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/llm.py

@@ -0,0 +1,131 @@
+"""OpenAI-compatible LLM client (OpenAI / DeepSeek / custom base URL).
+
+Mirrors the MiroFish provider-agnostic pattern. Credentials live in .env only.
+"""
+from __future__ import annotations
+
+import json
+import re
+from typing import Any
+
+from openai import OpenAI
+
+from .config import Config
+
+
+class LLMError(Exception):
+    pass
+
+
+def _strip_thinking_trace(text: str) -> str:
+    """Remove ReACT-style chain-of-thought / fences, keep the final JSON text."""
+    for fence in ("```json", "```"):
+        idx = text.rfind(fence)
+        if idx != -1:
+            after = text[idx:].lstrip()
+            lang_len = after.find("\n")
+            body = after[lang_len:] if lang_len != -1 else after
+            end = body.rfind("```")
+            if end != -1:
+                body = body[:end]
+            body = body.strip()
+            if body:
+                return body
+    for marker in ("\n\n[", "\n\n{"):
+        idx = text.rfind(marker)
+        if idx != -1:
+            candidate = text[idx:].strip()
+            if candidate and candidate[0] in "{[":
+                return candidate
+    return text
+
+
+class LLMClient:
+    def __init__(
+        self,
+        *,
+        base_url: str | None = None,
+        api_key: str | None = None,
+        model: str | None = None,
+    ) -> None:
+        self.base_url = base_url or Config.LLM_BASE_URL
+        self.api_key = api_key or Config.LLM_API_KEY
+        self.model = model or Config.LLM_MODEL
+        if not self.api_key:
+            raise LLMError("LLM_API_KEY is not configured in .env")
+        if not self.base_url:
+            raise LLMError("LLM_BASE_URL is not configured (unknown provider)")
+        self.client = OpenAI(base_url=self.base_url, api_key=self.api_key)
+
+    def complete(
+        self,
+        system_prompt: str,
+        user_prompt: str,
+        *,
+        temperature: float = 0.5,
+        max_tokens: int = 3000,
+    ) -> str:
+        try:
+            resp = self.client.chat.completions.create(
+                model=self.model,
+                temperature=temperature,
+                max_tokens=max_tokens,
+                messages=[
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": user_prompt},
+                ],
+            )
+        except Exception as exc:  # network/auth/provider
+            raise LLMError(f"LLM call failed: {exc}") from exc
+        text = (resp.choices[0].message.content or "").strip()
+        if not text:
+            raise LLMError("LLM returned empty response")
+        return text
+
+    def complete_json(
+        self,
+        system_prompt: str,
+        user_prompt: str,
+        *,
+        temperature: float = 0.2,
+        max_tokens: int = 6000,
+    ) -> dict[str, Any]:
+        text = self.complete(
+            system_prompt,
+            user_prompt,
+            temperature=temperature,
+            max_tokens=max_tokens,
+        )
+        text = _strip_thinking_trace(text)
+        try:
+            return json.loads(text)
+        except json.JSONDecodeError as exc:
+            # Last-ditch: strip leading text before the first { or [
+            match = re.search(r"[{\[].*[}\]]", text, re.DOTALL)
+            if match:
+                try:
+                    return json.loads(match.group(0))
+                except json.JSONDecodeError:
+                    pass
+            raise LLMError(f"LLM returned invalid JSON: {exc}") from exc
+
+    def complete_conversation(
+        self,
+        messages: list[dict[str, str]],
+        *,
+        temperature: float = 0.6,
+        max_tokens: int = 1200,
+    ) -> str:
+        try:
+            resp = self.client.chat.completions.create(
+                model=self.model,
+                temperature=temperature,
+                max_tokens=max_tokens,
+                messages=messages,
+            )
+        except Exception as exc:
+            raise LLMError(f"LLM call failed: {exc}") from exc
+        text = (resp.choices[0].message.content or "").strip()
+        if not text:
+            raise LLMError("LLM returned empty response")
+        return text