"""Generate a user's own persona (private) from weak-area spec or a manual form."""
from __future__ import annotations

from typing import Any

from ..llm import LLMClient

OWN_PERSONA_SYSTEM = """You generate ONE customer persona for a sales-training simulator,
PRIVATE to a specific trainee. You produce valid JSON only: {"persona": { ... }}.

The persona dict must contain: name, tier, channel, initiation_mode, profession, age_group,
location, product_context (revealable), plus background, income, lifestyle, personality,
communication_style, budget, decision_timeline, goal, objections[], pains[] (with fit + rootCause
+ resolutionConditions), negotiation_levers[], opener, difficulty, special, notes.

The trainee wants to specifically practice against the described weakness/profile, so make this
persona HARD in exactly that dimension (e.g. heavy price negotiation, seller-initiated cold lead,
skeptical). Keep pains partially product-solvable for realism.
"""


def build_own_persona_user_prompt(*, mode: str, spec: dict[str, Any]) -> str:
    if mode == "weak-area":
        return (
            "Mode: WEAK-AREA 'lock' persona. Generate a persona specifically targeting the "
            "trainee's reported weaknesses:\n" + str(spec)
        )
    return "Mode: MANUAL. Generate a persona matching the trainee's description:\n" + str(spec)


def generate_own_persona(llm: LLMClient, *, mode: str, spec: dict[str, Any]) -> dict[str, Any]:
    user_prompt = build_own_persona_user_prompt(mode=mode, spec=spec)
    result = llm.complete_json(OWN_PERSONA_SYSTEM, user_prompt, temperature=0.8, max_tokens=7000)
    persona = result.get("persona") or result
    if not isinstance(persona, dict):
        raise ValueError("own-persona generator returned invalid data")
    persona.setdefault("tier", "B")
    persona.setdefault("channel", "social")
    persona.setdefault("initiation_mode", "customer")
    persona.setdefault("pains", [])
    persona.setdefault("negotiation_levers", [])
    return persona