fix(chat): resume unfinished session without re-picking scenario; detect win/loss from real-LLM text

- start_session now RESUMES an existing ACTIVE session for the persona instead of creating
  a new one / forcing re-pick of the scenario (keep original scenario+messages).
- send_message falls back to a text-based decision detector (_detect_customer_decision)
  because real LLMs rarely emit structured meta.decision — so a customer who says
  'ซื้อไม่ไหว'/'no thanks' now actually ENDS the chat as lost (was stuck active forever).
  Fragments handled in TH + EN; buy + walk.
All 11 backend suites pass. Rebuilt dist.

backend/app/api/chat_routes.py

@@ -27,6 +27,39 @@ def _sim(group, persona):
     return Simulator(llm)
 
 
+WALK_FRAGMENTS = [
+    "no thanks", "not interested", "forget it", "never mind", "no longer", "out of budget",
+    "too expensive", "can't afford", "cannot afford", "ซื้อไม่ไหว", "ไม่ซื้อ", "ไม่เอาแล้ว",
+    "ไม่สนใจ", "พอแค่นี้", "ไม่ไหว", "แพงไป", "ลืมไปเถอะ", "ตัดสินใจไม่ซื้อ", "ขอไม่ซื้อ",
+    "ยังไม่ซื้อ", "ไว้ก่อน", "ไปก่อนนะ", "ขอบคุณแต่ไม่เอา", "ไม่เอาครับ", "ไม่เอาค่ะ", "ไม่เอา",
+]
+BUY_FRAGMENTS = [
+    "i'll take it", "i'll go with it", "i'll buy", "deal,", "let's do it", "count me in",
+    "how do i pay", "where do i sign", "ok i'll buy", "ซื้อเลย", "ตกลงซื้อ", "เอาครับ", "เอาค่ะ",
+    "ซื้อครับ", "ซื้อค่ะ", "ตัดสินใจซื้อ", "เอาเลย", "ตกลง", "ซื้อ", "รับไว้", "โอเคค่ะ รับ", "โอเคครับ รับ",
+]
+
+
+def _detect_customer_decision(text: str, locale: str = "th") -> str | None:
+    """Detect whether the customer clearly walked away or decided to buy, from their message.
+
+    Returns 'walk' | 'buy' | None. Used as a fallback because real LLMs rarely emit a
+    structured meta.decision — they just state it in the conversation.
+    """
+    if not text:
+        return None
+    low = text.lower()
+    # Walk-away signals can appear anywhere; be conservative to avoid false ends on
+    # phrases like "I can't afford that, what's the discount?" (question) vs "too expensive, no."
+    for frag in WALK_FRAGMENTS:
+        if frag in low:
+            return "walk"
+    for frag in BUY_FRAGMENTS:
+        if frag in low:
+            return "buy"
+    return None
+
+
 def _scenarios(locale: str = "th"):
     """Scenario presets, localized. Returns {id: {label, init, adapt}}."""
     t = locale != "en"
@@ -153,6 +186,18 @@ def start_session(gid: str, pid: str):
     if locale not in ("en", "th"):
         locale = "th"
     scenario_meta, init_mode = _scenario_config(scenario, persona, locale)
+
+    # RESUME: if this user already has an ACTIVE (unfinished) session for this persona, keep
+    # chatting it — do NOT create a new one and do NOT force re-picking the scenario.
+    existing = s["sessions"].active_for_persona(actor["id"], pid)
+    if existing and existing.get("group_id") == gid:
+        return jsonify({
+            "session": existing,
+            "initiation_mode": existing.get("persona_meta", {}).get("initiation_mode") or "customer",
+            "scenario": existing.get("scenario", scenario),
+            "scenario_meta": _scenario_config(existing.get("scenario", scenario), persona, locale),
+        })
+
     # One-shot: reject if already finished this persona
     try:
         session = s["sessions"].create(
@@ -273,6 +318,11 @@ def send_message(gid: str, pid: str):
 
     # Decision by the persona ends the session (one-shot lock).
     decision = meta.get("decision")
+    # Real LLMs usually DON'T emit a structured meta.decision — they just say it in the
+    # customer's reply. Fall back to a lightweight text detector so "ซื้อไม่ไหว"/"no thanks"
+    # actually ENDS the chat (otherwise it never reports win/loss).
+    if decision not in ("buy", "walk"):
+        decision = _detect_customer_decision(reply, locale=slocale)
     if decision in ("buy", "walk"):
         outcome = "won" if decision == "buy" else "lost"
         debrief = _build_abbrev_debrief(outcome, persona, internal, slocale)