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/config.py

@@ -0,0 +1,73 @@
+"""Configuration from environment / .env."""
+from __future__ import annotations
+
+import os
+from pathlib import Path
+from dotenv import load_dotenv
+
+# Load .env from backend/ (project root for this app)
+_BACKEND_DIR = Path(__file__).resolve().parent.parent
+load_dotenv(_BACKEND_DIR / ".env", override=True)
+
+
+def _get_bool(name: str, default: bool = False) -> bool:
+    raw = os.environ.get(name)
+    if raw is None:
+        return default
+    return raw.strip().lower() in {"1", "true", "yes", "on"}
+
+
+def resolve_llm() -> tuple[str, str, str, str | None]:
+    """Return (base_url, model, api_key, provider_name)."""
+    provider = os.environ.get("LLM_PROVIDER", "").strip()
+    explicit_base = os.environ.get("LLM_BASE_URL", "").strip()
+    explicit_model = os.environ.get("LLM_MODEL_NAME", "").strip()
+    api_key = os.environ.get("LLM_API_KEY", "").strip() or None
+
+    presets = {
+        "deepseek": ("https://api.deepseek.com/v1", "deepseek-chat"),
+        "openai": ("https://api.openai.com/v1", "gpt-4o-mini"),
+        "custom": ("", ""),
+    }
+    if provider and provider in presets:
+        base_url, model = presets[provider]
+        if explicit_base:
+            base_url = explicit_base
+        if explicit_model:
+            model = explicit_model
+        return base_url, model, api_key or "", provider
+    # No/unknown provider: fall back to explicit config
+    return (
+        explicit_base or "https://api.openai.com/v1",
+        explicit_model or "gpt-4o-mini",
+        api_key or "",
+        provider or None,
+    )
+
+
+class Config:
+    APP_NAME = "Sales Trainer"
+    SECRET_KEY = os.environ.get("JWT_SECRET", "dev-secret-change-me")
+    JWT_ALGO = "HS256"
+    JWT_EXPIRES_HOURS = int(os.environ.get("JWT_EXPIRES_HOURS", "24"))
+
+    DATA_DIR = Path(
+        os.environ.get("DATA_DIR", str(_BACKEND_DIR / "data"))
+    ).resolve()
+
+    FLASK_HOST = os.environ.get("FLASK_HOST", "0.0.0.0")
+    FLASK_PORT = int(os.environ.get("FLASK_PORT", "5001"))
+    FLASK_DEBUG = _get_bool("FLASK_DEBUG", True)
+
+    UPLOAD_MAX_MB = int(os.environ.get("UPLOAD_MAX_MB", "15"))
+    ALLOWED_UPLOAD_EXTS = {"pdf", "md", "txt"}
+
+    # LLM
+    LLM_BASE_URL, LLM_MODEL, LLM_API_KEY, LLM_PROVIDER = resolve_llm()
+
+    ROLES = ("super_admin", "admin", "user")
+
+    @classmethod
+    def ensure_dirs(cls) -> None:
+        for name in ("users", "orgs", "groups", "sessions"):
+            (cls.DATA_DIR / name).mkdir(parents=True, exist_ok=True)