Sales Trainer v0.1: corporate sales-training simulator (Flask+Vue, 15 personas, chat simulator, judge, analytics)

- Auth/roles (no self-reg), admin user provision, JWT
- Analyze: sales kit + initial pain-fit from form/upload
- Persona generator: 15 personas (5/tier) w/ pain variety, negotiation, init mode, channel, latent/revealable, wrong_text special
- Chat simulator: per-mode initiation, one-shot, hidden signals, judge-LLM debrief+coaching
- Trainee loop: win/lose board, weak-areas, user-generated personas
- Admin analytics; EN+TH Vue SPA served by Flask
- Deploy: Dockerfile, docker-compose, README, eng-log + HANDOFF
- Tests (mock LLM): m0/m1/routes/e2e all pass

backend/app/services/persona_generator.py

@@ -0,0 +1,74 @@
+"""Persona generator: builds 15 personas (5 per tier) from a Sales Kit + scenario."""
+from __future__ import annotations
+
+import json
+from typing import Any
+
+from ..llm import LLMClient
+from .persona_prompts import PERSONA_SYSTEM
+
+TIERS = ["A", "B", "C"]
+PER_TIER = 5
+
+
+class PersonaGenerator:
+    def __init__(self, llm: LLMClient) -> None:
+        self.llm = llm
+
+    def generate(
+        self,
+        *,
+        sales_kit: dict[str, Any],
+        language: str = "en",
+        channel: str = "facebook",
+    ) -> list[dict[str, Any]]:
+        kit_json = json.dumps(sales_kit, ensure_ascii=False)[:12000]
+        lang_name = "Thai" if language == "th" else "English"
+        scenario = (sales_kit.get("scenarioFrame") or "").strip() or "a general product sale"
+        user_prompt = (
+            f"Platform/channel preference: {channel}\n"
+            f"Language: {lang_name} (all persona text in {lang_name})\n"
+            f"Sales Kit:\n{kit_json}\n\n"
+            f"Generate exactly 15 personas (5 per tier A/B/C) as JSON."
+        )
+        result = self.llm.complete_json(
+            PERSONA_SYSTEM, user_prompt, temperature=0.8, max_tokens=14000
+        )
+        personas = result.get("personas") or []
+        if not isinstance(personas, list) or not personas:
+            raise ValueError("persona generator returned no personas")
+
+        normalized, counts = [], {"A": 0, "B": 0, "C": 0}
+        for idx, p in enumerate(personas, start=1):
+            if not isinstance(p, dict):
+                continue
+            tier = p.get("tier", p.get("intent_tier"))
+            if tier not in TIERS:
+                tier = "B"
+            if counts[tier] >= PER_TIER:
+                continue  # skip overflow per tier
+            counts[tier] += 1
+            p["id"] = f"persona-{idx:02d}"
+            p["tier"] = tier
+            p["channel"] = p.get("channel", channel)
+            p.setdefault("initiation_mode", "customer")
+            p.setdefault("special", "")
+            p.setdefault("difficulty", 1)
+            p.setdefault("pains", [])
+            p.setdefault("negotiation_levers", [])
+            p.setdefault("objections", [])
+            normalized.append(p)
+
+        # Wrap tier-C: ensure at least one wrong_text persona
+        if "C" in counts and not any(
+            p.get("special") == "wrong_text" for p in normalized
+        ):
+            # find first tier-C and mark it
+            for p in normalized:
+                if p["tier"] == "C":
+                    p["special"] = "wrong_text"
+                    break
+
+        if len(normalized) < 15:
+            raise ValueError(f"expected 15 personas, generated {len(normalized)}")
+        return normalized