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
2026-08-07 15:31:06 +07:00
commit c3d31c06e2
70 changed files with 6135 additions and 0 deletions
--- a/backend/scripts/mock_llm.py
+++ b/backend/scripts/mock_llm.py
@@ -0,0 +1,111 @@
+"""Mock LLM for deterministic end-to-end tests (no external API needed).
+
+Substitutes for app.llm.LLMClient. Returns canned JSON for structured calls and
+simple replies for chat calls, so the full analyze→persona→chat→debrief flow runs.
+"""
+from __future__ import annotations
+
+import json
+from typing import Any
+
+
+SAMPLE_SALES_KIT = {
+    "productName": "CloudPOS",
+    "category": "POS software",
+    "valueProps": ["faster checkout", "inventory sync"],
+    "features": ["tablets", "reports"],
+    "pricingAnchors": ["1,000 THB/month"],
+    "targetAudience": {"segment": "SME restaurants", "demographics": "", "useCases": ["front counter"]},
+    "objectionHandlers": ["free trial", "setup included"],
+    "initialPainFit": [
+        {"pain": "slow checkout queues", "fit": "strong", "evidence": "faster checkout"},
+        {"pain": "lost sales from stockouts", "fit": "partial", "evidence": "inventory sync"},
+    ],
+    "scenarioFrame": "Cloud POS sold over LINE to Bangkok SME restaurants.",
+}
+
+
+def _sample_persona(idx: int, tier: str) -> dict[str, Any]:
+    return {
+        "id": f"persona-{idx:02d}",
+        "name": f"Persona {idx}",
+        "tier": tier,
+        "channel": "line",
+        "initiation_mode": "customer" if idx % 3 else "seller",
+        "profession": "restaurant owner",
+        "age_group": "30s",
+        "location": "Bangkok",
+        "product_context": "running a small noodle shop",
+        "background": "Runs a family noodle shop for 8 years.",
+        "income": "60k THB/month",
+        "lifestyle": "works long hours",
+        "personality": "practical and cautious",
+        "communication_style": "short, direct, casual",
+        "budget": "1,500 THB/month max",
+        "decision_timeline": "within 2 weeks",
+        "goal": "reduce lunch-rush queues",
+        "objections": ["too expensive", "hard to learn"],
+        "pains": [
+            {"id": "p1", "name": "slow checkout", "fit": "strong",
+             "description": "Long queues at lunch", "rootCause": "manual order taking",
+             "resolutionConditions": ["show faster checkout", "offer a trial"]},
+            {"id": "p2", "name": "stockouts", "fit": "partial",
+             "description": "Runs out of ingredients", "rootCause": "no inventory tracking",
+             "resolutionConditions": ["show inventory feature"]},
+        ],
+        "negotiation_levers": ["price reduction", "free setup"],
+        "opener": "Hi, I saw your POS ad. Does it work with small shops?",
+        "special": "wrong_text" if (tier == "C" and idx % 5 == 4) else "",
+        "difficulty": 2 if tier == "A" else (3 if tier == "B" else 4),
+        "notes": "sample",
+    }
+
+
+def make_personas() -> list[dict[str, Any]]:
+    out = []
+    idx = 1
+    for tier in ["A", "B", "C"]:
+        for _ in range(5):
+            out.append(_sample_persona(idx, tier))
+            idx += 1
+    return out
+
+
+class MockLLM:
+    """Drop-in for app.llm.LLMClient — reads config the same way."""
+
+    persona_count = 0
+
+    def __init__(self, **kwargs):
+        pass
+
+    def complete(self, system_prompt: str, user_prompt: str, **kw) -> str:
+        if "Persona generation prompts" in system_prompt or "persona designer" in system_prompt.lower():
+            return json.dumps({"personas": make_personas()}, ensure_ascii=False)
+        if "market-research persona designer" in system_prompt.lower():
+            return json.dumps({"personas": make_personas()}, ensure_ascii=False)
+        return "ok"
+
+    def complete_json(self, system_prompt: str, user_prompt: str, **kw) -> dict[str, Any]:
+        sp = system_prompt.lower()
+        if "ecommerce/b2b analyst" in sp:
+            return dict(SAMPLE_SALES_KIT)
+        if "market-research persona designer" in sp:
+            return {"personas": make_personas()}
+        if "sales-training simulator" in sp and "PRIVATE" in system_prompt:
+            return {"persona": _sample_persona(99, "C")}
+        if "judge" in sp and "sales-training chat" in sp:
+            return {
+                "outcome": "won",
+                "score": 82,
+                "pain": "slow checkout queues",
+                "why": "resolved the pain and secured acceptance",
+                "failurePoints": [],
+                "coaching": [],
+                "painProgress": {"slow checkout": 100},
+            }
+        return {}
+
+    def complete_conversation(self, messages, **kw) -> str:
+        # persona chat: echo a short in-character reply
+        return json.dumps({"reply": "I see. Tell me more about the price then."}, ensure_ascii=False)