"""LLM 智能增强服务(翻译后调)。

4 个独立任务:
  1. format   — 排版译文(写入 body_zh_formatted)
  2. classify — 分类(写入 category)
  3. image    — 生成插图(写入 image_ai_url)
  4. commentary — 写点评(写入 commentary)

设计:
- 任务入口: enrich_article(article_id, settings_row)
- 任务间互不影响:每个任务独立 try/except + 写 status
- 全部任务共走 LlmClient 的全局限速
- 若设置 enabled=False,只跳过(不调 LLM)
"""
from __future__ import annotations

import asyncio
import logging
from typing import Any

from sqlalchemy import select

from app.database import AsyncSessionLocal
from app.models.article import Article
from app.models.llm_setting import LlmSetting
from app.schemas.llm import get_default_prompts
from app.services.llm.client import LlmClient

logger = logging.getLogger("news.llm.enrichment")


# === 获取当前设置(行锁 + 缓存刷新)===
async def get_setting() -> LlmSetting:
    """读 llm_settings 单行;不存在则用默认值插入。"""
    async with AsyncSessionLocal() as session:
        row = (await session.execute(select(LlmSetting).where(LlmSetting.id == 1))).scalar_one_or_none()
        if row is None:
            defaults = get_default_prompts()
            row = LlmSetting(
                id=1,
                format_prompt=defaults["format_prompt"],
                classify_prompt=defaults["classify_prompt"],
                commentary_prompt=defaults["commentary_prompt"],
                image_prompt_template=defaults["image_prompt_template"],
            )
            session.add(row)
            await session.commit()
            await session.refresh(row)
        return row


# === 单任务:format ===
async def _enrich_format(article: Article, setting: LlmSetting, client: LlmClient) -> None:
    prompt = (setting.format_prompt or get_default_prompts()["format_prompt"]).format(
        body=(article.body_zh_text or "")[:6000]
    )
    text = await client.chat(
        system="你是中文新闻排版助手,只输出排版后的纯文本。",
        user=prompt,
        temperature=0.3,
        max_tokens=2000,
    )
    # 极简 HTML 包裹:按段切 + <p>
    parts = [f"<p>{p.strip()}</p>" for p in text.split("\n\n") if p.strip()]
    article.body_zh_formatted = "\n".join(parts) or None
    article.format_status = "ok"


# === 单任务:classify ===
async def _enrich_classify(article: Article, setting: LlmSetting, client: LlmClient) -> None:
    prompt = (setting.classify_prompt or get_default_prompts()["classify_prompt"]).format(
        title=(article.title_zh or article.title)[:200],
        summary=(article.summary_zh or "")[:400],
    )
    result = await client.classify_json(
        system="你是新闻分类助手,只返回 JSON。",
        user=prompt,
    )
    cats = result.get("categories") or []
    if isinstance(cats, list) and cats:
        article.category = ",".join(str(c).strip() for c in cats[:3])[:32]
    article.classify_status = "ok"


# === 单任务:image ===
async def _enrich_image(article: Article, setting: LlmSetting, client: LlmClient) -> None:
    template = (setting.image_prompt_template or get_default_prompts()["image_prompt_template"])
    # 默认用 title_zh(若有),否则用原文 title
    title_for_prompt = (article.title_zh or article.title or "")[:200]
    prompt = template.format(title=title_for_prompt)
    url = await client.generate_image(prompt, size=setting.image_size)
    article.image_ai_url = url
    article.image_ai_status = "ok"


# === 单任务:commentary ===
async def _enrich_commentary(article: Article, setting: LlmSetting, client: LlmClient) -> None:
    prompt = (setting.commentary_prompt or get_default_prompts()["commentary_prompt"]).format(
        title=(article.title_zh or article.title)[:200],
        body=(article.body_zh_text or "")[:3000],
    )
    text = await client.chat(
        system="你是资深新闻评论员。",
        user=prompt,
        temperature=0.6,
        max_tokens=600,
    )
    article.commentary = text or None
    article.commentary_status = "ok"


# === 总编排:enrich_article ===
async def enrich_article(article_id: int) -> dict[str, str]:
    """对单篇文章做 4 项 LLM 增强。

    返回 {task: status} 字典(用于日志)。
    """
    async with AsyncSessionLocal() as session:
        art = (
            await session.execute(select(Article).where(Article.id == article_id))
        ).scalar_one_or_none()
        if not art:
            logger.warning("enrich_article: id=%s not found", article_id)
            return {}
        if not (art.title_zh or art.body_zh_text):
            logger.info("enrich_article: id=%s no translation yet, skip", article_id)
            return {}

    # 拉取设置
    setting = await get_setting()
    if not setting.enabled:
        logger.info("enrich_article: llm disabled, skip id=%s", article_id)
        return {"format": "skipped", "classify": "skipped", "image": "skipped", "commentary": "skipped"}

    # 用配置生成 client(允许热改设置)
    client = LlmClient(
        chat_model=setting.chat_model,
        image_model=setting.image_model,
        interval_sec=setting.interval_sec,
    )

    results: dict[str, str] = {}

    async with AsyncSessionLocal() as session:
        art = (
            await session.execute(select(Article).where(Article.id == article_id))
        ).scalar_one_or_none()
        if not art:
            return {}

        # 4 个任务(互不影响);format / classify / commentary 是 chat,image 是 image
        # 串行执行(已经过 client 内部 Semaphore),但每个 try/except 独立
        tasks: list[tuple[str, Any]] = [
            ("format", _enrich_format(art, setting, client)),
            ("classify", _enrich_classify(art, setting, client)),
            ("image", _enrich_image(art, setting, client)),
            ("commentary", _enrich_commentary(art, setting, client)),
        ]
        for name, coro in tasks:
            try:
                await coro
                results[name] = "ok"
            except Exception as e:
                logger.exception("enrich %s failed for article %s: %s", name, article_id, e)
                results[name] = f"failed:{type(e).__name__}"
                # 标 status
                if name == "format":
                    art.format_status = "failed"
                elif name == "classify":
                    art.classify_status = "failed"
                elif name == "image":
                    art.image_ai_status = "failed"
                elif name == "commentary":
                    art.commentary_status = "failed"

        await session.commit()
    logger.info("enrich_article id=%s: %s", article_id, results)
    return results


# === 后台循环 ===
# 与 translation_loop 一样,常驻从队列里取文章
ENRICHMENT_INTERVAL_SEC = 5.0  # 没活时等待
ENRICHMENT_BATCH_SIZE = 1


async def enrichment_loop() -> None:
    """扫描已翻译但未 enrich 的文章(任一 *_status 为 pending/n/a 且 translation_status=ok)。

    跟 translation_loop 一样常驻。
    """
    logger.info("enrichment_loop started")
    # 等一下让翻译先跑
    await asyncio.sleep(10)
    while True:
        try:
            async with AsyncSessionLocal() as session:
                # 已翻译完成 + 4 个状态中至少有一个是 pending
                rows = (
                    await session.execute(
                        select(Article)
                        .where(
                            Article.translation_status == "ok",
                            Article.title_zh.is_not(None),
                        )
                        .order_by(Article.translated_at.asc().nullslast(), Article.id.asc())
                        .limit(ENRICHMENT_BATCH_SIZE * 5)  # 多取几个找需要 enrich 的
                    )
                ).scalars()
                candidates = list(rows)

            # 过滤:任一 *_status 是 pending
            todo_ids: list[int] = []
            for a in candidates:
                statuses = [
                    a.format_status or "pending",
                    a.classify_status or "pending",
                    a.image_ai_status or "pending",
                    a.commentary_status or "pending",
                ]
                if any(s in ("pending", "failed", "n/a") for s in statuses):
                    todo_ids.append(a.id)
                if len(todo_ids) >= ENRICHMENT_BATCH_SIZE:
                    break

            if not todo_ids:
                await asyncio.sleep(ENRICHMENT_INTERVAL_SEC)
                continue

            for aid in todo_ids:
                try:
                    await enrich_article(aid)
                except Exception as e:
                    logger.exception("enrich_article %s in loop failed: %s", aid, e)
                await asyncio.sleep(0.5)  # 文章间轻节流
        except Exception as e:
            logger.exception("enrichment_loop error: %s", e)
            await asyncio.sleep(ENRICHMENT_INTERVAL_SEC)