"""独立候选回放：多轮 user 链式调用 LLM，不走路由 WS / ChatOrchestrator。"""

from __future__ import annotations

import time
from typing import Any

from app.core.logging import get_logger

logger = get_logger(__name__)


def _system_prompt_for_eval(version_config: dict | None) -> str:
    cfg = version_config or {}
    extra = (cfg.get("system_prompt_suffix") or "").strip()
    base = """你是「岁月留书」老年友好访谈员。语气温暖、耐心，先承接情绪再 gently 追问事实与感受；回答简洁分段，避免术语。"""
    if extra:
        return f"{base}\n\n{extra}"
    return base


def _model_override(version_config: dict | None) -> str | None:
    if not version_config:
        return None
    m = version_config.get("model")
    return str(m).strip() if m else None


class EvalCandidateRunner:
    """使用 LangChain Chat 模型回放用户轮次。"""

    def __init__(self, llm: Any) -> None:
        self._llm = llm

    async def replay_utterances(
        self,
        utterances: list[str],
        *,
        version_config: dict | None = None,
        temperature: float = 0.7,
    ) -> tuple[list[str], list[int]]:
        """返回每轮 assistant 回复与耗时 ms。"""
        if not self._llm:
            raise RuntimeError(" replay: llm 未配置")
        from langchain_core.messages import AIMessage, HumanMessage, SystemMessage

        sys_prompt = _system_prompt_for_eval(version_config)
        model = _model_override(version_config)
        lc_messages: list = [SystemMessage(content=sys_prompt)]
        replies: list[str] = []
        latencies: list[int] = []
        bound = (
            self._llm.bind(model=model, temperature=temperature)
            if model
            else self._llm.bind(temperature=temperature)
        )
        for u in utterances:
            text = (u or "").strip()
            if not text:
                continue
            lc_messages.append(HumanMessage(content=text))
            t0 = time.perf_counter()
            result = await bound.ainvoke(lc_messages)
            elapsed_ms = int((time.perf_counter() - t0) * 1000)
            reply = str(getattr(result, "content", "") or "").strip()
            replies.append(reply)
            latencies.append(elapsed_ms)
            lc_messages.append(AIMessage(content=reply))
        return replies, latencies


def simple_memoir_from_transcript(utterances: list[str], replies: list[str]) -> str:
    """轻量成稿：供评审用占位（非生产叙事管线）。"""
    lines = ["# 访谈摘录整理（评测占位稿）", ""]
    for i, u in enumerate(utterances):
        lines.append(f"## 片段 {i + 1}")
        lines.append("")
        lines.append(f"**用户：** {u.strip()}")
        if i < len(replies):
            lines.append("")
            lines.append(f"**访谈者：** {replies[i].strip()}")
        lines.append("")
    return "\n".join(lines)