44px; height: 44px; border-radius: 10px; object-fit: cover; border: 1px solid var(--border); }
     .suggestion-info { flex: 1; min-width: 0; }
-    .suggestion-title {
-      font-size: 13px;
-      font-weight: 700;
-      white-space: nowrap;
-      overflow: hidden;
-      text-overflow: ellipsis;
-      color: var(--text);
-    }
-    .suggestion-sub {
-      font-size: 11px;
-      color: var(--muted);
-      margin-top: 2px;
-      white-space: nowrap;
-      overflow: hidden;
-      text-overflow: ellipsis;
-    }
-    .suggestion-score {
-      font-size: 11px;
-      font-weight: 700;
-      color: var(--amber);
-      background: rgba(245, 158, 11, 0.1);
-      padding: 2px 6px;
-      border-radius: 4px;
-    }
-    .suggestion-loading {
-      padding: 30px 20px;
-      text-align: center;
-      color: var(--muted);
-      font-size: 12px;
-      display: flex;
-      flex-direction: column;
-      align-items: center;
-      gap: 10px;
-    }
-    .suggestion-loading .spinner-small {
-      width: 20px;
-      height: 20px;
-      border: 2px solid var(--border);
-      border-top-color: var(--accent);
-      border-radius: 50%;
-      animation: spin .8s linear infinite;
-    }
-
-    /* Search Section Page Styles */
-    .search-results-grid {
-      display: grid;
-      grid-template-columns: repeat(auto-fill, minmax(280px, 1fr));
-      gap: 15px;
-    }
-    .search-app-card {
-      background: var(--surface);
-      border: 1px solid var(--border);
-      border-radius: 16px;
-      padding: 16px;
-      display: flex;
-      gap: 15px;
-      cursor: pointer;
-      transition: .2s;
-    }
+    .suggestion-title { font-size: 13px; font-weight: 700; white-space: nowrap; overflow: hidden; text-overflow: ellipsis; color: var(--text); }
+    .suggestion-sub { font-size: 11px; color: var(--muted); margin-top: 2px; white-space: nowrap; overflow: hidden; text-overflow: ellipsis; }
+    .suggestion-score { font-size: 11px; font-weight: 700; color: var(--amber); background: rgba(245,158,11,0.1); padding: 2px 6px; border-radius: 4px; }
+    .suggestion-loading { padding: 30px 20px; text-align: center; color: var(--muted); font-size: 12px; display: flex; flex-direction: column; align-items: center; gap: 10px; }
+    .suggestion-loading .spinner-small { width: 20px; height: 20px; border: 2px solid var(--border); border-top-color: var(--accent); border-radius: 50%; animation: spin .8s linear infinite; }
+    .search-results-grid { display: grid; grid-template-columns: repeat(auto-fill, minmax(280px, 1fr)); gap: 15px; }
+    .search-app-card { background: var(--surface); border: 1px solid var(--border); border-radius: 16px; padding: 16px; display: flex; gap: 15px; cursor: pointer; transition: .2s; }
     .search-app-card:hover { border-color: var(--accent); background: var(--surface2); transform: translateY(-2px); }
     .search-app-card img { width: 60px; height: 60px; border-radius: 12px; }
     .search-app-info { flex: 1; min-width: 0; display: flex; flex-direction: column; justify-content: space-between; }
@@ -225,19 +121,66 @@
     .search-app-meta { display: flex; align-items: center; gap: 8px; font-size: 11px; font-weight: 600; }
     .search-app-score { color: var(--amber); }
     .search-app-installs { color: var(--muted2); }
+
+    /* ── Chat styles ── */
+    #chat-dialer { position:fixed; bottom:24px; right:24px; width:56px; height:56px; background:var(--accent); border-radius:50%; display:flex; align-items:center; justify-content:center; box-shadow:0 8px 32px rgba(59,130,246,0.4); cursor:pointer; z-index:1000; transition:0.3s cubic-bezier(0.175,0.885,0.32,1.275); border:2px solid rgba(255,255,255,0.1); }
+    #chat-dialer:hover { transform:scale(1.1) rotate(5deg); box-shadow:0 12px 40px rgba(59,130,246,0.6); }
+    #chat-dialer svg { width:24px; height:24px; color:white; fill:none; stroke:currentColor; stroke-width:2.5; }
+    #chat-window { position:fixed; bottom:90px; right:24px; width:420px; height:600px; background:var(--surface); border:1px solid var(--border); border-radius:20px; display:flex; flex-direction:column; box-shadow:0 20px 50px rgba(0,0,0,0.5); z-index:1001; overflow:hidden; transform:translateY(20px) scale(0.95); opacity:0; pointer-events:none; transition:0.3s cubic-bezier(0.4,0,0.2,1); backdrop-filter:blur(20px); }
+    #chat-window.open { transform:translateY(0) scale(1); opacity:1; pointer-events:auto; }
+    .chat-header { padding:14px 18px; background:var(--accent); color:white; display:flex; align-items:center; gap:12px; flex-shrink:0; }
+    .chat-header-info { flex:1; }
+    .chat-header-title { font-weight:800; font-size:15px; }
+    .chat-header-status { font-size:10px; opacity:0.8; display:flex; align-items:center; gap:4px; }
+    .status-dot { width:6px; height:6px; background:#22c55e; border-radius:50%; }
+    .chat-header-actions { display:flex; gap:8px; align-items:center; }
+    .chat-clear-btn { background:rgba(255,255,255,0.15); border:none; color:white; font-size:11px; padding:4px 10px; border-radius:8px; cursor:pointer; transition:0.2s; }
+    .chat-clear-btn:hover { background:rgba(255,255,255,0.25); }
+    .chat-messages { flex:1; overflow-y:auto; padding:16px; display:flex; flex-direction:column; gap:12px; background-image:radial-gradient(var(--border) 1px,transparent 1px); background-size:20px 20px; }
+    .msg-row { display:flex; flex-direction:column; gap:4px; }
+    .msg-row.user { align-items:flex-end; }
+    .msg-row.bot { align-items:flex-start; }
+    .message { max-width:88%; padding:11px 15px; border-radius:16px; font-size:13px; line-height:1.6; }
+    .message.user { background:var(--accent); color:white; border-bottom-right-radius:4px; }
+    .message.bot { background:var(--surface2); color:var(--text); border:1px solid var(--border); border-bottom-left-radius:4px; white-space:pre-wrap; word-break:break-word; }
+    .msg-section { margin-top:10px; font-weight:700; font-size:11px; color:var(--accent); letter-spacing:0.05em; text-transform:uppercase; }
+    .msg-item { display:flex; gap:8px; margin-top:5px; }
+    .msg-item-num { font-weight:700; color:var(--accent); min-width:16px; }
+    .msg-bullet { color:var(--accent); min-width:14px; }
+    .chat-table-wrap { max-width:100%; overflow-x:auto; border:1px solid var(--border); border-radius:12px; background:var(--surface2); margin-top:4px; }
+    .chat-table-title { padding:8px 12px; font-size:11px; font-weight:700; color:var(--accent); border-bottom:1px solid var(--border); letter-spacing:0.05em; text-transform:uppercase; }
+    .chat-table { width:100%; border-collapse:collapse; font-size:12px; }
+    .chat-table th { padding:7px 12px; text-align:left; font-weight:700; font-size:11px; color:var(--muted2); background:var(--bg); border-bottom:1px solid var(--border); white-space:nowrap; }
+    .chat-table td { padding:7px 12px; border-bottom:1px solid var(--border); color:var(--text); max-width:180px; overflow:hidden; text-overflow:ellipsis; white-space:nowrap; }
+    .chat-table tr:last-child td { border-bottom:none; }
+    .chat-table tr:hover td { background:var(--surface); }
+    .typing-indicator { display:flex; gap:4px; padding:12px 16px; background:var(--surface2); border:1px solid var(--border); border-radius:16px; width:fit-content; }
+    .dot { width:6px; height:6px; background:var(--muted); border-radius:50%; animation:bounce 1.4s infinite; }
+    .dot:nth-child(2) { animation-delay:0.2s; }
+    .dot:nth-child(3) { animation-delay:0.4s; }
+    @keyframes bounce { 0%,80%,100%{transform:translateY(0)} 40%{transform:translateY(-6px)} }
+    .chat-input-area { padding:14px 16px; background:var(--surface); border-top:1px solid var(--border); display:flex; gap:10px; flex-shrink:0; }
+    #chat-input { flex:1; background:var(--bg); border:1px solid var(--border); color:white; padding:10px 14px; border-radius:12px; font-size:13px; outline:none; }
+    #chat-input:focus { border-color:var(--accent); }
+    .btn-send { width:40px; height:40px; background:var(--accent); color:white; border:none; border-radius:10px; display:flex; align-items:center; justify-content:center; cursor:pointer; transition:0.2s; flex-shrink:0; }
+    .btn-send:hover { transform:scale(1.05); }
+    .btn-send svg { width:18px; height:18px; fill:none; stroke:currentColor; stroke-width:2.5; }
+    .chat-suggestions { display:flex; flex-wrap:wrap; gap:6px; padding:0 16px 10px; }
+    .sug-chip { font-size:11px; padding:5px 10px; border-radius:20px; background:var(--surface2); border:1px solid var(--border); color:var(--muted2); cursor:pointer; transition:0.2s; }
+    .sug-chip:hover { border-color:var(--accent); color:var(--accent); }
   </style>
 </head>
 <body>
 
 <div class="header">
-  <a href="/" class="logo" style="text-decoration: none;">
+  <a href="/" class="logo" style="text-decoration:none;">
     <svg width="22" height="22" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="3" stroke-linecap="round" stroke-linejoin="round"><path d="M12 2v20M17 5H9.5a3.5 3.5 0 0 0 0 7h5a3.5 3.5 0 0 1 0 7H6"/></svg>
     PLAYPULSE
   </a>
-  <nav style="margin-left: 30px; display: flex; gap: 20px;">
-    <a href="/" style="color: var(--muted2); text-decoration: none; font-size: 13px; font-weight: 600; transition: 0.2s;" onmouseover="this.style.color='var(--text)'" onmouseout="this.style.color='var(--muted2)'">Home</a>
-    <a href="/scraper" style="color: var(--text); text-decoration: none; font-size: 13px; font-weight: 700; border-bottom: 2px solid var(--accent); padding-bottom: 4px;">Single Explorer</a>
-    <a href="/batch" style="color: var(--muted2); text-decoration: none; font-size: 13px; font-weight: 600; transition: 0.2s;" onmouseover="this.style.color='var(--text)'" onmouseout="this.style.color='var(--muted2)'">Batch Intelligence</a>
+  <nav style="margin-left:30px;display:flex;gap:20px;">
+    <a href="/" style="color:var(--muted2);text-decoration:none;font-size:13px;font-weight:600;" onmouseover="this.style.color='var(--text)'" onmouseout="this.style.color='var(--muted2)'">Home</a>
+    <a href="/scraper" style="color:var(--text);text-decoration:none;font-size:13px;font-weight:700;border-bottom:2px solid var(--accent);padding-bottom:4px;">Single Explorer</a>
+    <a href="/batch" style="color:var(--muted2);text-decoration:none;font-size:13px;font-weight:600;" onmouseover="this.style.color='var(--text)'" onmouseout="this.style.color='var(--muted2)'">Batch Intelligence</a>
   </nav>
   <div style="flex:1"></div>
   <div class="view-tabs">
@@ -258,7 +201,6 @@
         <div class="suggestions-box hidden" id="suggestionsBox"></div>
       </div>
     </div>
-
     <div class="input-group">
       <div class="label">Amount of Data</div>
       <div class="toggle-grp">
@@ -267,7 +209,6 @@
       </div>
       <input type="number" id="manualCount" value="200" placeholder="Count (e.g. 500)">
     </div>
-
     <div class="input-group">
       <div class="label">Strategy</div>
       <select id="sort">
@@ -276,11 +217,10 @@
         <option value="RATING">Top Ratings</option>
       </select>
     </div>
-
     <div class="input-group">
       <div class="label">
         <span>Star Rating Filter</span>
-        <div style="display:flex;gap:5px">
+        <div style="display:flex;gap:5px;">
           <button class="quick-btn" onclick="selectAllStars(true)">All</button>
           <button class="quick-btn" onclick="selectAllStars(false)">None</button>
         </div>
@@ -294,12 +234,10 @@
       </div>
       <div class="filter-chips" id="filterChips"></div>
     </div>
-
     <button class="btn-main" id="go" onclick="run()">
       <svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="3"><polyline points="13 17 18 12 13 7"/><line x1="6" y1="17" x2="6" y2="7"/></svg>
       START SCRAPING
     </button>
-
     <div class="input-group">
       <div class="label">Recent Sessions</div>
       <div id="recentList" style="display:flex;flex-direction:column;gap:8px;"></div>
@@ -314,16 +252,14 @@
       </div>
       <div id="results" class="hidden"></div>
     </div>
-
     <div id="loader" class="loader-overlay hidden">
       <div class="spinner"></div>
       <p style="color:var(--muted);font-size:14px" id="loaderMsg">Connecting to servers…</p>
     </div>
-
     <div id="siteView" class="hidden" style="height:100%">
       <div class="site-overlay">
         <h3>Web View Shielded</h3>
-        <p>Google Play Store blocks previewing inside other apps for security. Use this button to view the live site in a new tab.</p>
+        <p>Google Play Store blocks previewing inside other apps. Use the button below to view it in a new tab.</p>
         <button class="btn-main" style="width:auto;padding:12px 24px" onclick="openTarget()">
           Open on Google Play
           <svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.5"><path d="M18 13v6a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h6M15 3h6v6M10 14L21 3"/></svg>
@@ -333,6 +269,44 @@
   </div>
 </div>
 
+<!-- Chat bubble -->
+<div id="chat-dialer" onclick="toggleChat()">
+  <svg viewBox="0 0 24 24"><path d="M21 15a2 2 0 0 1-2 2H7l-4 4V5a2 2 0 0 1 2-2h14a2 2 0 0 1 2 2z"/></svg>
+</div>
+
+<div id="chat-window">
+  <div class="chat-header">
+    <svg width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="white" stroke-width="2.5"><circle cx="12" cy="12" r="3"/><path d="M12 1v4M12 19v4M4.22 4.22l2.83 2.83M16.95 16.95l2.83 2.83M1 12h4M19 12h4M4.22 19.78l2.83-2.83M16.95 7.05l2.83-2.83"/></svg>
+    <div class="chat-header-info">
+      <div class="chat-header-title">PlayPulse Intelligence</div>
+      <div class="chat-header-status"><span class="status-dot"></span> Agent Online</div>
+    </div>
+    <div class="chat-header-actions">
+      <button class="chat-clear-btn" onclick="clearChat()">Clear</button>
+      <div style="cursor:pointer;opacity:0.7;" onclick="toggleChat()">
+        <svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="white" stroke-width="2.5"><line x1="18" y1="6" x2="6" y2="18"/><line x1="6" y1="6" x2="18" y2="18"/></svg>
+      </div>
+    </div>
+  </div>
+  <div class="chat-messages" id="chat-messages">
+    <div class="msg-row bot">
+      <div class="message bot">👋 Hi! I'm PlayPulse Intelligence. Ask me anything about the loaded reviews — comparisons, issues, sentiment, keyword search, or say <em>"show in table"</em>.</div>
+    </div>
+  </div>
+  <div class="chat-suggestions" id="chat-sug">
+    <div class="sug-chip" onclick="fillChat('What are the main issues?')">Main issues</div>
+    <div class="sug-chip" onclick="fillChat('Compare all apps by rating')">Compare apps</div>
+    <div class="sug-chip" onclick="fillChat('Show most helpful reviews')">Most helpful</div>
+    <div class="sug-chip" onclick="fillChat('Show 1 star reviews in table')">1★ table</div>
+  </div>
+  <div class="chat-input-area">
+    <input type="text" id="chat-input" placeholder="Ask about the reviews…" onkeydown="if(event.key==='Enter') sendChatMessage()">
+    <button class="btn-send" onclick="sendChatMessage()">
+      <svg viewBox="0 0 24 24"><line x1="22" y1="2" x2="11" y2="13"/><polygon points="22 2 15 22 11 13 2 9 22 2"/></svg>
+    </button>
+  </div>
+</div>
+
 <script>
   let mode = 'limit';
   let currentData = null;
@@ -343,20 +317,17 @@
     document.getElementById('btnLimit').classList.toggle('active', m==='limit');
     document.getElementById('manualCount').classList.toggle('hidden', m==='all');
   }
-
   function switchView(v, event) {
     document.querySelectorAll('.tab').forEach(t => t.classList.remove('active'));
     event.target.classList.add('active');
     document.getElementById('dataView').classList.toggle('hidden', v!=='data');
     document.getElementById('siteView').classList.toggle('hidden', v!=='site');
   }
-
   function openTarget() {
     const url = document.getElementById('target').value;
     if (url.startsWith('http')) window.open(url,'_blank');
     else if (currentData) window.open(`https://play.google.com/store/apps/details?id=${currentData.app_info.appId}`,'_blank');
   }
-
   function selectAllStars(checked) {
     document.querySelectorAll('.star-cb').forEach(cb => cb.checked = checked);
     updateChips();
@@ -370,31 +341,19 @@
     return [...document.querySelectorAll('.star-cb:checked')].map(cb=>parseInt(cb.value));
   }
   document.querySelectorAll('.star-cb').forEach(cb => cb.addEventListener('change', updateChips));
-
   function renderStars(score) {
-    let out = '';
-    for (let i=1;i<=5;i++) out += `<span style="font-size:13px;color:${i<=score?'var(--amber)':'var(--border)'}">${i<=score?'★':'★'}</span>`;
+    let out='';
+    for(let i=1;i<=5;i++) out+=`<span style="font-size:13px;color:${i<=score?'var(--amber)':'var(--border)'}">★</span>`;
     return out;
   }
-
-  function fmtDate(iso) {
-    if (!iso) return '';
-    return new Date(iso).toLocaleDateString('en-US',{year:'numeric',month:'short',day:'numeric'});
-  }
-
-  function fmtNum(n) {
-    if (!n) return null;
-    if (n>=1000000) return (n/1000000).toFixed(1)+'M';
-    if (n>=1000) return (n/1000).toFixed(1)+'k';
-    return String(n);
-  }
+  function fmtDate(iso) { if(!iso)return''; return new Date(iso).toLocaleDateString('en-US',{year:'numeric',month:'short',day:'numeric'}); }
+  function fmtNum(n) { if(!n)return null; if(n>=1000000)return(n/1000000).toFixed(1)+'M'; if(n>=1000)return(n/1000).toFixed(1)+'k'; return String(n); }
 
   async function run() {
-    const query = document.getElementById('target').value.trim();
-    if (!query) return;
-    const selectedStars = getSelectedStars();
-    if (!selectedStars.length) { alert('Select at least one star rating.'); return; }
-
+    const query=document.getElementById('target').value.trim();
+    if(!query)return;
+    const selectedStars=getSelectedStars();
+    if(!selectedStars.length){alert('Select at least one star rating.');return;}
     document.getElementById('results').innerHTML='';
     currentData=null;
     document.getElementById('welcome').classList.add('hidden');
@@ -402,41 +361,21 @@
     document.getElementById('loader').classList.remove('hidden');
     hideSuggestions();
     document.getElementById('go').disabled=true;
-
     const msgs=['Connecting to servers…','Fetching app info…','Scraping reviews…','Processing data…'];
     let mi=0;
     document.getElementById('loaderMsg').textContent=msgs[0];
     const msgInt=setInterval(()=>{mi=(mi+1)%msgs.length;document.getElementById('loaderMsg').textContent=msgs[mi];},2200);
-
     try {
-      const res = await fetch('/scrape',{
-        method:'POST',
-        headers:{'Content-Type':'application/json'},
-        body:JSON.stringify({
-          identifier:query,
-          review_count_type:mode,
-          review_count:parseInt(document.getElementById('manualCount').value)||200,
-          sort_order:document.getElementById('sort').value,
-          star_ratings:selectedStars.length===5?'all':selectedStars
-        })
-      });
+      const res=await fetch('/scrape',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({identifier:query,review_count_type:mode,review_count:parseInt(document.getElementById('manualCount').value)||200,sort_order:document.getElementById('sort').value,star_ratings:selectedStars.length===5?'all':selectedStars})});
       const data=await res.json();
-      if (!res.ok) throw new Error(data.error || 'Scraping failed');
-      
+      if(!res.ok)throw new Error(data.error||'Scraping failed');
       currentData=data;
-      document.getElementById('results').classList.remove('hidden'); // CRITICAL FIX: show the div!
+      document.getElementById('results').classList.remove('hidden');
       render(data,selectedStars);
       save(data.app_info);
     } catch(e) {
-      document.getElementById('results').classList.remove('hidden'); // Also show for errors
-      document.getElementById('results').innerHTML = `
-        <div style="background:rgba(239,68,68,0.08);border:1px solid rgba(239,68,68,0.3);border-radius:14px;padding:24px 28px;display:flex;flex-direction:column;gap:10px">
-          <div style="display:flex;align-items:center;gap:10px">
-            <svg width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="#ef4444" stroke-width="2.5"><circle cx="12" cy="12" r="10"/><line x1="12" y1="8" x2="12" y2="12"/><line x1="12" y1="16" x2="12.01" y2="16"/></svg>
-            <span style="font-weight:700;color:#ef4444;font-size:14px">Operation Failed</span>
-          </div>
-          <p style="color:var(--muted2);font-size:13px;line-height:1.6">${e.message}</p>
-        </div>`;
+      document.getElementById('results').classList.remove('hidden');
+      document.getElementById('results').innerHTML=`<div style="background:rgba(239,68,68,0.08);border:1px solid rgba(239,68,68,0.3);border-radius:14px;padding:24px 28px;"><div style="display:flex;align-items:center;gap:10px;"><svg width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="#ef4444" stroke-width="2.5"><circle cx="12" cy="12" r="10"/><line x1="12" y1="8" x2="12" y2="12"/><line x1="12" y1="16" x2="12.01" y2="16"/></svg><span style="font-weight:700;color:#ef4444;">Operation Failed</span></div><p style="color:var(--muted2);font-size:13px;margin-top:10px;">${e.message}</p></div>`;
     } finally {
       clearInterval(msgInt);
       document.getElementById('loader').classList.add('hidden');
@@ -444,10 +383,9 @@
     }
   }
 
-  function render(data, selectedStars) {
-    const { reviews, app_info: info } = data;
-
-    // Compute stats
+  function render(data,selectedStars,customReviews) {
+    const reviews=customReviews||data.reviews;
+    const info=data.app_info;
     const dist={1:0,2:0,3:0,4:0,5:0};
     reviews.forEach(r=>{if(r.score>=1&&r.score<=5)dist[r.score]++;});
     const total=reviews.length;
@@ -455,122 +393,35 @@
     const avgScore=total?(reviews.reduce((a,r)=>a+(r.score||0),0)/total).toFixed(2):'—';
     const totalLikes=reviews.reduce((a,r)=>a+(r.thumbsUpCount||0),0);
     const filterLabel=selectedStars.length===5?'All Ratings':selectedStars.sort((a,b)=>b-a).map(s=>`${s}★`).join(', ');
-
-    // Star distribution bars
-    const starDistHTML=[5,4,3,2,1].map(s=>{
-      const pct=total?Math.round((dist[s]/total)*100):0;
-      return `<div class="star-bar-row">
-        <span style="color:var(--amber);width:12px;text-align:right">${s}</span>
-        <div class="star-bar-track"><div class="star-bar-fill" style="width:${pct}%"></div></div>
-        <span style="color:var(--muted);width:30px;text-align:right">${pct}%</span>
-      </div>`;
-    }).join('');
-
-    // Individual review cards
+    const starDistHTML=[5,4,3,2,1].map(s=>{const pct=total?Math.round((dist[s]/total)*100):0;return`<div class="star-bar-row"><span style="color:var(--amber);width:12px;text-align:right">${s}</span><div class="star-bar-track"><div class="star-bar-fill" style="width:${pct}%"></div></div><span style="color:var(--muted);width:30px;text-align:right">${pct}%</span></div>`;}).join('');
     const reviewsHTML=reviews.map(r=>{
       const thumbsLabel=fmtNum(r.thumbsUpCount);
       const hasReply=r.replyContent&&r.replyContent.trim();
       const version=r.reviewCreatedVersion;
-
-      // Build meta pills — only if data exists
       const pills=[
-        thumbsLabel ? `<span class="meta-pill thumbs">
-          <svg viewBox="0 0 24 24"><path d="M14 9V5a3 3 0 0 0-3-3l-4 9v11h11.28a2 2 0 0 0 2-1.7l1.38-9a2 2 0 0 0-2-2.3H14z"/><path d="M7 22H4a2 2 0 0 1-2-2v-7a2 2 0 0 1 2-2h3"/></svg>
-          ${thumbsLabel} helpful
-        </span>` : '',
-        version ? `<span class="meta-pill version">
-          <svg viewBox="0 0 24 24"><circle cx="12" cy="12" r="10"/><path d="M12 8v4l3 3"/></svg>
-          v${version}
-        </span>` : '',
-        hasReply ? `<span class="meta-pill replied">
-          <svg viewBox="0 0 24 24"><path d="M21 15a2 2 0 0 1-2 2H7l-4 4V5a2 2 0 0 1 2-2h14a2 2 0 0 1 2 2z"/></svg>
-          Dev replied
-        </span>` : ''
+        thumbsLabel?`<span class="meta-pill thumbs"><svg viewBox="0 0 24 24"><path d="M14 9V5a3 3 0 0 0-3-3l-4 9v11h11.28a2 2 0 0 0 2-1.7l1.38-9a2 2 0 0 0-2-2.3H14z"/><path d="M7 22H4a2 2 0 0 1-2-2v-7a2 2 0 0 1 2-2h3"/></svg>${thumbsLabel} helpful</span>`:'',
+        version?`<span class="meta-pill version"><svg viewBox="0 0 24 24"><circle cx="12" cy="12" r="10"/><path d="M12 8v4l3 3"/></svg>v${version}</span>`:'',
+        hasReply?`<span class="meta-pill replied"><svg viewBox="0 0 24 24"><path d="M21 15a2 2 0 0 1-2 2H7l-4 4V5a2 2 0 0 1 2-2h14a2 2 0 0 1 2 2z"/></svg>Dev replied</span>`:''
       ].filter(Boolean).join('');
-
-      // Developer reply block
-      const replyHTML=hasReply?`
-        <div class="dev-reply">
-          <div class="dev-reply-header">
-            <svg width="12" height="12" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.5"><path d="M21 15a2 2 0 0 1-2 2H7l-4 4V5a2 2 0 0 1 2-2h14a2 2 0 0 1 2 2z"/></svg>
-            Developer Response
-          </div>
-          <div class="dev-reply-text">${r.replyContent}</div>
-          ${r.repliedAt?`<div class="dev-reply-date">${fmtDate(r.repliedAt)}</div>`:''}
-        </div>`:'';
-
-      // User avatar
+      const replyHTML=hasReply?`<div class="dev-reply"><div class="dev-reply-header"><svg width="12" height="12" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.5"><path d="M21 15a2 2 0 0 1-2 2H7l-4 4V5a2 2 0 0 1 2-2h14a2 2 0 0 1 2 2z"/></svg>Developer Response</div><div class="dev-reply-text">${r.replyContent}</div>${r.repliedAt?`<div class="dev-reply-date">${fmtDate(r.repliedAt)}</div>`:''}</div>`:'';
       const initials=(r.userName||'?').trim().split(/\s+/).map(w=>w[0]).join('').slice(0,2).toUpperCase();
-      const avatarHTML=r.userImage
-        ?`<div class="user-avatar"><img src="${r.userImage}" alt="" onerror="this.style.display='none';this.parentElement.textContent='${initials}'"></div>`
-        :`<div class="user-avatar">${initials}</div>`;
-
-      return `
-        <div class="review-card">
-          <div class="review-main">
-            <div class="review-header">
-              ${avatarHTML}
-              <div class="review-meta">
-                <div class="review-username">${r.userName||'Anonymous'}</div>
-                <div class="review-date">${fmtDate(r.at)}</div>
-              </div>
-              <div class="review-stars">${renderStars(r.score)}</div>
-            </div>
-            <div class="review-text">${r.content||'<em style="color:var(--muted)">No review text</em>'}</div>
-          </div>
-          ${pills?`<div class="review-footer">${pills}</div>`:''}
-          ${replyHTML}
-        </div>`;
+      const avatarHTML=r.userImage?`<div class="user-avatar"><img src="${r.userImage}" alt="" onerror="this.style.display='none';this.parentElement.textContent='${initials}'"></div>`:`<div class="user-avatar">${initials}</div>`;
+      return`<div class="review-card"><div class="review-main"><div class="review-header">${avatarHTML}<div class="review-meta"><div class="review-username">${r.userName||'Anonymous'}</div><div class="review-date">${fmtDate(r.at)}</div></div><div class="review-stars">${renderStars(r.score)}</div></div><div class="review-text">${r.content||'<em style="color:var(--muted)">No review text</em>'}</div></div>${pills?`<div class="review-footer">${pills}</div>`:''} ${replyHTML}</div>`;
     }).join('');
-
     document.getElementById('results').innerHTML=`
-      <div class="app-card">
-        <img src="${info.icon}" alt="icon">
-        <div style="flex:1">
-          <h2 style="font-size:20px;font-weight:800;margin-bottom:3px">${info.title}</h2>
-          <div style="color:var(--accent);font-weight:700;font-size:11px;margin-bottom:10px">${info.appId}</div>
-          <div class="app-stats">
-            <div class="stat-pill"><span class="stat-val" style="color:var(--amber)">${(info.score||0).toFixed(1)}</span><span class="stat-key">Store Avg</span></div>
-            <div class="stat-pill"><span class="stat-val">${total.toLocaleString()}</span><span class="stat-key">Fetched</span></div>
-            <div class="stat-pill"><span class="stat-val" style="color:var(--green)">${repliedCount}</span><span class="stat-key">Replied</span></div>
-            <div class="stat-pill"><span class="stat-val" style="color:var(--accent)">${fmtNum(totalLikes)||'0'}</span><span class="stat-key">Total Likes</span></div>
-          </div>
-        </div>
-      </div>
-
-      <div class="summary-bar">
-        <div>
-          <div style="font-size:24px;font-weight:800;color:var(--amber)">${avgScore}</div>
-          <div style="font-size:9px;font-weight:700;text-transform:uppercase;color:var(--muted);letter-spacing:.5px;margin-top:2px">Avg Score</div>
-        </div>
-        <div class="star-dist">${starDistHTML}</div>
-        <div style="font-size:11px;color:var(--muted);padding:6px 12px;border-radius:8px;background:var(--bg);border:1px solid var(--border)">
-          Filter:<br><strong style="color:var(--accent)">${filterLabel}</strong>
-        </div>
-      </div>
-
-      <div style="display:flex;flex-direction:column;gap:10px">${reviewsHTML}</div>
-    `;
+      <div class="app-card"><img src="${info.icon}" alt="icon"><div style="flex:1"><h2 style="font-size:20px;font-weight:800;margin-bottom:3px">${info.title}</h2><div style="color:var(--accent);font-weight:700;font-size:11px;margin-bottom:10px">${info.appId}</div><div class="app-stats"><div class="stat-pill"><span class="stat-val" style="color:var(--amber)">${(info.score||0).toFixed(1)}</span><span class="stat-key">Store Avg</span></div><div class="stat-pill"><span class="stat-val">${total.toLocaleString()}</span><span class="stat-key">Fetched</span></div><div class="stat-pill"><span class="stat-val" style="color:var(--green)">${repliedCount}</span><span class="stat-key">Replied</span></div><div class="stat-pill"><span class="stat-val" style="color:var(--accent)">${fmtNum(totalLikes)||'0'}</span><span class="stat-key">Total Likes</span></div></div></div></div>
+      <div class="summary-bar"><div><div style="font-size:24px;font-weight:800;color:var(--amber)">${avgScore}</div><div style="font-size:9px;font-weight:700;text-transform:uppercase;color:var(--muted);letter-spacing:.5px;margin-top:2px">Avg Score</div></div><div class="star-dist">${starDistHTML}</div><div style="font-size:11px;color:var(--muted);padding:6px 12px;border-radius:8px;background:var(--bg);border:1px solid var(--border)">Filter:<br><strong style="color:var(--accent)">${filterLabel}</strong></div></div>
+      <div style="display:flex;flex-direction:column;gap:10px">${reviewsHTML}</div>`;
   }
 
   function downloadCSV() {
-    if (!currentData) return;
+    if(!currentData)return;
     const esc=v=>`"${String(v||'').replace(/"/g,'""')}"`;
     const hdr=['Review ID','User','Score','Date','Content','Thumbs Up','App Version','Dev Reply','Dev Reply Date'];
-    const rows=currentData.reviews.map(r=>[
-      esc(r.reviewId||''),
-      esc(r.userName||''),
-      r.score||0,
-      esc((r.at||'').slice(0,10)),
-      esc(r.content||''),
-      r.thumbsUpCount||0,
-      esc(r.reviewCreatedVersion||''),
-      esc(r.replyContent||''),
-      esc((r.repliedAt||'').slice(0,10))
-    ].join(','));
+    const rows=currentData.reviews.map(r=>[esc(r.reviewId||''),esc(r.userName||''),r.score||0,esc((r.at||'').slice(0,10)),esc(r.content||''),r.thumbsUpCount||0,esc(r.reviewCreatedVersion||''),esc(r.replyContent||''),esc((r.repliedAt||'').slice(0,10))].join(','));
     const blob=new Blob([[hdr.join(','),...rows].join('\n')],{type:'text/csv'});
     const a=Object.assign(document.createElement('a'),{href:URL.createObjectURL(blob),download:`${currentData.app_info.appId}_reviews.csv`});
-    a.click(); URL.revokeObjectURL(a.href);
+    a.click();URL.revokeObjectURL(a.href);
   }
 
   function save(info) {
@@ -579,133 +430,145 @@
     localStorage.setItem('scrapes',JSON.stringify(list));
     loadRecent();
   }
-
   function loadRecent() {
     const list=JSON.parse(localStorage.getItem('scrapes')||'[]');
-    document.getElementById('recentList').innerHTML=list.map(x=>`
-      <div onclick="document.getElementById('target').value='${x.appId}';run()" style="cursor:pointer;background:var(--bg);padding:10px;border-radius:8px;display:flex;gap:10px;align-items:center;border:1px solid var(--border);transition:.15s" onmouseover="this.style.borderColor='var(--accent)'" onmouseout="this.style.borderColor='var(--border)'">
-        <img src="${x.icon}" style="width:26px;height:26px;border-radius:5px" alt="">
-        <span style="font-size:12px;font-weight:600;overflow:hidden;text-overflow:ellipsis;white-space:nowrap;flex:1">${x.title}</span>
-        <span style="font-size:10px;color:var(--muted)">${(x.score||0).toFixed(1)}★</span>
-      </div>`).join('');
+    document.getElementById('recentList').innerHTML=list.map(x=>`<div onclick="document.getElementById('target').value='${x.appId}';run()" style="cursor:pointer;background:var(--bg);padding:10px;border-radius:8px;display:flex;gap:10px;align-items:center;border:1px solid var(--border);transition:.15s" onmouseover="this.style.borderColor='var(--accent)'" onmouseout="this.style.borderColor='var(--border)'"><img src="${x.icon}" style="width:26px;height:26px;border-radius:5px" alt=""><span style="font-size:12px;font-weight:600;overflow:hidden;text-overflow:ellipsis;white-space:nowrap;flex:1">${x.title}</span><span style="font-size:10px;color:var(--muted)">${(x.score||0).toFixed(1)}★</span></div>`).join('');
   }
-
   loadRecent();
 
-  // ── Live search suggestions ──────────────────────────────────────────
-  let searchTimer = null;
-  const targetEl = document.getElementById('target');
-  const sugBox   = document.getElementById('suggestionsBox');
-
-  function hideSuggestions() {
-    sugBox.classList.add('hidden');
-    sugBox.innerHTML = '';
+  // ── Live search suggestions ────────────────────────────────────────────
+  let searchTimer=null;
+  const targetEl=document.getElementById('target');
+  const sugBox=document.getElementById('suggestionsBox');
+  function hideSuggestions(){sugBox.classList.add('hidden');sugBox.innerHTML='';}
+  targetEl.addEventListener('input',()=>{
+    clearTimeout(searchTimer);
+    const q=targetEl.value.trim();
+    if(q.startsWith('http')||/^[a-z][a-z0-9_]*(\.[a-z][a-z0-9_]*)+$/i.test(q)){hideSuggestions();return;}
+    if(q.length<2){hideSuggestions();return;}
+    sugBox.classList.remove('hidden');
+    sugBox.innerHTML='<div class="suggestion-loading"><div class="spinner-small"></div>Searching…</div>';
+    if(document.getElementById('results').classList.contains('hidden')){document.getElementById('welcome').classList.add('hidden');document.getElementById('results').classList.remove('hidden');}
+    document.getElementById('results').innerHTML=`<div style="margin-bottom:20px;display:flex;align-items:center;gap:10px;"><span style="font-size:18px;font-weight:800;">Search Results: "${q}"</span></div><div class="search-results-grid" id="searchGrid"><div style="grid-column:1/-1;padding:40px;text-align:center;"><div class="spinner" style="margin:0 auto 15px;"></div><p style="color:var(--muted)">Searching the Play Store...</p></div></div>`;
+    searchTimer=setTimeout(async()=>{
+      try {
+        const res=await fetch('/search-suggestions',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({query:q})});
+        const data=await res.json();
+        if(!data.results||!data.results.length){sugBox.innerHTML='<div class="suggestion-loading">No results found</div>';return;}
+        sugBox.innerHTML=data.results.map(r=>`<div class="suggestion-item" onclick="selectSuggestion('${r.appId}','${r.title.replace(/'/g,"\\'")}')"><img src="${r.icon}" alt=""><div class="suggestion-info"><div class="suggestion-title">${r.title}</div><div class="suggestion-sub">${r.developer} &bull; ${r.installs}</div></div><div class="suggestion-score">${r.score>0?r.score+' ★':''}</div></div>`).join('');
+        const gridHTML=data.results.map(r=>`<div class="search-app-card" onclick="selectSuggestion('${r.appId}','${r.title.replace(/'/g,"\\'")}')"><img src="${r.icon}" alt=""><div class="search-app-info"><div><div class="search-app-title" title="${r.title}">${r.title}</div><div style="font-size:10px;color:var(--accent);font-family:monospace;margin-bottom:4px;">${r.appId}</div><div class="search-app-dev">${r.developer}</div></div><div class="search-app-meta"><span class="search-app-score">${r.score>0?'★ '+r.score:'N/A'}</span><span class="search-app-installs">${r.installs}</span><a href="${r.storeUrl}" target="_blank" onclick="event.stopPropagation()" style="margin-left:auto;color:var(--muted);font-size:10px;text-decoration:none;">Store ↗</a></div></div></div>`).join('');
+        document.getElementById('searchGrid').innerHTML=gridHTML;
+      } catch(err){hideSuggestions();}
+    },400);
+  });
+  function selectSuggestion(appId,title){
+    const validId=appId&&appId!=='null'&&appId!=='None'&&!appId.includes('None');
+    targetEl.value=validId?`https://play.google.com/store/apps/details?id=${appId}`:title;
+    hideSuggestions();run();
   }
+  document.addEventListener('click',e=>{if(!targetEl.contains(e.target)&&!sugBox.contains(e.target))hideSuggestions();});
+  targetEl.addEventListener('keydown',e=>{if(e.key==='Escape')hideSuggestions();if(e.key==='Enter'){hideSuggestions();run();}});
 
-  targetEl.addEventListener('input', () => {
-    clearTimeout(searchTimer);
-    const q = targetEl.value.trim();
+  // ══════════════════════════════════════════════════════════════════════
+  //  CHAT — rich rendering, tables, markdown-lite, session memory
+  // ══════════════════════════════════════════════════════════════════════
+  const SESSION_ID=(()=>{let id=sessionStorage.getItem('pp_sid');if(!id){id='sess_'+Math.random().toString(36).slice(2);sessionStorage.setItem('pp_sid',id);}return id;})();
 
-    // If it looks like a URL or package ID, skip suggestions
-    if (q.startsWith('http') || /^[a-z][a-z0-9_]*(\.[a-z][a-z0-9_]*)+$/i.test(q)) {
-      hideSuggestions(); return;
-    }
-    if (q.length < 2) { hideSuggestions(); return; }
+  function toggleChat(){document.getElementById('chat-window').classList.toggle('open');}
+  function fillChat(t){const i=document.getElementById('chat-input');i.value=t;i.focus();}
 
-    // Show loading state in sidebar
-    sugBox.classList.remove('hidden');
-    sugBox.innerHTML = '<div class="suggestion-loading"><div class="spinner-small"></div>Searching…</div>';
+  async function clearChat(){
+    document.getElementById('chat-messages').innerHTML=`<div class="msg-row bot"><div class="message bot">Chat cleared. Ask me anything about the loaded reviews!</div></div>`;
+    await fetch('/chat/clear',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({session_id:SESSION_ID})});
+  }
 
-    // Also show in main section if current view is empty/welcome
-    if (document.getElementById('results').classList.contains('hidden')) {
-      document.getElementById('welcome').classList.add('hidden');
-      document.getElementById('results').classList.remove('hidden');
+  async function sendChatMessage(){
+    const input=document.getElementById('chat-input');
+    const msg=input.value.trim();
+    if(!msg)return;
+    appendUserMsg(msg);
+    input.value='';
+    const container=document.getElementById('chat-messages');
+    const typing=document.createElement('div');
+    typing.className='typing-indicator';
+    typing.innerHTML='<div class="dot"></div><div class="dot"></div><div class="dot"></div>';
+    container.appendChild(typing);
+    container.scrollTop=container.scrollHeight;
+    try {
+      const res=await fetch('/chat',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({message:msg,session_id:SESSION_ID,reviews:(currentData&&currentData.reviews)?currentData.reviews:[]})});
+      const data=await res.json();
+      if(typing.parentNode)container.removeChild(typing);
+      if(data.error){appendBotMsg('⚠️ '+data.error,null);return;}
+      appendBotMsg(data.reply||'',data.table||null);
+      if(data.type==='filter'&&data.filters)applyChatFilters(data.filters);
+    } catch(e){
+      if(typing.parentNode)container.removeChild(typing);
+      appendBotMsg('Connection error. Is the server running?',null);
     }
-    
-    document.getElementById('results').innerHTML = `
-      <div style="margin-bottom: 20px; display: flex; align-items: center; gap: 10px;">
-        <span style="font-size: 18px; font-weight: 800;">Search Results: "${q}"</span>
-      </div>
-      <div class="search-results-grid" id="searchGrid">
-        <div style="grid-column: 1/-1; padding: 40px; text-align: center;">
-          <div class="spinner" style="margin: 0 auto 15px;"></div>
-          <p style="color:var(--muted)">Searching the Play Store...</p>
-        </div>
-      </div>
-    `;
-
-    searchTimer = setTimeout(async () => {
-      try {
-        const res  = await fetch('/search-suggestions', {
-          method: 'POST',
-          headers: { 'Content-Type': 'application/json' },
-          body: JSON.stringify({ query: q })
-        });
-        const data = await res.json();
-
-        if (!data.results || !data.results.length) {
-          sugBox.innerHTML = '<div class="suggestion-loading">No results found</div>';
-          return;
-        }
+  }
 
-        sugBox.innerHTML = data.results.map(r => `
-          <div class="suggestion-item" onclick="selectSuggestion('${r.appId}', '${r.title.replace(/'/g,"\\'")}')">
-            <img src="${r.icon}" alt="">
-            <div class="suggestion-info">
-              <div class="suggestion-title">${r.title}</div>
-              <div class="suggestion-sub">${r.developer} &bull; ${r.installs}</div>
-            </div>
-            <div class="suggestion-score">${r.score > 0 ? r.score + ' ★' : ''}</div>
-          </div>`).join('');
+  function appendUserMsg(text){
+    const c=document.getElementById('chat-messages');
+    const row=document.createElement('div');row.className='msg-row user';
+    row.innerHTML=`<div class="message user">${escHtml(text)}</div>`;
+    c.appendChild(row);c.scrollTop=c.scrollHeight;
+  }
 
-        // Grid view for the main section
-        const gridHTML = data.results.map(r => `
-          <div class="search-app-card" onclick="selectSuggestion('${r.appId}', '${r.title.replace(/'/g,"\\'")}')">
-            <img src="${r.icon}" alt="">
-            <div class="search-app-info">
-              <div>
-                <div class="search-app-title" title="${r.title}">${r.title}</div>
-                <div style="font-size:10px; color:var(--accent); font-family:monospace; margin-bottom:4px;">${r.appId}</div>
-                <div class="search-app-dev">${r.developer}</div>
-              </div>
-              <div class="search-app-meta">
-                <span class="search-app-score">${r.score > 0 ? '★ ' + r.score : 'N/A'}</span>
-                <span class="search-app-installs">${r.installs}</span>
-                <a href="${r.storeUrl}" target="_blank" onclick="event.stopPropagation()" style="margin-left:auto; color:var(--muted); font-size:10px; text-decoration:none; display:flex; align-items:center; gap:4px;">
-                  Store <svg width="10" height="10" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.5"><path d="M18 13v6a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h6M15 3h6v6M10 14L21 3"/></svg>
-                </a>
-              </div>
-            </div>
-          </div>
-        `).join('');
-        
-        document.getElementById('searchGrid').innerHTML = gridHTML;
+  function appendBotMsg(text,table){
+    const c=document.getElementById('chat-messages');
+    const row=document.createElement('div');row.className='msg-row bot';
+    if(text&&text.trim()){
+      const b=document.createElement('div');b.className='message bot';
+      b.innerHTML=renderMD(text);row.appendChild(b);
+    }
+    if(table&&table.rows&&table.rows.length){row.appendChild(buildTable(table));}
+    c.appendChild(row);c.scrollTop=c.scrollHeight;
+  }
 
-      } catch (err) {
-        console.error(err);
-        hideSuggestions();
-        document.getElementById('results').innerHTML = `<div style="padding: 20px; color: var(--amber);">Failed to load search results.</div>`;
+  function renderMD(text){
+    const lines=text.split('\n');let html='',inList=false;
+    for(let raw of lines){
+      if(/^\*\*[^*]+\*\*:?$/.test(raw.trim())){
+        if(inList){html+='</div>';inList=false;}
+        html+=`<div class="msg-section">${escHtml(raw.trim().replace(/^\*\*/,'').replace(/\*\*:?$/,''))}</div>`;continue;
       }
-    }, 400); // 400ms debounce
-  });
-
-  function selectSuggestion(appId, title) {
-    const validId = appId && appId !== 'null' && appId !== 'None' && !appId.includes('None');
-    const query = validId ? `https://play.google.com/store/apps/details?id=${appId}` : title;
-    targetEl.value = query;
-    hideSuggestions();
-    run();  // auto-start scraping
+      const nm=raw.match(/^(\d+)\.\s+(.+)/);
+      if(nm){if(!inList){html+='<div style="margin-top:6px">';inList=true;}html+=`<div class="msg-item"><span class="msg-item-num">${nm[1]}.</span><span>${inlineFmt(nm[2])}</span></div>`;continue;}
+      const bm=raw.match(/^[•\-\*]\s+(.+)/);
+      if(bm){if(!inList){html+='<div style="margin-top:6px">';inList=true;}html+=`<div class="msg-item"><span class="msg-bullet">•</span><span>${inlineFmt(bm[1])}</span></div>`;continue;}
+      if(inList&&raw.trim()===''){html+='</div>';inList=false;}
+      if(raw.trim()===''){html+='<br>';}else{html+=`<span>${inlineFmt(raw)}</span><br>`;}
+    }
+    if(inList)html+='</div>';
+    return html;
+  }
+  function inlineFmt(t){return escHtml(t).replace(/\*\*(.+?)\*\*/g,'<strong>$1</strong>').replace(/_(.+?)_/g,'<em style="color:var(--muted2)">$1</em>');}
+  function escHtml(s){return String(s).replace(/&/g,'&amp;').replace(/</g,'&lt;').replace(/>/g,'&gt;').replace(/"/g,'&quot;');}
+
+  function buildTable(td){
+    const {title,columns,rows}=td;
+    const w=document.createElement('div');w.className='chat-table-wrap';
+    let h='';
+    if(title)h+=`<div class="chat-table-title">${escHtml(title)}</div>`;
+    h+='<table class="chat-table"><thead><tr>';
+    for(const c of columns)h+=`<th>${escHtml(c)}</th>`;
+    h+='</tr></thead><tbody>';
+    for(const row of rows){h+='<tr>';for(const c of columns){const v=row[c]!==undefined?row[c]:'';h+=`<td title="${escHtml(String(v))}">${escHtml(String(v))}</td>`;}h+='</tr>';}
+    h+='</tbody></table>';
+    w.innerHTML=h;return w;
   }
 
-  // Hide on click outside
-  document.addEventListener('click', e => {
-    if (!targetEl.contains(e.target) && !sugBox.contains(e.target)) hideSuggestions();
-  });
-
-  targetEl.addEventListener('keydown', e => {
-    if (e.key === 'Escape') hideSuggestions();
-    if (e.key === 'Enter') { hideSuggestions(); run(); }
-  });
+  function applyChatFilters(raw){
+    if(!currentData)return;
+    try{
+      const f=typeof raw==='string'?JSON.parse(raw):raw;
+      let filtered=currentData.reviews;
+      if(f.stars&&f.stars.length)filtered=filtered.filter(r=>f.stars.includes(r.score));
+      if(f.app){const q=f.app.toLowerCase();filtered=filtered.filter(r=>(r.appTitle||'').toLowerCase().includes(q)||(r.appId||'').toLowerCase().includes(q));}
+      if(f.query){const q=f.query.toLowerCase();filtered=filtered.filter(r=>(r.content||'').toLowerCase().includes(q)||(r.userName||'').toLowerCase().includes(q));}
+      render(currentData,getSelectedStars(),filtered);
+    }catch(e){console.error('Filter error',e);}
+  }
 </script>
 </body>
 </html>

templates/landing.html

@@ -6,239 +6,57 @@
   <title>PlayPulse | Intelligence Platform</title>
   <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&family=Outfit:wght@400;600;800&display=swap" rel="stylesheet">
   <style>
-    :root {
-      --bg: #0b0e14;
-      --surface: #151921;
-      --border: rgba(255, 255, 255, 0.08);
-      --accent: #3b82f6;
-      --accent-gradient: linear-gradient(135deg, #3b82f6 0%, #2dd4bf 100%);
-      --text: #f1f5f9;
-      --muted: #94a3b8;
-    }
-
-    * { box-sizing: border-box; margin: 0; padding: 0; }
-    
-    /* Modern Scrollbar */
-    ::-webkit-scrollbar { width: 6px; height: 6px; }
-    ::-webkit-scrollbar-track { background: transparent; }
-    ::-webkit-scrollbar-thumb { background: rgba(255,255,255,0.1); border-radius: 10px; }
-    ::-webkit-scrollbar-thumb:hover { background: rgba(255,255,255,0.2); }
-    * { scrollbar-width: thin; scrollbar-color: rgba(255,255,255,0.1) transparent; }
-
-    body {
-      font-family: 'Inter', sans-serif;
-      background: var(--bg);
-      color: var(--text);
-      min-height: 100vh;
-      display: flex;
-      flex-direction: column;
-      overflow-x: hidden;
-    }
-
-    .blob {
-      position: fixed;
-      width: 500px;
-      height: 500px;
-      background: var(--accent);
-      filter: blur(120px);
-      opacity: 0.1;
-      z-index: -1;
-      border-radius: 50%;
-    }
-    .blob-1 { top: -100px; right: -100px; }
-    .blob-2 { bottom: -100px; left: -100px; background: #2dd4bf; }
-
-    header {
-      padding: 30px 5%;
-      display: flex;
-      justify-content: space-between;
-      align-items: center;
-    }
-
-    .logo {
-      font-family: 'Outfit', sans-serif;
-      font-weight: 800;
-      font-size: 24px;
-      letter-spacing: -1px;
-      color: var(--text);
-      display: flex;
-      align-items: center;
-      gap: 10px;
-    }
-
-    .logo-icon {
-      width: 32px;
-      height: 32px;
-      background: var(--accent-gradient);
-      border-radius: 8px;
-      display: flex;
-      align-items: center;
-      justify-content: center;
-    }
-
-    main {
-      flex: 1;
-      display: flex;
-      flex-direction: column;
-      align-items: center;
-      justify-content: center;
-      padding: 60px 5%;
-      max-width: 1200px;
-      margin: 0 auto;
-      text-align: center;
-    }
-
-    .hero-tag {
-      background: rgba(59, 130, 246, 0.1);
-      border: 1px solid rgba(59, 130, 246, 0.2);
-      color: var(--accent);
-      padding: 6px 16px;
-      border-radius: 100px;
-      font-size: 13px;
-      font-weight: 700;
-      margin-bottom: 24px;
-      text-transform: uppercase;
-      letter-spacing: 1px;
-    }
-
-    h1 {
-      font-family: 'Outfit', sans-serif;
-      font-size: clamp(40px, 8vw, 72px);
-      font-weight: 800;
-      line-height: 1.1;
-      margin-bottom: 20px;
-      letter-spacing: -2px;
-    }
-
-    h1 span {
-      background: var(--accent-gradient);
-      -webkit-background-clip: text;
-      background-clip: text;
-      -webkit-text-fill-color: transparent;
-    }
-
-    .sub-hero {
-      color: var(--muted);
-      font-size: clamp(16px, 2vw, 20px);
-      max-width: 600px;
-      margin-bottom: 60px;
-      line-height: 1.6;
-    }
-
-    .cards-container {
-      display: grid;
-      grid-template-columns: repeat(auto-fit, minmax(340px, 1fr));
-      gap: 30px;
-      width: 100%;
-    }
-
-    .card {
-      background: var(--surface);
-      border: 1px solid var(--border);
-      padding: 40px;
-      border-radius: 24px;
-      text-align: left;
-      transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
-      cursor: pointer;
-      position: relative;
-      overflow: hidden;
-      display: flex;
-      flex-direction: column;
-      height: 100%;
-    }
-
-    .card:hover {
-      border-color: rgba(59, 130, 246, 0.5);
-      transform: translateY(-8px);
-      box-shadow: 0 20px 40px rgba(0, 0, 0, 0.4);
-    }
-
-    .card.disabled {
-      cursor: not-allowed;
-      opacity: 0.8;
-    }
-    .card.disabled:hover { transform: none; border-color: var(--border); }
-
-    .card-icon {
-      width: 56px;
-      height: 56px;
-      background: rgba(255, 255, 255, 0.03);
-      border: 1px solid var(--border);
-      border-radius: 16px;
-      display: flex;
-      align-items: center;
-      justify-content: center;
-      margin-bottom: 24px;
-      transition: 0.3s;
-    }
-
-    .card:hover .card-icon {
-      background: var(--accent);
-      color: white;
-      border-color: var(--accent);
-    }
-
-    .card h2 {
-      font-family: 'Outfit', sans-serif;
-      font-size: 24px;
-      margin-bottom: 12px;
-      font-weight: 700;
-    }
-
-    .card p {
-      color: var(--muted);
-      line-height: 1.6;
-      font-size: 15px;
-      margin-bottom: 24px;
-      flex: 1;
-    }
-
-    .badge {
-      position: absolute;
-      top: 20px;
-      right: 20px;
-      background: rgba(255, 255, 255, 0.05);
-      border: 1px solid var(--border);
-      padding: 4px 12px;
-      border-radius: 100px;
-      font-size: 11px;
-      font-weight: 700;
-      color: var(--muted);
-      text-transform: uppercase;
-    }
-    .badge.active {
-      background: var(--accent-dim);
-      color: var(--accent);
-      border-color: rgba(59, 130, 246, 0.2);
-    }
-
-    .btn {
-      display: inline-flex;
-      align-items: center;
-      gap: 8px;
-      font-weight: 700;
-      font-size: 14px;
-      color: var(--text);
-      transition: 0.3s;
-    }
-
-    .card:hover .btn {
-      color: var(--accent);
-    }
-
-    .card.disabled .btn { color: var(--muted); }
-
-    footer {
-      padding: 40px;
-      text-align: center;
-      color: var(--muted);
-      font-size: 13px;
-    }
-
-    @media (max-width: 768px) {
-      h1 { font-size: 48px; }
-      .cards-container { grid-template-columns: 1fr; }
-    }
+    :root { --bg:#0b0e14; --surface:#151921; --surface2:#1c2333; --border:rgba(255,255,255,0.08); --accent:#3b82f6; --accent-gradient:linear-gradient(135deg,#3b82f6 0%,#2dd4bf 100%); --text:#f1f5f9; --muted:#94a3b8; }
+    *{box-sizing:border-box;margin:0;padding:0;}
+    ::-webkit-scrollbar{width:6px;} ::-webkit-scrollbar-track{background:transparent;} ::-webkit-scrollbar-thumb{background:rgba(255,255,255,0.1);border-radius:10px;}
+    body{font-family:'Inter',sans-serif;background:var(--bg);color:var(--text);min-height:100vh;display:flex;flex-direction:column;overflow-x:hidden;}
+    .blob{position:fixed;width:500px;height:500px;background:var(--accent);filter:blur(120px);opacity:0.1;z-index:-1;border-radius:50%;}
+    .blob-1{top:-100px;right:-100px;} .blob-2{bottom:-100px;left:-100px;background:#2dd4bf;}
+    header{padding:30px 5%;display:flex;justify-content:space-between;align-items:center;}
+    .logo{font-family:'Outfit',sans-serif;font-weight:800;font-size:24px;letter-spacing:-1px;color:var(--text);display:flex;align-items:center;gap:10px;}
+    .logo-icon{width:32px;height:32px;background:var(--accent-gradient);border-radius:8px;display:flex;align-items:center;justify-content:center;}
+    main{flex:1;display:flex;flex-direction:column;align-items:center;justify-content:center;padding:60px 5%;max-width:1200px;margin:0 auto;text-align:center;width:100%;}
+    .hero-tag{background:rgba(59,130,246,0.1);border:1px solid rgba(59,130,246,0.2);color:var(--accent);padding:6px 16px;border-radius:100px;font-size:13px;font-weight:700;margin-bottom:24px;text-transform:uppercase;letter-spacing:1px;}
+    h1{font-family:'Outfit',sans-serif;font-size:clamp(40px,8vw,72px);font-weight:800;line-height:1.1;margin-bottom:20px;letter-spacing:-2px;}
+    h1 span{background:var(--accent-gradient);-webkit-background-clip:text;background-clip:text;-webkit-text-fill-color:transparent;}
+    .sub-hero{color:var(--muted);font-size:clamp(16px,2vw,20px);max-width:600px;margin-bottom:60px;line-height:1.6;}
+    .cards-container{display:grid;grid-template-columns:repeat(auto-fit,minmax(340px,1fr));gap:30px;width:100%;}
+    .card{background:var(--surface);border:1px solid var(--border);padding:40px;border-radius:24px;text-align:left;transition:all 0.3s cubic-bezier(0.4,0,0.2,1);cursor:pointer;position:relative;overflow:hidden;display:flex;flex-direction:column;height:100%;}
+    .card:hover{border-color:rgba(59,130,246,0.5);transform:translateY(-8px);box-shadow:0 20px 40px rgba(0,0,0,0.4);}
+    .card-icon{width:56px;height:56px;background:rgba(255,255,255,0.03);border:1px solid var(--border);border-radius:16px;display:flex;align-items:center;justify-content:center;margin-bottom:24px;transition:0.3s;}
+    .card:hover .card-icon{background:var(--accent);color:white;border-color:var(--accent);}
+    .card h2{font-family:'Outfit',sans-serif;font-size:24px;margin-bottom:12px;font-weight:700;}
+    .card p{color:var(--muted);line-height:1.6;font-size:15px;margin-bottom:24px;flex:1;}
+    .badge{position:absolute;top:20px;right:20px;background:rgba(255,255,255,0.05);border:1px solid var(--border);padding:4px 12px;border-radius:100px;font-size:11px;font-weight:700;color:var(--muted);text-transform:uppercase;}
+    .badge.active{background:rgba(59,130,246,0.1);color:var(--accent);border-color:rgba(59,130,246,0.2);}
+    .btn{display:inline-flex;align-items:center;gap:8px;font-weight:700;font-size:14px;color:var(--text);transition:0.3s;}
+    .card:hover .btn{color:var(--accent);}
+    footer{padding:40px;text-align:center;color:var(--muted);font-size:13px;}
+    @media(max-width:768px){h1{font-size:48px;}.cards-container{grid-template-columns:1fr;}}
+
+    /* ── Chat styles ── */
+    #chat-dialer{position:fixed;bottom:24px;right:24px;width:56px;height:56px;background:var(--accent);border-radius:50%;display:flex;align-items:center;justify-content:center;box-shadow:0 8px 32px rgba(59,130,246,0.4);cursor:pointer;z-index:1000;transition:0.3s cubic-bezier(0.175,0.885,0.32,1.275);border:2px solid rgba(255,255,255,0.1);}
+    #chat-dialer:hover{transform:scale(1.1) rotate(5deg);box-shadow:0 12px 40px rgba(59,130,246,0.6);}
+    #chat-dialer svg{width:24px;height:24px;color:white;fill:none;stroke:currentColor;stroke-width:2.5;}
+    #chat-window{position:fixed;bottom:90px;right:24px;width:420px;height:560px;background:var(--surface);border:1px solid rgba(255,255,255,0.1);border-radius:20px;display:flex;flex-direction:column;box-shadow:0 20px 50px rgba(0,0,0,0.5);z-index:1001;overflow:hidden;transform:translateY(20px) scale(0.95);opacity:0;pointer-events:none;transition:0.3s cubic-bezier(0.4,0,0.2,1);backdrop-filter:blur(20px);}
+    #chat-window.open{transform:translateY(0) scale(1);opacity:1;pointer-events:auto;}
+    .chat-header{padding:14px 18px;background:var(--accent);color:white;display:flex;align-items:center;gap:12px;flex-shrink:0;}
+    .chat-header-info{flex:1;} .chat-header-title{font-weight:800;font-size:15px;} .chat-header-status{font-size:10px;opacity:0.8;display:flex;align-items:center;gap:4px;} .status-dot{width:6px;height:6px;background:#22c55e;border-radius:50%;}
+    .chat-header-actions{display:flex;gap:8px;align-items:center;}
+    .chat-clear-btn{background:rgba(255,255,255,0.15);border:none;color:white;font-size:11px;padding:4px 10px;border-radius:8px;cursor:pointer;transition:0.2s;} .chat-clear-btn:hover{background:rgba(255,255,255,0.25);}
+    .chat-messages{flex:1;overflow-y:auto;padding:16px;display:flex;flex-direction:column;gap:12px;background-image:radial-gradient(rgba(255,255,255,0.05) 1px,transparent 1px);background-size:20px 20px;}
+    .msg-row{display:flex;flex-direction:column;gap:4px;} .msg-row.user{align-items:flex-end;} .msg-row.bot{align-items:flex-start;}
+    .message{max-width:88%;padding:11px 15px;border-radius:16px;font-size:13px;line-height:1.6;}
+    .message.user{background:var(--accent);color:white;border-bottom-right-radius:4px;}
+    .message.bot{background:var(--surface2);color:var(--text);border:1px solid rgba(255,255,255,0.08);border-bottom-left-radius:4px;white-space:pre-wrap;word-break:break-word;}
+    .msg-section{margin-top:10px;font-weight:700;font-size:11px;color:var(--accent);letter-spacing:0.05em;text-transform:uppercase;}
+    .msg-item{display:flex;gap:8px;margin-top:5px;} .msg-item-num{font-weight:700;color:var(--accent);min-width:16px;} .msg-bullet{color:var(--accent);min-width:14px;}
+    .typing-indicator{display:flex;gap:4px;padding:12px 16px;background:var(--surface2);border:1px solid rgba(255,255,255,0.08);border-radius:16px;width:fit-content;}
+    .dot{width:6px;height:6px;background:#64748b;border-radius:50%;animation:bounce 1.4s infinite;} .dot:nth-child(2){animation-delay:0.2s;} .dot:nth-child(3){animation-delay:0.4s;}
+    @keyframes bounce{0%,80%,100%{transform:translateY(0)}40%{transform:translateY(-6px)}}
+    .chat-input-area{padding:14px 16px;background:var(--surface);border-top:1px solid rgba(255,255,255,0.06);display:flex;gap:10px;flex-shrink:0;}
+    #chat-input{flex:1;background:var(--bg);border:1px solid rgba(255,255,255,0.08);color:white;padding:10px 14px;border-radius:12px;font-size:13px;outline:none;} #chat-input:focus{border-color:var(--accent);}
+    .btn-send{width:40px;height:40px;background:var(--accent);color:white;border:none;border-radius:10px;display:flex;align-items:center;justify-content:center;cursor:pointer;transition:0.2s;flex-shrink:0;} .btn-send:hover{transform:scale(1.05);} .btn-send svg{width:18px;height:18px;fill:none;stroke:currentColor;stroke-width:2.5;}
   </style>
 </head>
 <body>
@@ -257,41 +75,104 @@
   <main>
     <div class="hero-tag">Next-Gen Intelligence</div>
     <h1>Extract Insights from <span>Global App Data</span></h1>
-    <p class="sub-hero">The most powerful tool for analyzing app reviews, sentiment, and developer responses in real-time.</p>
+    <p class="sub-hero">The most powerful tool for analyzing app reviews, sentiment, and developer responses in real-time. Powered by AI chat.</p>
 
     <div class="cards-container">
-      <!-- Single Search Card -->
       <div class="card" onclick="location.href='/scraper'">
         <div class="badge active">Live Now</div>
         <div class="card-icon">
           <svg width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><path d="M11 19a8 8 0 1 0 0-16 8 8 0 0 0 0 16zM21 21l-4.35-4.35"/></svg>
         </div>
         <h2>Single App Explorer</h2>
-        <p>Deep-dive into any Play Store app. Extract hundreds of reviews, analyze ratings, and export clean data instantly.</p>
-        <div class="btn">
-          Explore Now
-          <svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="3"><path d="M5 12h14M12 5l7 7-7 7"/></svg>
-        </div>
+        <p>Deep-dive into any Play Store app. Extract hundreds of reviews, analyze ratings, and chat with AI to get instant insights.</p>
+        <div class="btn">Explore Now <svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="3"><path d="M5 12h14M12 5l7 7-7 7"/></svg></div>
       </div>
-
-      <!-- Batch Intelligence Card -->
       <div class="card" onclick="location.href='/batch'">
         <div class="badge active">New Mode</div>
         <div class="card-icon">
           <svg width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><rect x="3" y="3" width="7" height="7"/><rect x="14" y="3" width="7" height="7"/><rect x="14" y="14" width="7" height="7"/><rect x="3" y="14" width="7" height="7"/></svg>
         </div>
         <h2>Batch Intelligence</h2>
-        <p>Compare multiple apps side-by-side. Track competitor updates and aggregate sentiment across entire categories.</p>
-        <div class="btn">
-          Start Analysis
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+
+    function toggleChat(){document.getElementById('chat-window').classList.toggle('open');}
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+      document.getElementById('chat-messages').innerHTML=`<div class="msg-row bot"><div class="message bot">Chat cleared!</div></div>`;
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+      appendUserMsg(msg);input.value='';
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+  </script>
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-</html>
+</html>

utils/agents.py

@@ -0,0 +1,1553 @@
+#!/usr/bin/env python3
+"""
+╔══════════════════════════════════════════════════════════════════════════════╗
+║       REVIEW INTELLIGENCE AGENT  v4  —  LangGraph + LangChain               ║
+║  BERT sentiment · merged tools · app-aware · Pinecone native embed           ║
+║  Code-agent tool · scope guard · LLM fallback chain                          ║
+╚══════════════════════════════════════════════════════════════════════════════╝
+
+CHANGES FROM v3:
+  ① NLP  — DistilBERT (transformers) replaces LLM batched sentiment.
+            Zero token cost. Runs locally. Falls back to rating-heuristic.
+  ② Tools — get_top_negative / get_top_positive merged into one tool
+            get_reviews_by_rating(min_stars, max_stars, n, app_name?).
+            All tools accept optional app_name to handle multi-app CSVs.
+  ③ Pinecone — uses pc.create_index_for_model() + idx.upsert_records()
+            (Pinecone integrated embedding, no OpenAI dependency).
+            Query uses pc.inference.embed() for the query vector.
+  ④ Code-Agent — new @tool run_pandas_code(code) that POSTs to a
+            deployed HuggingFace Space (docker) for heavy pandas/stats.
+  ⑤ Scope guard — planner tracks iteration count; terminates cleanly
+            after MAX_PLANNER_ITERATIONS to avoid infinite loops.
+
+Install:
+  pip install langgraph langchain langchain-core langchain-openai \
+              langchain-groq pinecone pandas transformers torch \
+              rich python-dotenv requests
+
+Usage:
+  python review_agent_v4.py --csv reviews.csv
+  python review_agent_v4.py --csv reviews.csv \\
+      --query "Which action game has the most ad complaints?"
+  python review_agent_v4.py --csv reviews.csv --use-pinecone \\
+      --query "Show crash issues for com.JindoBlu app"
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import logging
+import operator
+import os
+import re
+import sys
+import time
+import textwrap
+import uuid
+from datetime import datetime
+from pathlib import Path
+from typing import Annotated, Any, Optional, TypedDict
+
+import pandas as pd
+import requests
+
+# ── LangGraph / LangChain ──────────────────────────────────────────────────
+from langgraph.graph import StateGraph, START, END
+from langchain_core.messages import HumanMessage, AIMessage, BaseMessage, SystemMessage
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.tools import tool
+
+try:
+    from langchain_groq import ChatGroq
+    HAS_GROQ = True
+except ImportError:
+    HAS_GROQ = False
+
+try:
+    from langchain_openai import ChatOpenAI
+    HAS_OPENAI_PKG = True
+except ImportError:
+    HAS_OPENAI_PKG = False
+
+# ── Pinecone (native SDK only — no langchain-pinecone needed) ──────────────
+try:
+    from pinecone import Pinecone as PineconeClient, ServerlessSpec
+    from pinecone import CloudProvider, AwsRegion, EmbedModel, IndexEmbed
+    HAS_PINECONE = True
+except ImportError:
+    HAS_PINECONE = False
+
+# ── BERT sentiment (local, zero token cost) ────────────────────────────────
+try:
+    from transformers import pipeline as hf_pipeline
+    HAS_TRANSFORMERS = True
+except ImportError:
+    HAS_TRANSFORMERS = False
+
+# ── Rich terminal ──────────────────────────────────────────────────────────
+try:
+    from rich.console import Console
+    from rich.panel import Panel
+    from rich.table import Table
+    from rich import box
+    RICH = True
+    console = Console()
+except ImportError:
+    RICH = False
+    console = None  # type: ignore
+
+logging.basicConfig(level=logging.WARNING)
+
+# ══════════════════════════════════════════════════════════════════════════════
+#  CONSTANTS
+# ══════════════════════════════════════════════════════════════════════════════
+
+# LLM providers
+GROQ_MODEL        = "llama-3.3-70b-versatile"
+OPENROUTER_URL    = "https://openrouter.ai/api/v1"
+OPENROUTER_MODEL  = "meta-llama/llama-3.3-70b-instruct"
+NVIDIA_URL        = "https://integrate.api.nvidia.com/v1"
+NVIDIA_MODEL      = "meta/llama-3.3-70b-instruct"
+
+# BERT model — lightweight distilled, ~67M params, runs on CPU
+BERT_SENTIMENT_MODEL = "distilbert-base-uncased-finetuned-sst-2-english"
+BERT_BATCH_SIZE      = 64       # reviews per inference batch
+MAX_BERT_TEXT_LEN    = 512      # chars; BERT max tokens ≈ 512 subwords
+
+# Pipeline limits
+SAMPLE_ROWS_FOR_SCHEMA   = 5
+MAX_REVIEWS_NLP          = 5_000   # BERT is fast; can handle more
+MAX_CLUSTERS             = 10
+MAX_PLANNER_ITERATIONS   = 4       # ④ scope guard: stop after N iterations
+ANOMALY_SIGMA            = 2.0
+
+# Pinecone integrated embedding
+PINECONE_EMBED_MODEL  = "multilingual-e5-large"  # free on Pinecone starter
+PINECONE_INDEX_NAME   = os.getenv("PINECONE_INDEX", "review-agent-v4")
+PINECONE_NAMESPACE    = "reviews"
+PINECONE_TOP_K        = 6
+PINECONE_UPSERT_BATCH = 96      # upsert_records batch size
+
+# Code-agent HuggingFace Space endpoint
+# Deploy the companion space (see code_agent_space/ folder) and set this env var
+HF_CODE_AGENT_URL = os.getenv(
+    "HF_CODE_AGENT_URL",
+    "https://YOUR-HF-USERNAME-review-code-agent.hf.space/run"
+)
+
+
+# ══════════════════════════════════════════════════════════════════════════════
+#  ①  LLM PROVIDER CHAIN  — Groq → OpenRouter → NVIDIA NIMs
+# ══════════════════════════════════════════════════════════════════════════════
+
+def _make_groq():
+    key = os.getenv("GROQ_API_KEY", "")
+    if not key or not HAS_GROQ:
+        return None
+    return ChatGroq(model=GROQ_MODEL, temperature=0, max_retries=1, api_key=key)
+
+
+def _make_openrouter():
+    key = os.getenv("OPENROUTER_API_KEY", "")
+    if not key or not HAS_OPENAI_PKG:
+        return None
+    return ChatOpenAI(
+        model=OPENROUTER_MODEL, temperature=0, max_retries=1, api_key=key,
+        base_url=OPENROUTER_URL,
+        default_headers={"HTTP-Referer": "review-agent-v4"},
+    )
+
+
+def _make_nvidia():
+    key = os.getenv("NVIDIA_API_KEY", "")
+    if not key or not HAS_OPENAI_PKG:
+        return None
+    return ChatOpenAI(
+        model=NVIDIA_MODEL, temperature=0, max_retries=1,
+        api_key=key, base_url=NVIDIA_URL,
+    )
+
+
+def build_llm():
+    """Return LLM chain with .with_fallbacks() or None."""
+    providers = [p for p in [_make_groq(), _make_openrouter(), _make_nvidia()]
+                 if p is not None]
+    if not providers:
+        return None
+    primary   = providers[0]
+    fallbacks = providers[1:]
+    return primary.with_fallbacks(fallbacks) if fallbacks else primary
+
+
+LLM = build_llm()
+_PROVIDER_TAG = ("Groq→OpenRouter→NVIDIA" if LLM else "NO LLM — heuristic only")
+
+
+# ══════════════════════════════════════════════════════════════════════════════
+#  ①  BERT SENTIMENT ANALYSER  (local, zero cost)
+# ══════════════════════════════════════════════════════════════════════════════
+
+_bert_pipe = None   # lazy-loaded
+
+def _load_bert():
+    global _bert_pipe
+    if _bert_pipe is None and HAS_TRANSFORMERS:
+        _log("Loading DistilBERT sentiment model (first run may download ~270 MB)…", "info")
+        _bert_pipe = hf_pipeline(
+            "sentiment-analysis",
+            model=BERT_SENTIMENT_MODEL,
+            truncation=True,
+            max_length=512,
+            device=-1,          # CPU; set device=0 for GPU
+            batch_size=BERT_BATCH_SIZE,
+        )
+        _log("DistilBERT loaded ✓", "ok")
+    return _bert_pipe
+
+
+def bert_sentiment(texts: list[str]) -> dict[str, Any]:
+    """
+    Run DistilBERT sentiment on a list of texts.
+    Returns counts + themes extracted purely from rating distribution (no LLM).
+    Falls back to None if transformers not installed.
+    """
+    pipe = _load_bert()
+    if pipe is None:
+        return {}
+
+    # Truncate texts for BERT
+    clean = [t[:MAX_BERT_TEXT_LEN] for t in texts if t.strip()]
+    if not clean:
+        return {}
+
+    pos = neg = 0
+    for i in range(0, len(clean), BERT_BATCH_SIZE):
+        batch = clean[i:i + BERT_BATCH_SIZE]
+        try:
+            results = pipe(batch)
+            for r in results:
+                if r["label"] == "POSITIVE":
+                    pos += 1
+                else:
+                    neg += 1
+        except Exception as e:
+            _log(f"BERT batch error: {e}", "warn")
+
+    total = max(1, pos + neg)
+    neu   = 0   # DistilBERT SST-2 is binary; neutral is inferred below
+    pct_pos = round(pos / total * 100, 1)
+    pct_neg = round(neg / total * 100, 1)
+
+    # Heuristic tone
+    if pct_pos >= 60:       tone = "Positive"
+    elif pct_neg >= 50:     tone = "Negative"
+    elif abs(pct_pos - pct_neg) < 20: tone = "Polarised"
+    else:                   tone = "Mixed"
+
+    return {
+        "pct_positive": pct_pos,
+        "pct_neutral":  round(100 - pct_pos - pct_neg, 1),
+        "pct_negative": pct_neg,
+        "overall_tone": tone,
+        "method":       "DistilBERT (local)",
+        "themes":       [],   # populated by LLM cluster node
+        "phrases":      [],
+    }
+
+
+# ══════════════════════════════════════════════════════════════════════════════
+#  GRAPH STATE
+# ══════════════════════════════════════════════════════════════════════════════
+
+class ReviewState(TypedDict, total=False):
+    # Inputs
+    filepath:       str
+    user_query:     str
+    use_pinecone:   bool
+
+    # Data
+    raw_df:         Any               # pd.DataFrame
+    total_rows:     int
+    columns:        list[str]
+    schema:         dict[str, Optional[str]]
+    schema_confidence: str
+    detected_apps:  list[str]         # unique app names found in data
+
+    # Analysis
+    stats:          dict[str, Any]
+    app_breakdown:  list[dict]
+    sentiment:      dict[str, Any]
+    clusters:       list[dict]
+    sample_texts:   list[str]
+
+    # Planner
+    tool_results:   list[dict]
+    planner_notes:  str
+    planner_iter:   int               # ④ scope guard counter
+
+    # RAG
+    rag_context:    str
+
+    # Report
+    report:         dict[str, Any]
+
+    # Message trace (reducer = append)
+    messages:       Annotated[list[BaseMessage], operator.add]
+
+    # Meta
+    errors:         list[str]
+    run_id:         str
+
+
+def _empty_state(filepath: str, query: str, use_pinecone: bool) -> ReviewState:
+    return ReviewState(
+        filepath=filepath, user_query=query, use_pinecone=use_pinecone,
+        raw_df=None, total_rows=0, columns=[], schema={},
+        schema_confidence="", detected_apps=[],
+        stats={}, app_breakdown=[], sentiment={}, clusters=[],
+        sample_texts=[], tool_results=[], planner_notes="",
+        planner_iter=0, rag_context="", report={},
+        messages=[], errors=[], run_id=f"run_{int(time.time())}",
+    )
+
+
+# ══════════════════════════════════════════════════════════════════════════════
+#  TOOL CLOSURE STATE  (set before planner runs)
+# ══════════════════════════════════════════════════════════════════════════════
+
+_ACTIVE: dict[str, Any] = {}
+
+def _df() -> Optional[pd.DataFrame]:  return _ACTIVE.get("raw_df")
+def _schema() -> dict:                return _ACTIVE.get("schema", {})
+def _apps() -> list[str]:             return _ACTIVE.get("detected_apps", [])
+
+
+# ══════════════════════════════════════════════════════════════════════════════
+#  ②  TOOLS  — merged, app-aware
+# ══════════════════════════════════════════════════════════════════════════════
+
+def _base_df(app_name: Optional[str] = None) -> Optional[pd.DataFrame]:
+    """Return DataFrame, optionally filtered to a specific app."""
+    df = _df()
+    if df is None:
+        return None
+    ac = _schema().get("app")
+    if app_name and ac:
+        mask = df[ac].astype(str).str.lower().str.contains(
+            re.escape(app_name.lower()), na=False)
+        return df[mask].copy()
+    return df.copy()
+
+
+@tool
+def get_reviews_by_rating(
+    min_stars: int = 1,
+    max_stars: int = 5,
+    n: int = 15,
+    app_name: Optional[str] = None,
+) -> str:
+    """
+    Retrieve reviews filtered by star rating range [min_stars, max_stars].
+    Replaces separate positive/negative tools.
+
+    Examples:
+      get_reviews_by_rating(1, 2)           — all 1-2 star reviews
+      get_reviews_by_rating(4, 5, n=20)     — top positive reviews
+      get_reviews_by_rating(1, 1, app_name="Challenge")  — 1-star for one app
+
+    Use this for any star-based filtering. It understands that ratings 1-2
+    are negative, 3 is neutral, and 4-5 are positive.
+    """
+    df = _base_df(app_name)
+    s  = _schema()
+    rc = s.get("rating"); tc = s.get("text")
+    if df is None or not rc or not tc:
+        return json.dumps({"error": "No rating or text column."})
+
+    df["__r"] = pd.to_numeric(df[rc], errors="coerce")
+    mask = (df["__r"] >= min_stars) & (df["__r"] <= max_stars)
+    filtered = df[mask].dropna(subset=["__r"])
+
+    cols = {tc: "text", rc: "rating"}
+    ac = s.get("app")
+    if ac: cols[ac] = "app"
+    uc = s.get("user")
+    if uc: cols[uc] = "user"
+
+    rows = (filtered[list(cols.keys())]
+            .rename(columns=cols)
+            .head(n)
+            .to_dict("records"))
+    return json.dumps({
+        "filter": f"{min_stars}–{max_stars} stars",
+        "app_filter": app_name or "all",
+        "count_matched": int(mask.sum()),
+        "returned": len(rows),
+        "reviews": rows,
+    }, ensure_ascii=False)
+
+
+@tool
+def get_most_helpful_reviews(
+    n: int = 10,
+    app_name: Optional[str] = None,
+) -> str:
+    """
+    Return the reviews with the most helpful/thumbs-up votes.
+    Optionally filtered to a specific app. Useful for finding highly-validated complaints.
+    """
+    df = _base_df(app_name)
+    s  = _schema()
+    hc = s.get("helpful"); tc = s.get("text"); rc = s.get("rating")
+    if df is None or not hc or not tc:
+        return json.dumps({"error": "No helpful column found."})
+
+    df["__h"] = pd.to_numeric(df[hc], errors="coerce").fillna(0)
+    cols = {tc: "text", hc: "helpful_votes"}
+    if rc: cols[rc] = "rating"
+    ac = s.get("app")
+    if ac: cols[ac] = "app"
+
+    rows = (df.nlargest(n, "__h")[list(cols.keys())]
+             .rename(columns=cols)
+             .to_dict("records"))
+    return json.dumps({
+        "app_filter": app_name or "all",
+        "returned": len(rows),
+        "reviews": rows,
+    }, ensure_ascii=False)
+
+
+@tool
+def get_rating_timeseries(app_name: Optional[str] = None) -> str:
+    """
+    Return daily average rating over time [{date, avg_rating, count}].
+    Pass app_name to see trends for a specific app only.
+    Useful for spotting when ratings dropped after an update.
+    """
+    df = _base_df(app_name)
+    s  = _schema()
+    dc = s.get("date"); rc = s.get("rating")
+    if df is None or not dc or not rc:
+        return json.dumps({"error": "No date or rating column."})
+
+    df["__d"] = pd.to_datetime(df[dc], errors="coerce")
+    df["__r"] = pd.to_numeric(df[rc], errors="coerce")
+    df = df.dropna(subset=["__d", "__r"])
+    grp = df.groupby(df["__d"].dt.date)["__r"]
+    daily = pd.DataFrame({
+        "date":       grp.mean().index.astype(str),
+        "avg_rating": grp.mean().values.round(2),
+        "count":      grp.count().values,
+    })
+    return json.dumps({
+        "app_filter": app_name or "all",
+        "rows": daily.to_dict("records"),
+    }, ensure_ascii=False)
+
+
+@tool
+def search_reviews_by_keyword(
+    keyword: str,
+    max_results: int = 20,
+    app_name: Optional[str] = None,
+) -> str:
+    """
+    Full-text search reviews for a keyword or phrase.
+    Optionally filter to a specific app.
+    Use this to investigate specific topics: 'crash', 'ads', 'login', etc.
+    """
+    df = _base_df(app_name)
+    s  = _schema()
+    tc = s.get("text"); rc = s.get("rating")
+    if df is None or not tc:
+        return json.dumps({"error": "No text column."})
+
+    mask = df[tc].astype(str).str.lower().str.contains(
+        re.escape(keyword.lower()), na=False, regex=True)
+    cols = {tc: "text"}
+    if rc: cols[rc] = "rating"
+    ac = s.get("app")
+    if ac: cols[ac] = "app"
+
+    rows = (df[mask][list(cols.keys())]
+            .rename(columns=cols)
+            .head(max_results)
+            .to_dict("records"))
+    return json.dumps({
+        "keyword": keyword,
+        "app_filter": app_name or "all",
+        "count_matched": int(mask.sum()),
+        "returned": len(rows),
+        "reviews": rows,
+    }, ensure_ascii=False)
+
+
+@tool
+def get_app_comparison(metric: str = "avg_rating") -> str:
+    """
+    Compare all detected apps by a metric: avg_rating, pct_negative,
+    pct_positive, count, or helpful_votes.
+    Use when the CSV has multiple apps and you need to rank them.
+    """
+    breakdown = _ACTIVE.get("app_breakdown", [])
+    if not breakdown:
+        return json.dumps({"error": "No app breakdown computed yet."})
+    valid_metrics = {"avg_rating","pct_negative","pct_positive","count","helpful_votes"}
+    m = metric if metric in valid_metrics else "avg_rating"
+    ranked = sorted(breakdown, key=lambda x: x.get(m) or 0, reverse=(m != "pct_negative"))
+    return json.dumps({
+        "metric": m,
+        "ranking": [
+            {"rank": i+1, "app": r["app"], m: r.get(m)}
+            for i, r in enumerate(ranked)
+        ],
+    }, ensure_ascii=False)
+
+
+@tool
+def get_rating_distribution(app_name: Optional[str] = None) -> str:
+    """
+    Return star count breakdown {1: N, 2: N, 3: N, 4: N, 5: N}.
+    Optionally for a single app.
+    """
+    df = _base_df(app_name)
+    s  = _schema(); rc = s.get("rating")
+    if df is None or not rc:
+        return json.dumps({"error": "No rating column."})
+    df["__r"] = pd.to_numeric(df[rc], errors="coerce")
+    dist = df["__r"].value_counts().sort_index()
+    return json.dumps({
+        "app_filter": app_name or "all",
+        "distribution": {str(int(k)): int(v) for k, v in dist.items() if not pd.isna(k)},
+    }, ensure_ascii=False)
+
+
+# ══════════════════════════════════════════════════════════════════════════════
+#  ③  CODE-AGENT TOOL  — runs pandas/stats code on HuggingFace Space
+# ══════════════════════════════════════════════════════════════════════════════
+
+@tool
+def run_pandas_code(code: str) -> str:
+    """
+    Execute arbitrary pandas / statistics Python code on the review DataFrame
+    hosted in a HuggingFace Space code-execution endpoint.
+
+    Use this when you need:
+    - custom aggregations not covered by other tools
+    - statistical tests (e.g. t-test between two apps' ratings)
+    - pivot tables, cross-tabs, rolling averages
+    - complex filtering with multiple conditions
+
+    The remote environment has: pandas, numpy, scipy, statsmodels.
+    The DataFrame is available as `df` with the current schema columns.
+    The result must be assigned to a variable named `result`.
+
+    Example code:
+        import scipy.stats as stats
+        g1 = df[df['app']=='AppA']['score'].dropna()
+        g2 = df[df['app']=='AppB']['score'].dropna()
+        t, p = stats.ttest_ind(g1, g2)
+        result = {'t_stat': round(float(t),4), 'p_value': round(float(p),4)}
+    """
+    url = HF_CODE_AGENT_URL.rstrip("/") + "/execute"
+    if "YOUR-HF-USERNAME" in url:
+        return json.dumps({
+            "error": "HF_CODE_AGENT_URL not configured. "
+                     "Set env var HF_CODE_AGENT_URL to your deployed HuggingFace Space URL.",
+            "hint": "See code_agent_space/app.py in this repo for the companion Space.",
+        })
+
+    # Send the CSV as base64 + code to the Space
+    df = _df()
+    if df is None:
+        return json.dumps({"error": "No dataframe loaded."})
+
+    import base64, io
+    buf = io.StringIO()
+    df.to_csv(buf, index=False)
+    csv_b64 = base64.b64encode(buf.getvalue().encode()).decode()
+
+    try:
+        resp = requests.post(
+            url,
+            json={"csv_b64": csv_b64, "code": code, "schema": _schema()},
+            timeout=30,
+        )
+        resp.raise_for_status()
+        return resp.text
+    except requests.exceptions.ConnectionError:
+        return json.dumps({"error": f"Cannot reach HF Space at {url}. Is it deployed?"})
+    except requests.exceptions.Timeout:
+        return json.dumps({"error": "Code agent timed out (>30s)."})
+    except Exception as e:
+        return json.dumps({"error": str(e)})
+
+
+# All tools list — used by planner
+TOOLS = [
+    get_reviews_by_rating,
+    get_most_helpful_reviews,
+    get_rating_timeseries,
+    search_reviews_by_keyword,
+    get_app_comparison,
+    get_rating_distribution,
+    run_pandas_code,
+]
+
+TOOL_MAP = {t.name: t for t in TOOLS}
+
+TOOL_DESCRIPTIONS = """
+- get_reviews_by_rating(min_stars, max_stars, n, app_name?) — filter by star range 1-5. Use instead of separate positive/negative tools.
+- get_most_helpful_reviews(n, app_name?) — most upvoted reviews
+- get_rating_timeseries(app_name?) — daily avg rating trend
+- search_reviews_by_keyword(keyword, max_results, app_name?) — full-text search
+- get_app_comparison(metric) — rank all apps by metric (avg_rating|pct_negative|count…)
+- get_rating_distribution(app_name?) — star count breakdown
+- run_pandas_code(code) — custom pandas/scipy code on HuggingFace Space for complex stats
+"""
+
+
+# ══════════════════════════════════════════════════════════════════════════════
+#  LLM HELPER
+# ══════════════════════════════════════════════════════════════════════════════
+
+def _llm_json(system: str, user: str, fallback_fn=None) -> dict | list:
+    if LLM is None:
+        return fallback_fn() if fallback_fn else {}
+    prompt = ChatPromptTemplate.from_messages([
+        SystemMessage(content=system),
+        ("human", "{u}")
+    ])
+    chain  = prompt | LLM
+    try:
+        resp = chain.invoke({"u": user})
+        raw  = getattr(resp, "content", str(resp)).strip()
+        raw  = re.sub(r"^```(?:json)?\s*", "", raw)
+        raw  = re.sub(r"\s*```$", "", raw)
+        return json.loads(raw)
+    except Exception as e:
+        _log(f"LLM JSON parse failed: {e}", "warn")
+        return fallback_fn() if fallback_fn else {}
+
+
+# ══════════════════════════════════════════════════════════════════════════════
+#  NODE 1 — INGESTION
+# ══════════════════════════════════════════════════════════════════════════════
+
+def node_ingest(state: ReviewState) -> dict:
+    _log("Node [ingest]", "agent")
+    
+    if state.get("raw_df") is not None:
+        df = state["raw_df"]
+        _log(f"Using provided DataFrame: {len(df):,} rows", "ok")
+        return {
+            "total_rows": len(df),
+            "columns": list(df.columns),
+            "messages": [HumanMessage(content=f"Using provided data with {len(df)} reviews")],
+        }
+
+    fp  = state["filepath"]
+    if not fp:
+        return {"errors": ["ingest: no filepath or dataframe provided"]}
+        
+    ext = Path(fp).suffix.lower()
+    try:
+        if ext in (".xls", ".xlsx", ".xlsm"):
+            df = pd.read_excel(fp)
+        else:
+            for enc in ("utf-8", "utf-8-sig", "latin-1", "cp1252"):
+                try:
+                    df = pd.read_csv(fp, encoding=enc); break
+                except Exception: continue
+            else:
+                raise ValueError(f"Cannot decode {fp}")
+        df = df.dropna(how="all")
+        _log(f"Loaded {len(df):,} rows × {len(df.columns)} cols", "ok")
+        return {
+            "raw_df": df, "total_rows": len(df),
+            "columns": list(df.columns),
+            "messages": [HumanMessage(content=f"Loaded {len(df)} reviews")],
+        }
+    except Exception as e:
+        return {"errors": [f"ingest: {e}"],
+                "messages": [HumanMessage(content=f"ERROR loading: {e}")]}
+
+
+# ══════════════════════════════════════════════════════════════════════════════
+#  NODE 2 — LLM SCHEMA DETECTION
+# ══════════════════════════════════════════════════════════════════════════════
+
+_SCHEMA_SYS = """You are a CSV schema analyst.
+Given column names and sample rows from a user-review dataset, map each role to the exact column name.
+Return ONLY valid JSON (no markdown):
+{{
+  "text":    "<review text column or null>",
+  "rating":  "<numeric star/score column or null>",
+  "date":    "<date/timestamp column or null>",
+  "app":     "<app/product/game identifier column or null>",
+  "user":    "<reviewer name/id column or null>",
+  "helpful": "<helpful/upvote count column or null>",
+  "confidence": "high|medium|low",
+  "reasoning":  "one sentence"
+}}
+Use EXACT column names. null if uncertain."""
+
+
+def node_schema(state: ReviewState) -> dict:
+    _log("Node [schema]", "agent")
+    df = state.get("raw_df")
+    if df is None:
+        return {"errors": ["schema: no dataframe"]}
+
+    sample = df.head(SAMPLE_ROWS_FOR_SCHEMA).to_dict(orient="records")
+    result = _llm_json(
+        _SCHEMA_SYS,
+        json.dumps({"columns": state["columns"], "sample_rows": sample}),
+        fallback_fn=lambda: _heuristic_schema(state["columns"]),
+    )
+    schema = {k: result.get(k) for k in ["text","rating","date","app","user","helpful"]}
+    conf   = result.get("confidence","low")
+    _log(f"Schema ({conf}): {schema}", "ok")
+
+    # Detect unique apps so tools can be app-aware
+    apps: list[str] = []
+    ac = schema.get("app")
+    if ac and df is not None and ac in df.columns:
+        apps = sorted(df[ac].dropna().astype(str).unique().tolist())
+        _log(f"Detected {len(apps)} apps: {apps[:5]}", "info")
+
+    return {
+        "schema": schema, "schema_confidence": conf,
+        "detected_apps": apps,
+        "messages": [AIMessage(content=f"Schema ({conf}): {schema}. Apps: {apps[:5]}")],
+    }
+
+
+def _heuristic_schema(cols: list[str]) -> dict:
+    low = {c.lower(): c for c in cols}
+    def first(pats):
+        for p in pats:
+            for cl, c in low.items():
+                if p in cl: return c
+        return None
+    return {
+        "text":    first(["content","review","text","body","comment"]),
+        "rating":  first(["score","rating","stars","rate","grade"]),
+        "date":    first(["date","time","created","posted","at"]),
+        "app":     first(["app","product","game","title","name"]),
+        "user":    first(["user","reviewer","author"]),
+        "helpful": first(["helpful","thumbs","vote","like","useful"]),
+        "confidence": "low", "reasoning": "heuristic fallback",
+    }
+
+
+# ══════════════════════════════════════════════════════════════════════════════
+#  NODE 3 — STATISTICAL ANALYSIS
+# ══════════════════════════════════════════════════════════════════════════════
+
+def node_stats(state: ReviewState) -> dict:
+    _log("Node [stats]", "agent")
+    df = state.get("raw_df"); s = state.get("schema", {})
+    rc = s.get("rating"); tc = s.get("text")
+    if df is None or not rc:
+        return {"stats": {}}
+
+    df2 = df.copy()
+    df2["__r"] = pd.to_numeric(df2[rc], errors="coerce")
+    valid = df2["__r"].dropna()
+
+    stats: dict[str, Any] = {
+        "total_reviews":  int(len(df2)),
+        "rated_reviews":  int(len(valid)),
+        "avg_rating":     round(float(valid.mean()), 3) if len(valid) else None,
+        "median_rating":  float(valid.median())         if len(valid) else None,
+        "std_rating":     round(float(valid.std()),  3) if len(valid) else None,
+        "pct_positive":   round(float((valid >= 4).mean() * 100), 1),
+        "pct_negative":   round(float((valid <= 2).mean() * 100), 1),
+        "pct_neutral":    round(float(((valid > 2) & (valid < 4)).mean() * 100), 1),
+        "rating_distribution": {
+            str(int(k)): int(v)
+            for k, v in df2["__r"].value_counts().sort_index().items()
+            if not pd.isna(k)
+        },
+    }
+
+    if tc:
+        lens = df2[tc].dropna().astype(str).str.len()
+        stats["avg_review_length"] = int(lens.mean())
+        stats["short_reviews_pct"] = round(float((lens < 20).mean() * 100), 1)
+
+    hc = s.get("helpful")
+    if hc:
+        df2["__h"] = pd.to_numeric(df2[hc], errors="coerce").fillna(0)
+        stats["total_helpful_votes"] = int(df2["__h"].sum())
+
+    dc = s.get("date")
+    if dc:
+        try:
+            df2["__d"] = pd.to_datetime(df2[dc], errors="coerce")
+            daily = df2.dropna(subset=["__d","__r"]).groupby(df2["__d"].dt.date)["__r"].mean()
+            if len(daily) > 7:
+                m, std = daily.mean(), daily.std()
+                bad = daily[daily < (m - ANOMALY_SIGMA * std)]
+                stats["anomaly_days"] = [
+                    {"date": str(d), "avg_rating": round(float(v), 2)}
+                    for d, v in bad.items()
+                ]
+        except Exception:
+            pass
+
+    # Per-app breakdown
+    app_rows = []
+    ac = s.get("app")
+    if ac:
+        for name, grp in df2.groupby(ac):
+            gr = pd.to_numeric(grp["__r"], errors="coerce")
+            row: dict[str, Any] = {
+                "app": str(name), "count": int(len(grp)),
+                "avg_rating":   round(float(gr.mean()), 2) if len(gr) else None,
+                "pct_negative": round(float((gr <= 2).mean() * 100), 1),
+                "pct_positive": round(float((gr >= 4).mean() * 100), 1),
+            }
+            if hc:
+                row["helpful_votes"] = int(
+                    pd.to_numeric(grp[hc], errors="coerce").fillna(0).sum())
+            app_rows.append(row)
+        app_rows.sort(key=lambda x: x.get("avg_rating") or 5)
+        stats["apps_analyzed"] = len(app_rows)
+
+    _log(f"Stats: avg={stats.get('avg_rating')} neg={stats.get('pct_negative')}%", "ok")
+    return {
+        "stats": stats, "app_breakdown": app_rows,
+        "messages": [AIMessage(content=(
+            f"Stats: {stats['total_reviews']} reviews, avg {stats.get('avg_rating')}/5"))],
+    }
+
+
+# ══════════════════════════════════════════════════════════════════════════════
+#  NODE 4 — ①  BERT SENTIMENT  (local, zero token cost)
+# ══════════════════════════════════════════════════════════════════════════════
+
+def node_nlp(state: ReviewState) -> dict:
+    _log("Node [nlp/BERT]", "agent")
+    df = state.get("raw_df"); s = state.get("schema", {})
+    tc = s.get("text")
+    if df is None or not tc:
+        return {"sentiment": _rating_sentiment_fallback(state)}
+
+    # Sample
+    sample_df = df.sample(min(MAX_REVIEWS_NLP, len(df)), random_state=42)
+    texts     = sample_df[tc].fillna("").astype(str).tolist()
+
+    # ① Try BERT first (free, local)
+    bert_result = bert_sentiment(texts)
+    if bert_result:
+        _log(f"BERT sentiment: pos={bert_result['pct_positive']}% "
+             f"neg={bert_result['pct_negative']}% tone={bert_result['overall_tone']}", "ok")
+        return {
+            "sentiment":    bert_result,
+            "sample_texts": texts[:500],
+            "messages":     [AIMessage(content=(
+                f"BERT sentiment: {bert_result['overall_tone']} "
+                f"(pos={bert_result['pct_positive']}% neg={bert_result['pct_negative']}%)"))],
+        }
+
+    # ② Fall back to rating-based heuristic (no transformers installed)
+    _log("BERT unavailable — using rating heuristic", "warn")
+    fallback = _rating_sentiment_fallback(state)
+    return {
+        "sentiment":    fallback,
+        "sample_texts": texts[:500],
+    }
+
+
+def _rating_sentiment_fallback(state: ReviewState) -> dict:
+    stats = state.get("stats", {})
+    return {
+        "pct_positive": stats.get("pct_positive", 0),
+        "pct_neutral":  stats.get("pct_neutral",  0),
+        "pct_negative": stats.get("pct_negative", 0),
+        "overall_tone": "Unknown",
+        "method":       "rating-heuristic (no BERT)",
+        "themes": [], "phrases": [],
+    }
+
+
+# ══════════════════════════════════════════════════════════════════════════════
+#  NODE 5 — LLM CLUSTERING  (topic discovery, not keyword matching)
+# ══════════════════════════════════════════════════════════════════════════════
+
+_CLUSTER_SYS = f"""You are a product issue analyst.
+Given a mixed sample of user reviews, discover distinct topic clusters.
+Return ONLY valid JSON:
+{{
+  "clusters": [
+    {{
+      "label":            "Short name",
+      "type":             "issue|praise|request|general",
+      "description":      "1-2 sentence summary",
+      "frequency_signal": "high|medium|low",
+      "severity":         "critical|high|medium|low",
+      "example_quote":    "verbatim short quote",
+      "keywords":         ["word1","word2"]
+    }}
+  ]
+}}
+Max {MAX_CLUSTERS} clusters. Merge near-duplicates. Issues first."""
+
+
+def node_cluster(state: ReviewState) -> dict:
+    _log("Node [cluster]", "agent")
+    df = state.get("raw_df"); s = state.get("schema", {})
+    rc = s.get("rating"); tc = s.get("text")
+    if df is None or not tc or LLM is None:
+        return {"clusters": []}
+
+    df2 = df.copy()
+    if rc:
+        df2["__r"] = pd.to_numeric(df2[rc], errors="coerce")
+        neg = df2.nsmallest(50, "__r")
+        pos = df2.nlargest(30, "__r")
+        rnd = df2.sample(min(50, len(df2)), random_state=7)
+        combined = pd.concat([neg, pos, rnd]).drop_duplicates(subset=[tc])
+    else:
+        combined = df2.sample(min(130, len(df2)), random_state=7)
+
+    sample = [{"text": str(r[tc])[:280], "rating": r.get(rc)}
+              for _, r in combined.iterrows()][:130]
+    result   = _llm_json(_CLUSTER_SYS, json.dumps(sample))
+    clusters = result.get("clusters", [])
+
+    _sev = {"critical":0,"high":1,"medium":2,"low":3}
+    _typ = {"issue":0,"request":1,"general":2,"praise":3}
+    clusters.sort(key=lambda c: (
+        _typ.get(c.get("type","general"),9),
+        _sev.get(c.get("severity","low"),9)))
+
+    _log(f"Discovered {len(clusters)} clusters", "ok")
+    return {
+        "clusters": clusters,
+        "messages": [AIMessage(content=f"Found {len(clusters)} topic clusters")],
+    }
+
+
+# ══════════════════════════════════════════════════════════════════════════════
+#  NODE 6 — ④  PLANNER (ReAct with scope guard)
+# ══════════════════════════════════════════════════════════════════════════════
+
+_PLANNER_SYS = """You are an analytical planner for a review intelligence system.
+A user asked a specific question. Decide which tools to call to best answer it.
+
+Available tools:
+{tools}
+
+Already computed context:
+{context}
+
+Detected apps in dataset: {apps}
+
+User query: {query}
+
+Rules:
+- Use get_reviews_by_rating(min_stars, max_stars) instead of separate positive/negative tools.
+- If multiple apps exist and the query targets one, pass app_name to every tool call.
+- Use run_pandas_code only for custom stats not covered by other tools.
+- Choose 1-3 tools maximum. Do not repeat a tool with identical arguments.
+
+Return ONLY valid JSON:
+{{
+  "reasoning": "1-2 sentences",
+  "calls": [{{"tool": "name", "args": {{...}}}}],
+  "done": false
+}}
+Set "done": true if the context already fully answers the query with no more tools needed.
+"""
+
+
+def node_planner(state: ReviewState) -> dict:
+    _log("Node [planner]", "agent")
+    query = state.get("user_query", "").strip()
+    itr   = state.get("planner_iter", 0)
+
+    # ④ Scope guard — terminate if iterations exceeded
+    if itr >= MAX_PLANNER_ITERATIONS:
+        _log(f"Planner reached max iterations ({MAX_PLANNER_ITERATIONS}) — terminating", "warn")
+        return {
+            "planner_notes": f"Terminated after {itr} iterations (scope guard).",
+            "planner_iter":  itr,
+            "messages": [AIMessage(content="Planner: max iterations reached, proceeding to report.")],
+        }
+
+    if not query or LLM is None:
+        return {"planner_notes": "No query or LLM unavailable.", "planner_iter": itr}
+
+    # Load active state for tool closures
+    _ACTIVE.update(state)
+
+    context = {
+        "total_reviews": state.get("stats",{}).get("total_reviews"),
+        "avg_rating":    state.get("stats",{}).get("avg_rating"),
+        "clusters":      [c.get("label") for c in state.get("clusters",[])[:5]],
+        "overall_tone":  state.get("sentiment",{}).get("overall_tone"),
+        "has_date":      bool(state.get("schema",{}).get("date")),
+        "has_helpful":   bool(state.get("schema",{}).get("helpful")),
+        "tools_called":  [t["tool"] for t in state.get("tool_results",[])],
+    }
+
+    plan = _llm_json(
+        _PLANNER_SYS.format(
+            tools=TOOL_DESCRIPTIONS,
+            context=json.dumps(context),
+            apps=state.get("detected_apps", [])[:10],
+            query=query,
+        ),
+        "",
+    )
+
+    reasoning = plan.get("reasoning", "")
+    done       = plan.get("done", False)
+    _log(f"Planner iter={itr+1}: {reasoning}", "info")
+
+    # ④ If planner says done, skip tool calls
+    if done:
+        return {
+            "planner_notes": reasoning,
+            "planner_iter":  itr + 1,
+            "messages": [AIMessage(content=f"Planner done: {reasoning}")],
+        }
+
+    tool_results = list(state.get("tool_results", []))
+    for call in plan.get("calls", []):
+        tname = call.get("tool","")
+        args  = call.get("args",{})
+        t = TOOL_MAP.get(tname)
+        if not t:
+            continue
+        _log(f"  Tool: {tname}({args})", "info")
+        try:
+            result = t.invoke(args)
+            tool_results.append({"tool": tname, "args": args, "result": result, "ok": True})
+        except Exception as e:
+            tool_results.append({"tool": tname, "args": args, "error": str(e), "ok": False})
+            _log(f"  Tool {tname} failed: {e}", "warn")
+
+    return {
+        "tool_results":  tool_results,
+        "planner_notes": reasoning,
+        "planner_iter":  itr + 1,
+        "messages": [AIMessage(content=f"Planner iter {itr+1}: {reasoning}")],
+    }
+
+
+# ══════════════════════════════════════════════════════════════════════════════
+#  NODE 7 — ③  PINECONE RAG  (native integrated embedding, no OpenAI needed)
+# ══════════════════════════════════════════════════════════════════════════════
+
+def node_rag(state: ReviewState) -> dict:
+    _log("Node [rag/pinecone]", "agent")
+    if not state.get("use_pinecone") or not HAS_PINECONE:
+        return {"rag_context": ""}
+
+    api_key = os.getenv("PINECONE_API_KEY", "")
+    if not api_key:
+        _log("PINECONE_API_KEY missing — skipping RAG", "warn")
+        return {"rag_context": ""}
+
+    try:
+        pc = PineconeClient(api_key=api_key)
+
+        # ③ Create index with Pinecone's integrated embedding model (no OpenAI)
+        existing = [idx.name for idx in pc.list_indexes()]
+        if PINECONE_INDEX_NAME not in existing:
+            _log(f"Creating Pinecone index '{PINECONE_INDEX_NAME}' with {PINECONE_EMBED_MODEL}…", "info")
+            pc.create_index_for_model(
+                name=PINECONE_INDEX_NAME,
+                cloud="aws",
+                region="us-east-1",
+                embed={
+                    "model":     PINECONE_EMBED_MODEL,
+                    "field_map": {"text": "chunk_text"},
+                },
+            )
+            time.sleep(3)   # wait for index to be ready
+
+        idx = pc.Index(PINECONE_INDEX_NAME)
+
+        # Upsert review sample as records — Pinecone auto-embeds chunk_text
+        texts = state.get("sample_texts", [])[:500]
+        if texts:
+            records = [
+                {"_id": f"rev_{i}_{state.get('run_id','')}", "chunk_text": t}
+                for i, t in enumerate(texts) if t.strip()
+            ]
+            # Batch upsert
+            for i in range(0, len(records), PINECONE_UPSERT_BATCH):
+                batch = records[i:i + PINECONE_UPSERT_BATCH]
+                idx.upsert_records(namespace=PINECONE_NAMESPACE, records=batch)
+            _log(f"Upserted {len(records)} reviews to Pinecone", "ok")
+
+        # ③ Query: use pc.inference.embed() for the query vector (Pinecone-native)
+        query = state.get("user_query") or "most common user complaints and praise"
+        q_embeddings = pc.inference.embed(
+            model=PINECONE_EMBED_MODEL,
+            inputs=[query],
+            parameters={"input_type": "query", "truncate": "END"},
+        )
+        q_vector = q_embeddings[0].values
+
+        results = idx.query(
+            namespace=PINECONE_NAMESPACE,
+            vector=q_vector,
+            top_k=PINECONE_TOP_K,
+            include_metadata=True,
+        )
+
+        matches = results.get("matches", [])
+        rag_context = "\n\n".join(
+            f"[Similar review {i+1} score={round(m.get('score',0),3)}]: "
+            f"{m.get('metadata',{}).get('chunk_text','')}"
+            for i, m in enumerate(matches)
+        )
+        _log(f"RAG: retrieved {len(matches)} similar reviews", "ok")
+        return {
+            "rag_context": rag_context,
+            "messages": [AIMessage(content=f"RAG: {len(matches)} similar reviews retrieved")],
+        }
+
+    except Exception as e:
+        _log(f"Pinecone RAG failed: {e}", "warn")
+        return {"rag_context": ""}
+
+
+# ══════════════════════════════════════════════════════════════════════════════
+#  NODE 8 — REPORT SYNTHESISER
+# ══════════════════════════════════════════════════════════════════════════════
+
+_REPORT_SYS = """You are a senior product intelligence analyst.
+Produce a structured insight report from pipeline data, tool results, and RAG context.
+User query: {query}
+RAG context: {rag}
+
+Return ONLY valid JSON:
+{{
+  "executive_summary": "2-3 sentences. Lead with direct answer if query present.",
+  "direct_answer":     "1-2 sentence direct answer, or null.",
+  "top_problems": [
+    {{"issue":"","description":"","severity":"critical|high|medium|low",
+      "frequency":"high|medium|low","evidence":""}}
+  ],
+  "key_strengths": [{{"strength":"","description":"","evidence":""}}],
+  "trend_observations": [{{"observation":"","detail":"","data_ref":""}}],
+  "anomalies": [{{"anomaly":"","type":"Spike|Pattern|Outlier|Trend",
+                  "detail":"","hypothesis":""}}],
+  "recommendations": [
+    {{"priority":"critical|high|medium|low","action":"",
+      "rationale":"","expected_impact":""}}
+  ],
+  "confidence_note": ""
+}}"""
+
+
+def node_report(state: ReviewState) -> dict:
+    _log("Node [report]", "agent")
+    payload = {
+        "stats":         state.get("stats",{}),
+        "sentiment":     state.get("sentiment",{}),
+        "clusters":      state.get("clusters",[])[:MAX_CLUSTERS],
+        "app_breakdown": state.get("app_breakdown",[])[:10],
+        "tool_results":  [
+            {"tool": t["tool"], "result": str(t.get("result",""))[:400]}
+            for t in state.get("tool_results",[]) if t.get("ok")
+        ],
+    }
+
+    if LLM is None:
+        return {"report": _heuristic_report(state)}
+
+    system = _REPORT_SYS.format(
+        query=state.get("user_query") or "(general analysis)",
+        rag=state.get("rag_context") or "None",
+    )
+    report = _llm_json(system, json.dumps(payload),
+                       fallback_fn=lambda: _heuristic_report(state))
+    _log("Report synthesised", "ok")
+    return {
+        "report":   report,
+        "messages": [AIMessage(content="Report synthesis complete.")],
+    }
+
+
+def _heuristic_report(state: ReviewState) -> dict:
+    clusters = state.get("clusters",[])
+    stats    = state.get("stats",{})
+    sent     = state.get("sentiment",{})
+    problems = [
+        {"issue": c.get("label","?"), "description": c.get("description",""),
+         "severity": c.get("severity","medium"), "frequency": c.get("frequency_signal","medium"),
+         "evidence": c.get("example_quote","")[:150]}
+        for c in clusters if c.get("type")=="issue"
+    ][:5]
+    return {
+        "executive_summary": (
+            f"Analysed {stats.get('total_reviews','?')} reviews. "
+            f"Avg rating {stats.get('avg_rating','?')}/5. "
+            f"Tone: {sent.get('overall_tone','?')} (method: {sent.get('method','?')})."),
+        "direct_answer": None,
+        "top_problems":  problems,
+        "key_strengths": [],
+        "trend_observations": [],
+        "anomalies": [
+            {"anomaly": f"Drop on {d['date']}", "type": "Spike",
+             "detail": f"Avg {d['avg_rating']}", "hypothesis": "Possible bad update"}
+            for d in stats.get("anomaly_days",[])[:3]
+        ],
+        "recommendations": [
+            {"priority": p["severity"], "action": f"Fix: {p['issue']}",
+             "rationale": p["description"], "expected_impact": "Better ratings"}
+            for p in problems[:4]
+        ],
+        "confidence_note": "Heuristic fallback — LLM unavailable.",
+    }
+
+
+# ══════════════════════════════════════════════════════════════════════════════
+#  CONDITIONAL EDGES
+# ══════════════════════════════════════════════════════════════════════════════
+
+def _route_after_stats(state: ReviewState) -> str:
+    return "nlp" if state.get("schema",{}).get("text") else "planner"
+
+def _route_after_cluster(state: ReviewState) -> str:
+    if state.get("user_query","").strip() and LLM is not None:
+        return "planner"
+    return "rag"
+
+def _route_after_planner(state: ReviewState) -> str:
+    # ④ Also route to report if scope guard triggered
+    itr  = state.get("planner_iter", 0)
+    done = itr >= MAX_PLANNER_ITERATIONS
+    if state.get("use_pinecone") and HAS_PINECONE and not done:
+        return "rag"
+    return "report"
+
+
+# ══════════════════════════════════════════════════════════════════════════════
+#  GRAPH ASSEMBLY
+# ══════════════════════════════════════════════════════════════════════════════
+
+def build_graph():
+    g = StateGraph(ReviewState)
+
+    g.add_node("ingest",  node_ingest)
+    g.add_node("schema",  node_schema)
+    g.add_node("stats",   node_stats)
+    g.add_node("nlp",     node_nlp)
+    g.add_node("cluster", node_cluster)
+    g.add_node("planner", node_planner)
+    g.add_node("rag",     node_rag)
+    g.add_node("report",  node_report)
+
+    g.add_edge(START,    "ingest")
+    g.add_edge("ingest", "schema")
+    g.add_edge("schema", "stats")
+
+    g.add_conditional_edges("stats",   _route_after_stats,
+                            {"nlp":"nlp","planner":"planner"})
+    g.add_edge("nlp", "cluster")
+
+    g.add_conditional_edges("cluster", _route_after_cluster,
+                            {"planner":"planner","rag":"rag"})
+
+    g.add_conditional_edges("planner", _route_after_planner,
+                            {"rag":"rag","report":"report"})
+
+    g.add_edge("rag",    "report")
+    g.add_edge("report", END)
+
+    return g.compile()
+
+
+# ══════════════════════════════════════════════════════════════════════════════
+#  RENDERER
+# ══════════════════════════════════════════════════════════════════════════════
+
+def render(state: ReviewState):
+    if RICH: _render_rich(state)
+    else:    _render_plain(state)
+
+
+def _render_rich(state: ReviewState):
+    stats    = state.get("stats",{})
+    sent     = state.get("sentiment",{})
+    clusters = state.get("clusters",[])
+    apps     = state.get("app_breakdown",[])
+    report   = state.get("report",{})
+    query    = state.get("user_query","")
+    tools    = state.get("tool_results",[])
+    itr      = state.get("planner_iter",0)
+
+    console.rule("[bold cyan]REVIEW INTELLIGENCE  v4  ·  LangGraph[/bold cyan]")
+
+    if query and report.get("direct_answer"):
+        console.print(Panel(
+            f"[bold yellow]Q:[/bold yellow] {query}\n\n"
+            f"[bold green]{report['direct_answer']}[/bold green]",
+            title="[bold]Direct Answer[/bold]", border_style="bright_green"))
+
+    console.print(Panel(
+        f"[italic]{report.get('executive_summary','')}[/italic]",
+        title="[bold]Executive Summary[/bold]", border_style="cyan"))
+
+    # Metrics
+    t = Table(box=box.SIMPLE, show_header=False)
+    t.add_column("", style="dim"); t.add_column("", style="bold")
+    method_tag = sent.get("method","")
+    for k, v in [
+        ("Total Reviews",  f"{stats.get('total_reviews','?'):,}"),
+        ("Avg Rating",     f"{stats.get('avg_rating','?')} / 5  σ={stats.get('std_rating','?')}"),
+        ("% Positive",     f"[green]{sent.get('pct_positive','?')}%[/green]"),
+        ("% Neutral",      f"[blue]{sent.get('pct_neutral','?')}%[/blue]"),
+        ("% Negative",     f"[red]{sent.get('pct_negative','?')}%[/red]"),
+        ("Tone",           str(sent.get("overall_tone","?"))),
+        ("Sentiment method", f"[dim]{method_tag}[/dim]"),
+        ("LLM Provider",   f"[dim]{_PROVIDER_TAG}[/dim]"),
+        ("Planner iters",  str(itr)),
+    ]:
+        t.add_row(k, v)
+    console.print(Panel(t, title="[bold]Key Metrics[/bold]", border_style="green"))
+
+    # Rating distribution bar
+    console.print("[bold]Rating Distribution[/bold]")
+    for star in [5,4,3,2,1]:
+        cnt   = stats.get("rating_distribution",{}).get(str(star), 0)
+        total = max(1, stats.get("total_reviews",1))
+        bar   = "█" * int(cnt/total*40)
+        color = "green" if star>=4 else "yellow" if star==3 else "red"
+        console.print(f"  {'★'*star:<5} [{color}]{bar:<40}[/{color}] {cnt:,}")
+    console.print()
+
+    # Detected apps
+    det_apps = state.get("detected_apps",[])
+    if det_apps:
+        console.print(Panel(
+            "  ".join(f"[cyan]{a}[/cyan]" for a in det_apps[:15]),
+            title=f"[bold]Detected Apps ({len(det_apps)})[/bold]",
+            border_style="dim"))
+        console.print()
+
+    # Clusters
+    if clusters:
+        t = Table(title="LLM-Discovered Clusters", box=box.ROUNDED, border_style="magenta")
+        t.add_column("Type",width=8); t.add_column("Label",style="bold")
+        t.add_column("Freq",width=7); t.add_column("Severity",width=10)
+        t.add_column("Description",style="dim")
+        for c in clusters[:10]:
+            typ = c.get("type","?")
+            tc_ = "green" if typ=="praise" else "red" if typ=="issue" else "blue"
+            sev = c.get("severity","?")
+            sc  = "red" if sev in("critical","high") else "yellow" if sev=="medium" else "blue"
+            t.add_row(f"[{tc_}]{typ}[/{tc_}]", c.get("label",""),
+                      c.get("frequency_signal",""), f"[{sc}]{sev}[/{sc}]",
+                      c.get("description","")[:80])
+        console.print(t); console.print()
+
+    # Top problems
+    if report.get("top_problems"):
+        t = Table(title="Top Problems", box=box.ROUNDED, border_style="red")
+        t.add_column("#",width=3,style="dim"); t.add_column("Issue",style="bold")
+        t.add_column("Severity",width=10); t.add_column("Evidence",style="dim")
+        for i, p in enumerate(report["top_problems"],1):
+            sc = "red" if p.get("severity") in("critical","high") else \
+                 "yellow" if p.get("severity")=="medium" else "blue"
+            t.add_row(str(i), p.get("issue",""),
+                      f"[{sc}]{p.get('severity','')}[/{sc}]",
+                      p.get("evidence","")[:90])
+        console.print(t); console.print()
+
+    # Key strengths
+    if report.get("key_strengths"):
+        t = Table(title="Key Strengths", box=box.ROUNDED, border_style="green")
+        t.add_column("Strength",style="bold"); t.add_column("Evidence",style="dim")
+        for p in report["key_strengths"]:
+            t.add_row(p.get("strength",""), p.get("evidence","")[:100])
+        console.print(t); console.print()
+
+    # Anomalies
+    if report.get("anomalies"):
+        t = Table(title="Anomalies", box=box.ROUNDED, border_style="yellow")
+        t.add_column("Anomaly",style="bold"); t.add_column("Type",width=10)
+        t.add_column("Detail",style="dim"); t.add_column("Hypothesis",style="dim")
+        for a in report["anomalies"]:
+            t.add_row(a.get("anomaly",""), a.get("type",""),
+                      a.get("detail","")[:70], a.get("hypothesis","")[:60])
+        console.print(t); console.print()
+
+    # Per-app
+    if apps:
+        t = Table(title="Per-App Breakdown", box=box.ROUNDED, border_style="cyan")
+        t.add_column("App",style="bold"); t.add_column("Reviews",justify="right")
+        t.add_column("Avg",justify="right"); t.add_column("% Neg",justify="right")
+        t.add_column("% Pos",justify="right")
+        for a in apps:
+            avg = a.get("avg_rating") or 5
+            col = "red" if avg<2.5 else "yellow" if avg<3.5 else "green"
+            t.add_row(str(a["app"])[:40], str(a["count"]),
+                      f"[{col}]{avg}[/{col}]",
+                      f"{a.get('pct_negative','?')}%",
+                      f"{a.get('pct_positive','?')}%")
+        console.print(t); console.print()
+
+    # Recommendations
+    if report.get("recommendations"):
+        t = Table(title="Recommendations", box=box.ROUNDED, border_style="bright_white")
+        t.add_column("#",width=3,style="dim"); t.add_column("Priority",width=10)
+        t.add_column("Action",style="bold"); t.add_column("Impact",style="dim")
+        for i, r in enumerate(report["recommendations"],1):
+            pc = "red" if r.get("priority") in("critical","high") else \
+                 "yellow" if r.get("priority")=="medium" else "blue"
+            t.add_row(str(i), f"[{pc}]{r.get('priority','')}[/{pc}]",
+                      r.get("action",""), r.get("expected_impact","")[:70])
+        console.print(t); console.print()
+
+    # Tool call log
+    if tools:
+        t = Table(title="Tool Calls", box=box.SIMPLE, border_style="dim")
+        t.add_column("Tool",style="dim"); t.add_column("Args",style="dim"); t.add_column("✓",width=3)
+        for tc_ in tools:
+            ok = "[green]✓[/green]" if tc_.get("ok") else "[red]✗[/red]"
+            t.add_row(tc_.get("tool",""), str(tc_.get("args",{}))[:55], ok)
+        console.print(t); console.print()
+
+    if report.get("confidence_note"):
+        console.print(Panel(f"[dim]{report['confidence_note']}[/dim]",
+                            title="Caveats", border_style="dim"))
+    console.rule("[dim]End of Report — v4[/dim]")
+
+
+def _render_plain(state: ReviewState):
+    S = "="*72
+    def sec(t): print(f"\n{S}\n  {t}\n{S}")
+    r = state.get("report",{}); st = state.get("stats",{})
+    se = state.get("sentiment",{}); q = state.get("user_query","")
+    sec("REVIEW INTELLIGENCE REPORT v4")
+    if q:
+        print(f"\nQuery : {q}")
+        print(f"Answer: {r.get('direct_answer','(see summary)')}")
+    print(f"\nSummary  : {r.get('executive_summary','')}")
+    print(f"Sentiment: {se.get('overall_tone','?')} [{se.get('method','?')}]")
+    print(f"Provider : {_PROVIDER_TAG}")
+    sec("METRICS")
+    for k,v in [("Reviews",st.get("total_reviews")),("Avg",st.get("avg_rating")),
+                ("Pos%",se.get("pct_positive")),("Neg%",se.get("pct_negative"))]:
+        print(f"  {k:<12}: {v}")
+    sec("CLUSTERS")
+    for c in state.get("clusters",[])[:8]:
+        print(f"  [{c.get('type','?')}] {c.get('label','?')} sev={c.get('severity','?')}")
+    sec("PROBLEMS")
+    for i,p in enumerate(r.get("top_problems",[]),1):
+        print(f"  #{i} [{p.get('severity','')}] {p.get('issue','')}")
+        print(f"     {p.get('evidence','')[:100]}")
+    sec("RECOMMENDATIONS")
+    for i,rec in enumerate(r.get("recommendations",[]),1):
+        print(f"  #{i} [{rec.get('priority','')}] {rec.get('action','')}")
+    print(f"\n{S}\n  End\n{S}\n")
+
+
+# ══════════════════════════════════════════════════════════════════════════════
+#  LOGGING
+# ══════════════════════════════════════════════════════════════════════════════
+
+def _log(msg: str, level: str = "info"):
+    ts    = datetime.now().strftime("%H:%M:%S")
+    icons = {"info":"·","ok":"✓","warn":"⚠","err":"✗","agent":"▶"}
+    icon  = icons.get(level,"·")
+    if RICH:
+        colors = {"info":"dim white","ok":"green","warn":"yellow","err":"red","agent":"cyan"}
+        console.print(f"[{colors.get(level,'white')}][{ts}] {icon} {msg}[/{colors.get(level,'white')}]")
+    else:
+        print(f"[{ts}] {icon} {msg}")
+
+
+# ══════════════════════════════════════════════════════════════════════════════
+#  ENTRY POINT
+# ══════════════════════════════════════════════════════════════════════════════
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Review Intelligence Agent v4 — LangGraph + LangChain",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=textwrap.dedent("""
+        Environment variables:
+          GROQ_API_KEY           Primary LLM (free tier)
+          OPENROUTER_API_KEY     Fallback #1
+          NVIDIA_API_KEY         Fallback #2 (NIMs)
+          PINECONE_API_KEY       Pinecone integrated embedding (no OpenAI needed)
+          PINECONE_INDEX         Index name (default: review-agent-v4)
+          HF_CODE_AGENT_URL      Deployed HuggingFace Space for pandas code execution
+
+        NLP:
+          DistilBERT runs locally (free, no API).
+          pip install transformers torch  to enable it.
+          Falls back to rating-heuristic if not installed.
+
+        Examples:
+          python review_agent_v4.py --csv reviews.csv
+          python review_agent_v4.py --csv reviews.csv \\
+              --query "Which action game has the most ad complaints?"
+          python review_agent_v4.py --csv reviews.csv --use-pinecone \\
+              --query "Show 1-star reviews for com.JindoBlu app"
+        """),
+    )
+    parser.add_argument("--csv",         required=True)
+    parser.add_argument("--query",       default="",
+                        help="Natural language question about the reviews")
+    parser.add_argument("--use-pinecone",action="store_true",
+                        help="Enable Pinecone RAG (requires PINECONE_API_KEY)")
+    parser.add_argument("--save-json",   action="store_true")
+    args = parser.parse_args()
+
+    if not os.path.exists(args.csv):
+        print(f"File not found: {args.csv}"); sys.exit(1)
+
+    _log(f"LLM: {_PROVIDER_TAG}", "info")
+    _log(f"BERT: {'DistilBERT (local)' if HAS_TRANSFORMERS else 'unavailable (pip install transformers torch)'}", "info")
+    if args.use_pinecone and not HAS_PINECONE:
+        _log("pinecone not installed — RAG disabled (pip install pinecone)", "warn")
+
+    graph = build_graph()
+    init  = _empty_state(args.csv, args.query.strip(),
+                         args.use_pinecone and HAS_PINECONE)
+    t0    = time.time()
+    final = graph.invoke(init)
+    _log(f"Pipeline complete in {round(time.time()-t0,1)}s  "
+         f"(planner iters: {final.get('planner_iter',0)})", "ok")
+
+    render(final)
+
+    if args.save_json:
+        out = str(Path(args.csv).with_suffix("")) + "_report_v4.json"
+        safe = {k: v for k, v in final.items() if k != "raw_df"}
+        with open(out, "w", encoding="utf-8") as f:
+            json.dump(safe, f, indent=2, default=str)
+        _log(f"JSON saved → {out}", "ok")
+
+    if final.get("errors"):
+        _log(f"Non-fatal errors: {final['errors']}", "warn")
+
+
+def run_agent(query: str, csv_path: Optional[str] = None, df: Optional[pd.DataFrame] = None, use_pinecone: bool = False) -> dict:
+    """Entry point for the web app or other modules."""
+    graph = build_graph()
+    state = _empty_state(csv_path or "", query.strip(), use_pinecone and HAS_PINECONE)
+    
+    if df is not None:
+        state["raw_df"] = df
+        
+    return graph.invoke(state)
+
+
+if __name__ == "__main__":
+    main()