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/analyzer.py

@@ -0,0 +1,96 @@
+"""Analyzer: extracts a Sales Kit (product facts) + initial pain-fit from inputs.
+
+Product data is used primarily to derive pains that persona generation can build
+against. The result also carries a `scenario` prompt that frames persona creation.
+"""
+from __future__ import annotations
+
+from typing import Any
+
+from ..llm import LLMClient
+
+SALES_KIT_SYSTEM = """You are an expert ecommerce/B2B analyst. Given product information
+(typed in a form and/or extracted from uploaded files), produce a structured Sales Kit.
+
+Rules:
+- Output ONLY valid JSON with the exact keys requested.
+- Pain-fit: judge which pains / customer pain categories the product can PLAUSIBLY solve,
+  and clearly distinguish "strong fit" from "partial / weak fit".
+- The product info is only initial grounding; personas may be reused across similar products.
+- If some fields are unknown, leave them as empty lists / empty strings (never invent specifics).
+
+Output schema:
+{
+  "productName": string,
+  "category": string,
+  "valueProps": [string],
+  "features": [string],
+  "pricingAnchors": [string],
+  "targetAudience": { "segment": string, "demographics": string, "useCases": [string] },
+  "objectionHandlers": [string],
+  "initialPainFit": [
+    { "pain": string, "fit": "strong"|"partial"|"weak", "evidence": string }
+  ],
+  "scenarioFrame": string
+}
+The scenarioFrame is a one-paragraph description of the selling situation (who the seller,
+what channel, target segment) that will frame persona creation.
+"""
+
+
+class Analyzer:
+    def __init__(self, llm: LLMClient) -> None:
+        self.llm = llm
+
+    def analyze(
+        self,
+        *,
+        product: str = "",
+        segment: str = "",
+        description: str = "",
+        file_text: str = "",
+        channel: str = "facebook",
+    ) -> dict[str, Any]:
+        # Build the merged product context (form wins over file text)
+        product_src = product.strip() or file_text.strip() or ""
+        context = (
+            f"PRODUCT (form/typed):\n{product}\n\n" if product.strip() else ""
+        )
+        if segment.strip():
+            context += f"INITIAL CUSTOMER SEGMENT:\n{segment}\n\n"
+        if description.strip():
+            context += f"ADDITIONAL DESCRIPTION / SCENARIO:\n{description}\n\n"
+        if file_text.strip():
+            context += f"UPLOADED FILE CONTENT:\n{file_text[:12000]}\n"
+        if not context.strip():
+            raise ValueError("no product information provided (form or file)")
+
+        user_prompt = (
+            f"Channel: {channel}\n\n"
+            f"Analyze the following and return the Sales Kit JSON:\n\n{context}"
+        )
+        result = self.llm.complete_json(
+            SALES_KIT_SYSTEM, user_prompt, temperature=0.2, max_tokens=5000
+        )
+
+        # Normalize shape defensively
+        result.setdefault("productName", product_src[:200] or "Untitled product")
+        result.setdefault("category", "")
+        result.setdefault("valueProps", [])
+        result.setdefault("features", [])
+        result.setdefault("pricingAnchors", [])
+        result.setdefault("targetAudience", {
+            "segment": segment or "",
+            "demographics": "",
+            "useCases": [],
+        })
+        result.setdefault("objectionHandlers", [])
+        result.setdefault("initialPainFit", [])
+        result.setdefault("scenarioFrame", description or "")
+
+        for k in ("valueProps", "features", "pricingAnchors", "objectionHandlers"):
+            if not isinstance(result[k], list):
+                result[k] = []
+        if not isinstance(result.get("initialPainFit"), list):
+            result["initialPainFit"] = []
+        return result