c3d31c06e2
- 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
120 lines
4.6 KiB
Markdown
120 lines
4.6 KiB
Markdown
# 🎯 Sales Trainer
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A **corporate, multi-user sales-training simulator**. Admins upload/describe a product; the app
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analyzes it and generates **15 realistic customer personas** (5 per buying-intent tier) with real,
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varied pains. Trainees **chat one-on-one** with each persona to practice closing a sale — customers
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negotiate, stall, and refuse unless their pain is genuinely resolved. Debrief reveals + coaches.
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Built on patterns from the **CrowdSight / MiroFish** swarm engine and clean-room Hermes Brain & Tools plugin.
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---
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## Features
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- **Login + roles** (no self-registration): `super_admin` / `admin` / `user`.
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- Admin builds & edits **persona groups** (product + 15 personas), hand-edits personas, sees analytics.
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- User (trainee) **can't create** — only selects a group and practices; sees own results.
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- **Input** via form (product / segment / description) **and/or file upload** (.pdf/.md/.txt).
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Product data is used mainly to extract **pains**; personas are reusable across similar products.
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- **15 personas** (5 × tier A/B/C):
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- A = ready to buy · B = unsure · C = not interested but has pain (hardest).
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- Varied demographics, income, occupation, lifestyle, personality — consistent with product.
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- **Pain variety** (directly-solvable / partial / unrelated red-herring).
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- **Negotiation levers** (price, freebies, delivery time, scope, payment).
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- **Initiation mode**: customer opens OR seller must open the sale (outbound, e.g. insurance).
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- **Channel**: Facebook / LINE.
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- Special tier-C **"wrong_text"** persona (appears to buy, loses interest, but still has pain).
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- **One-shot rule**: a persona is chatted **once per user** (final); shared across other users.
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- **Chat realism**: all tiers can lose; everything negotiates; hidden internal signals + latent
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fields (pain/income/personality/budget) revealed only after the result.
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- **Debrief**: short summary + **coaching** (how to answer better on weak-score messages),
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scored by a **separate judge LLM** (no speed factor).
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- **Training loop**: win/lose board, **weak-area analysis**, and **user-generated personas**
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(weak-area "lock" or manual form).
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- **Admin analytics**: close rate, avg score, hardest personas.
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- **EN + TH** UI.
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---
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## Quick start
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### Local (dev)
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```bash
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# backend (Python 3.11)
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cd backend
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uv venv --python 3.11 .venv
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uv pip install -r requirements.txt --python .venv/bin/python
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cp .env.example .env # edit LLM keys + JWT_SECRET
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uv run python run.py # Flask on :5001
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# frontend (separate terminal)
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cd frontend
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npm install
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npm run dev # Vite on :3000 -> proxies /api to :5001
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```
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The first run creates a default super-admin: **`admin@salestrainer.local` / `admin123`** (change it!).
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### Docker / EasyPanel
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```bash
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cp .env.example .env # set LLM_API_KEY + a strong JWT_SECRET
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docker compose up -d # single container serving frontend + API on :5001
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```
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---
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## LLM config
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Any OpenAI-compatible endpoint (OpenAI, DeepSeek, or custom base URL):
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```env
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LLM_PROVIDER=deepseek # deepseek | openai | custom
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LLM_BASE_URL= # optional override
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LLM_MODEL_NAME=deepseek-chat # optional override
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LLM_API_KEY=sk-...
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```
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---
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## Architecture
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```
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frontend/ Vue 3 + Vite SPA (login, dashboard, group builder, personas, chat, debrief,
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gen-persona, weak-areas, analytics). Built to dist/ and served by Flask.
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backend/ Flask API (JWT auth, roles, groups, analyzer, persona generator, chat simulator,
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judge, trainee loop, analytics). Filesystem JSON persistence (no external DB).
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app/services/ analyzer · persona_generator · simulator (+ judge) · report · trainee · groups · sessions
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docs/PLAN.md full design & decisions record
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```
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- **Storage**: `backend/data/` — JSON files per entity (users, orgs, groups, sessions, my_personas).
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- **LLM calls**: analyzer (sales kit + pain-fit), persona generator, persona chat, judge.
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---
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## Tests
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Run with the built-in deterministic **mock LLM** (no external key needed):
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```bash
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cd backend
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uv run python scripts/test_m0.py # auth/roles/no-self-registration
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uv run python scripts/test_m1.py # group create + failure handling + role visibility
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uv run python scripts/test_routes.py # all API routes registered
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uv run python scripts/test_e2e.py # full flow: analyze→personas→chat→debrief→one-shot→board→analytics
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```
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Real-model verification requires a live `LLM_API_KEY` in `.env`.
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---
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## Default accounts
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| Role | Email | Password |
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|------|-------|----------|
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| super_admin | `admin@salestrainer.local` | `admin123` (change after first login) |
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Admins create additional users (users/login has no self-registration).
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