xiaoxia-saas/apps/worker/worker_app/tasks/viral_video.py

"""爆款视频 Celery 编排器 — ViralVideoOrchestrator (v1.6 单次 Seedance 出片版).

v1.6 重大简化（Seedance 2.5 单次最长 30 秒，直接出片）：
  1. _step_image_analysis   图片 VLM 分析（保留）
  1.5 _step_video_analysis  参考视频风格分析（可选）
  2. _step_intent_parsing   用户文案意图解析
  3. _step_script_generation 编导分镜脚本生成（融合原 copy_fusion+storyboard+review，输出 copy_result 结构 + voiceover_script）
  4. _step_review           合规审核（6 维度，不通过自动重写 1 次）
  5. _step_tts              CosyVoice 整段配音（voiceover_script → 单个 mp3 → 上传 OSS 拿公网 URL）
  6. _step_render           单次 Seedance 生成（prompt=完整编导脚本，reference_audios=[TTS URL]，reference_images=产品图，generate_audio=true）
  7. _step_upload           OSS 上传单个视频文件 + 通知 + 扣点

删除/不再使用：
- 分镜拆分多段生成（storyboard 不再单独驱动分段生成，仅作为 copy_result.shots 存到 DB 给前端/日志参考）
- ffmpeg concat 拼接（concat_engine 保留但 viral video 主流程不再调用）
- placeholder 占位视频、分段重试降级
- BGM 单独混音（Seedance generate_audio=true 原生生成环境音效/BGM）
"""

from __future__ import annotations

import json
import logging
import os
import tempfile
import threading
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
from pathlib import Path

from celery import Task, shared_task
from celery.exceptions import Retry
from worker_app.celery_app import celery_app  # noqa: F401
from worker_app.db import SessionLocal

from packages.adapters.sqlalchemy_impl.viral_video_repository import (
    SQLAlchemyViralVideoJobRepository,
)
from packages.domain.viral_video import (
    STAGE_LABELS,
    ViralVideoJob,
    ViralVideoStage,
    ViralVideoStatus,
)
from packages.shared import get_shared_settings

logger = logging.getLogger(__name__)


# ── WS 进度推送 ──────────────────────────────────────────────────────────


def _emit_progress(
    job_id: str,
    stage: str,
    progress: float,
    message: str = "",
    data: dict | None = None,
    event_type: str = "viral_video:progress",
):
    try:
        import redis as redis_lib

        redis_url = os.environ.get("REDIS_URL", "redis://localhost:6379/0")
        r = redis_lib.from_url(redis_url)
        event = {
            "type": event_type,
            "job_id": job_id,
            "stage": stage,
            "progress": progress,
            "message": message or STAGE_LABELS.get(stage, stage),
            "data": data or {},
        }
        r.publish(f"viral_video:{job_id}", json.dumps(event, ensure_ascii=False))
    except Exception as e:
        logger.warning("[爆款视频] WS 进度推送失败: %s", e)


# ── 仓储辅助 ────────────────────────────────────────────────────────────


def _get_repo_and_job(job_id: str):
    session = SessionLocal()
    repo = SQLAlchemyViralVideoJobRepository(session)
    job = repo.get(job_id)
    return session, repo, job


def _save_job(repo, job, session):
    repo.update(job)
    session.commit()


def _set_stage(job, repo, session, stage: str, message: str, persist: bool = True) -> None:
    """更新细粒度阶段并持久化到 DB，同时通过 Redis 推送进度事件。

    stage 用 ViralVideoStage.value（snake_case，与前端 phase 对齐）。
    message 为中文提示文案，前端轮询/SSE 直接展示给用户。
    """
    job.current_stage = stage or ""
    job.phase_message = message or ""
    _emit_progress(job.id, stage, 0.0, message)
    if persist and repo is not None and session is not None:
        try:
            _save_job(repo, job, session)
        except Exception as e:  # 阶段持久化失败不阻塞主流程
            logger.warning("[爆款视频] 阶段持久化失败 stage=%s err=%s", stage, e)


# ── worker 心跳（僵尸任务检测） ─────────────────────────────────────────

# 心跳间隔（秒）；超过此时间未更新 heartbeat_at 视为 worker 异常
_HEARTBEAT_INTERVAL_SEC = 25
# 任务整体超时：running 超过此时长且心跳停止，则判定为僵尸并回收
_STALE_RUNNING_TIMEOUT_SEC = 10 * 60  # 10 分钟
# 心跳过期窗口：heartbeat_at 距 now 超过此时长视为失效
_HEARTBEAT_EXPIRE_SEC = 2 * 60  # 2 分钟


def _heartbeat_once(job_id: str) -> None:
    """在独立 session 中更新一次 heartbeat_at（不捕获主流程事务状态）。"""
    ssn = None
    try:
        from datetime import datetime, timezone

        ssn = SessionLocal()
        ssn.execute(
            __import__("sqlalchemy").text(
                "UPDATE viral_video_jobs SET heartbeat_at = :now, updated_at = :now "
                "WHERE id = :jid AND status = 'running'"
            ),
            {"now": datetime.now(timezone.utc), "jid": job_id},
        )
        ssn.commit()
    except Exception as e:
        logger.warning("[爆款视频] 心跳更新失败 job=%s err=%s", job_id, e)
    finally:
        if ssn is not None:
            try:
                ssn.close()
            except Exception:
                pass


def _start_heartbeat_thread(job_id: str) -> tuple[threading.Event, threading.Thread]:
    """启动后台心跳线程，每 _HEARTBEAT_INTERVAL_SEC 秒更新一次 heartbeat_at。
    返回 (stop_event, thread)；任务结束时调用 stop_event.set() 停止心跳。
    """
    stop = threading.Event()

    def _loop():
        # 立即打一次心跳
        _heartbeat_once(job_id)
        while not stop.wait(_HEARTBEAT_INTERVAL_SEC):
            _heartbeat_once(job_id)

    t = threading.Thread(target=_loop, name=f"vv-heartbeat-{job_id[:8]}", daemon=True)
    t.start()
    return stop, t


def _recover_stale_jobs() -> int:
    """启动/定时扫描：把僵尸任务（running 超时且心跳停止）标记为 failed。
    返回本次回收的任务数。可由 celery beat 周期性调用，也可在任务启动前顺带扫一次。
    """
    from datetime import datetime, timedelta, timezone

    ssn = None
    try:
        ssn = SessionLocal()
        now = datetime.now(timezone.utc)
        # 判定条件：status=running 且 (started_at 距今 > 10min) 且 (heartbeat_at < now-2min 或 heartbeat_at IS NULL 且 updated_at < now-2min)
        cutoff_beat = now - timedelta(seconds=_HEARTBEAT_EXPIRE_SEC)
        cutoff_start = now - timedelta(seconds=_STALE_RUNNING_TIMEOUT_SEC)
        sql = __import__("sqlalchemy").text(
            "UPDATE viral_video_jobs "
            "SET status='failed', error_msg='任务执行超时，请重试', updated_at=:now "
            "WHERE status='running' "
            "  AND started_at IS NOT NULL AND started_at < :cutoff_start "
            "  AND (heartbeat_at IS NULL OR heartbeat_at < :cutoff_beat) "
            "  AND (heartbeat_at IS NOT NULL OR updated_at < :cutoff_beat)"
        )
        result = ssn.execute(sql, {"now": now, "cutoff_start": cutoff_start, "cutoff_beat": cutoff_beat})
        ssn.commit()
        cnt = result.rowcount or 0
        if cnt > 0:
            logger.warning("[爆款视频] 回收 %d 个僵尸 running 任务", cnt)
        return cnt
    except Exception as e:
        logger.warning("[爆款视频] 僵尸任务扫描失败: %s", e)
        return 0
    finally:
        if ssn is not None:
            try:
                ssn.close()
            except Exception:
                pass


# ── 默认结构 ─────────────────────────────────────────────────────────────

_DEFAULT_HARD_CONSTRAINTS = [
    "无字幕、无水印、无任何自动生成文字、无 logo",
    "同一人物全程保持一致的五官、发型、服装、身材，不得换脸或变形",
    "口播语音必须在指定时长内自然念完，语速自然，口型与语音同步",
    "画面流畅无闪烁、无多余肢体、无扭曲变形、无穿模",
    "色彩自然、曝光正确、电影级质感、高清细节",
]

_DEFAULT_NEGATIVE_PROMPTS = [
    "字幕",
    "自动字幕",
    "水印",
    "logo",
    "图标",
    "错误文字",
    "乱码文字",
    "男女声错配",
    "中途换声",
    "五官崩坏",
    "脸部变形",
    "多余手指",
    "肢体扭曲",
    "闪烁",
    "画面抖动",
    "模糊",
    "低分辨率",
]


def _empty_copy_result(duration: int = 15, ratio: str = "9:16") -> dict:
    return {
        "overview": {"theme": "好物推荐", "total_duration": duration, "aspect_ratio": ratio},
        "scene_and_lighting": "简洁明亮的室内场景，柔和自然光，产品主体清晰",
        "shots": [],
        "hard_constraints": list(_DEFAULT_HARD_CONSTRAINTS),
        "negative_prompts": list(_DEFAULT_NEGATIVE_PROMPTS),
        "voiceover_script": "",
        "final_copy": "",
        "suggested_copy": "",
        "title": "",
    }


# ── 流水线各步骤 ────────────────────────────────────────────────────────


_IMAGE_ANALYSIS_SYSTEM_PROMPT = """你是电商商品视觉分析师，从商品图片中提取关键商品信息。严格规则：
1. 只说图片里真实可见的内容，看不清/没有的填「无法判断」，不要瞎猜。
2. 输出必须是严格 JSON（不要 Markdown 代码块，不要额外解释文字）。
3. 字段说明：
{
  "name": "商品全名（品牌+产品名+规格，如『大公鸡头管家 多功能油污净 625ml』，从包装 OCR 读出）",
  "brand": "品牌名（从 Logo/包装文字读出，看不清填『无法判断』）",
  "category": "商品品类（如『家用清洁/油污清洁剂』『日化/洗衣液』；非产品图填『非产品图』）",
  "appearance": "外观特征（50-100字：瓶身形状、颜色、瓶盖、标签颜色、尺寸感）",
  "packaging": "包装细节（50-100字：标签分区、图案元素、瓶盖/泵头样式、塑封状态）",
  "text_on_package": ["包装上清晰可见的文字列表（品牌、产品名、卖点、规格等，看不清的不列）"],
  "key_features": [
    "3-5 条图片中能看到的外观/视觉特征（如『红色瓶盖白色瓶身』『鸡头图案 Logo』等）"
  ],
  "scene": "图片场景（如白底棚拍/浴室实拍/桌面静物/手持实拍等）",
  "summary": "100-180字中文导购描述，连贯自然段落，像电商详情页介绍，前端直接展示，必须提到品牌/品名/核心外观特征，不能写『无法判断』"
}"""

_IMAGE_ANALYSIS_USER_PROMPT = """请分析这张商品图片，输出严格 JSON。重点：
1. name/brand/text_on_package 从图片包装 OCR 读取，不编造；
2. appearance/packaging 各写 50-100 字，要具体；
3. summary 必须是 100-180 字连贯中文段落，说清商品是什么、长什么样、适合谁用，不要写「无法判断」；
4. 非产品图时 category 填「非产品图」，name 填实际看到的内容；
5. 看不清的字段填「无法判断」。"""


def _vision_fallback(idx: int, reason: str, extra: dict | None = None) -> dict:
    d = {
        "name": "未识别",
        "category": "无法判断",
        "appearance": "无法判断",
        "packaging": "无法判断",
        "text_on_package": [],
        "key_features": [],
        "scene": "通用",
        "summary": "",
        "_source": reason,
    }
    if extra:
        d.update(extra)
    return d


def _is_vision_result_usable(result: dict) -> bool:
    """判断 VLM 返回是否有效：name/summary 不能为未识别/无法判断/空，summary 要够长。"""
    if not isinstance(result, dict):
        return False
    name = (result.get("name") or "").strip()
    if not name or name in ("未识别", "无法判断", "未知"):
        return False
    summary = (result.get("summary") or "").strip()
    if len(summary) < 30 or summary in ("无法判断", "未识别"):
        return False
    category = (result.get("category") or "").strip()
    if category == "非产品图":
        return True
    feats = result.get("key_features") or []
    if not isinstance(feats, list) or len(feats) == 0:
        return False
    return True


def _analyze_single_image(
    idx: int,
    img_url: str,
    vision_model: str,
    timeout: int,
    *,
    pro_fallback_model: str | None = None,
) -> dict:
    """单张图片 VLM 分析（线程池并行调用）。
    - lite 失败/结果不可用 时自动用 pro 模型降级重试 1 次。
    - 失败/None/不可用最终返回含默认字段的 dict（不会让用户看到「未识别·无法判断」裸结果）。
    """
    from packages.shared.ai_service import call_vision

    if not img_url or not isinstance(img_url, str):
        return _vision_fallback(idx, "invalid_url")

    def _call(model: str, tmo: int):
        try:
            return call_vision(
                image_url=img_url,
                prompt=_IMAGE_ANALYSIS_USER_PROMPT,
                model=model,
                max_tokens=800,
                temperature=0.1,
                timeout=tmo,
                system_prompt=_IMAGE_ANALYSIS_SYSTEM_PROMPT,
            )
        except Exception as e:
            logger.warning("[爆款视频] 图片 #%d call_vision(%s) 异常 err=%s", idx, model, e)
            return None

    def _normalize(raw, source: str) -> dict:
        if raw is None:
            return _vision_fallback(idx, f"{source}_none")
        if isinstance(raw, str):
            logger.warning("[爆款视频] 图片 #%d VLM(%s) 返回非 JSON: %s", idx, source, raw[:200])
            return _vision_fallback(idx, f"{source}_text", {"_raw": raw[:500]})
        if not isinstance(raw, dict):
            return _vision_fallback(idx, f"{source}_badtype")
        raw.setdefault("_source", source)
        raw.setdefault("name", "未识别")
        raw.setdefault("brand", "无法判断")
        raw.setdefault("category", "无法判断")
        raw.setdefault("appearance", "无法判断")
        raw.setdefault("packaging", "无法判断")
        raw.setdefault("text_on_package", [])
        raw.setdefault("key_features", [])
        raw.setdefault("scene", "通用")
        raw.setdefault("summary", "")
        if not isinstance(raw.get("text_on_package"), list):
            raw["text_on_package"] = []
        if not isinstance(raw.get("key_features"), list):
            raw["key_features"] = []
        return raw

    # 第一次：传入模型（通常是 lite）
    first_raw = _call(vision_model, timeout)
    tag1 = vision_model.split("/")[-1] if "/" in vision_model else vision_model
    first_result = _normalize(first_raw, tag1)
    if _is_vision_result_usable(first_result):
        return first_result

    logger.warning(
        "[爆款视频] 图片 #%d VLM(%s) 结果不可用 name=%r summary_len=%d，尝试 pro 降级",
        idx,
        vision_model,
        first_result.get("name"),
        len(first_result.get("summary") or ""),
    )

    # 第二次：pro 降级重试
    if pro_fallback_model and pro_fallback_model != vision_model:
        pro_raw = _call(pro_fallback_model, max(60, timeout))
        pro_result = _normalize(pro_raw, "pro_fallback")
        if _is_vision_result_usable(pro_result):
            pro_result["_fallback_used"] = True
            return pro_result
        logger.warning(
            "[爆款视频] 图片 #%d pro 降级仍不可用 name=%r summary_len=%d",
            idx,
            pro_result.get("name"),
            len(pro_result.get("summary") or ""),
        )
        return pro_result

    return first_result


def _step_image_analysis(job: ViralVideoJob) -> dict:
    """步骤 1: 图片 VLM 分析 — 识别产品特征（v1.6 优化：并行 + lite 模型提速）。"""
    try:
        from packages.shared.ai_service import call_vision  # noqa: F401
    except ImportError:
        logger.warning("[爆款视频] ai_service.call_vision 不可用，使用占位结果")
        return {"products": [_vision_fallback(0, "fallback_import_error")]}

    if not job.images:
        logger.warning("[爆款视频] 任务无 images，跳过图片分析")
        return {"products": []}

    # 选择视觉模型：lite 速度优先（默认），pro 作为降级备用
    try:
        _s = get_shared_settings()
        if _s.doubao_vision_use_lite:
            vision_model = _s.doubao_vision_lite_model
            pro_model = _s.doubao_vision_model
            vision_timeout = 45  # lite 给到 45s 避免首轮就 timeout 降级 pro
        else:
            vision_model = _s.doubao_vision_model
            pro_model = None  # 已经是 pro，不再降级
            vision_timeout = 60
    except Exception:
        vision_model = "doubao-1-5-vision-lite-250315"
        pro_model = "doubao-1-5-vision-pro-250328"
        vision_timeout = 45

    results: list[dict] = [None] * len(job.images)  # type: ignore
    max_workers = min(4, max(1, len(job.images)))
    logger.info(
        "[爆款视频] 开始并行图片分析 n=%d model=%s pro_fallback=%s timeout=%d workers=%d",
        len(job.images),
        vision_model,
        pro_model,
        vision_timeout,
        max_workers,
    )
    with ThreadPoolExecutor(max_workers=max_workers) as pool:
        future_to_idx = {
            pool.submit(
                _analyze_single_image, idx, url, vision_model, vision_timeout, pro_fallback_model=pro_model
            ): idx
            for idx, url in enumerate(job.images)
        }
        for fut in as_completed(future_to_idx):
            idx = future_to_idx[fut]
            try:
                results[idx] = fut.result()
            except Exception as e:
                logger.warning("[爆款视频] 图片 #%d future 异常 err=%s", idx, e, exc_info=True)
                results[idx] = _vision_fallback(idx, "future_exception", {"_error": str(e)[:200]})

    return {"products": results}


def _step_video_analysis(job: ViralVideoJob) -> dict | None:
    """步骤 1.5: 参考视频风格分析（可选）。"""
    if not job.reference_video_url:
        return None
    try:
        from viral_video.video_analyzer import analyze_video_style

        style_guide = analyze_video_style(job.reference_video_url)
        return style_guide if isinstance(style_guide, dict) else None
    except ImportError as e:
        logger.info("[爆款视频] video_analyzer 模块未就绪(%s)，使用占位风格分析", e)
        return {
            "cut_speed": "medium",
            "transition": "cross_dissolve",
            "energy": "medium",
            "color_grade": "neutral",
            "narrative": False,
            "source": "placeholder",
        }
    except Exception as e:
        logger.error("[爆款视频] 视频风格分析失败: %s", e)
        return {"error": str(e), "source": "failed"}


def _step_intent_parsing(job: ViralVideoJob, image_analysis: dict) -> dict:
    """步骤 2: 用户文案意图解析。"""
    try:
        from packages.shared.ai_service import call_llm
    except ImportError:
        return {"intent": "推广产品", "key_messages": ["产品亮点"], "tone": "专业", "suggested_title": ""}

    products_summary = ""
    products = (image_analysis or {}).get("products", []) or []
    for p in products:
        if not isinstance(p, dict):
            continue
        feats = p.get("key_features") or p.get("features") or []
        extras = []
        if p.get("brand") and p.get("brand") not in ("未知", "无法判断"):
            extras.append(f"品牌={p['brand']}")
        if p.get("category") and p.get("category") not in ("无法判断", "非产品图"):
            extras.append(f"品类={p['category']}")
        if p.get("colors"):
            extras.append(f"颜色={','.join(p['colors'])}")
        if p.get("visual_style"):
            extras.append(f"风格={p['visual_style']}")
        feat_str = ", ".join([str(x) for x in feats + extras])
        products_summary += f"- {p.get('name', '产品')}: {feat_str}\n"

    prompt = f"""你是一个营销编导。请分析以下信息，理解用户的营销意图并给出短视频主题建议：

用户原始文案：{job.user_copy_text or "（未提供，全由 AI 创作）"}
行业：{job.industry or "未指定"}
目标客户：{job.target_customer or "未指定"}
营销目的：{job.marketing_purpose or "未指定"}
视频时长：{job.duration}秒
产品信息：
{products_summary or "- （无图片分析结果）"}

请返回严格 JSON（不要 Markdown，不要解释）：
{{
  "intent": "核心营销意图（一句话）",
  "key_messages": ["要传达的3-5个关键信息"],
  "tone": "文案调性（如亲切/专业/高端/活力/治愈/搞笑）",
  "target_emotion": "希望触发的用户情感",
  "call_to_action": "行动号召短句（口语化，5-10字）",
  "suggested_title": "视频主题标题（5-15字）"
}}"""

    _s = get_shared_settings()
    _fast = _s.doubao_fast_model
    try:
        # 用快模型提速（结构化输出任务，不需要推理模型）
        result = call_llm(prompt, temperature=0.4, max_tokens=800, model=_fast)
        return (
            result
            if isinstance(result, dict)
            else {"intent": str(result)[:200], "key_messages": [], "tone": "专业", "suggested_title": ""}
        )
    except Exception as e:
        logger.warning("[爆款视频] 意图解析失败: %s", e)
        return {"intent": "推广产品", "key_messages": ["产品亮点"], "tone": "专业", "suggested_title": ""}


# ── 编导分镜脚本生成（核心，v1.6 新 prompt） ──────────────────────────────


# 人设 IP 类型 → 文案/出镜风格指导（前端下拉 10 个 IP 类型）
_PERSONA_STYLE_GUIDE = {
    "通用个人IP": "亲切自然、像朋友分享好物，第一人称口语化，不端着",
    "老板型IP": "沉稳大气、有行业格局感，适度使用『我做了XX年』『我一直坚持』等老板视角，语气自信不夸张",
    "专家型IP": "专业权威、讲原理和数据支撑，用词严谨，少用网梗，像行业专家做科普",
    "顾问型IP": "贴心周到、给建议给方案，多用『建议你』『可以试试』『我帮你梳理』",
    "创始人IP": "真诚有温度、讲品牌故事和创业初心，带点情怀和个人观点，不端老板架子",
    "创业者IP": "真实接地气、讲踩坑经验和创业心路，带点自嘲和韧劲，像身边的创业者朋友",
    "从业者经验派": "内行视角、讲行业内幕/实操经验/踩坑教训，多用『干了X年我发现』『内行都知道』",
    "避坑顾问型": "直接点出痛点和雷区，先讲『别买XX』『很多人踩过的坑』再给正确选择，节奏感强",
    "知识科普型": "清晰讲原理、讲知识点，条理分明、信息密度高，像做一期小科普",
    "测评种草型": "真实测评感、讲使用体验和优缺点对比，带『亲测』『我用了XX天』『实测下来』真实感词汇",
}


def _persona_style_hint(persona_id: str) -> str:
    """根据 persona_id 查文案风格指导；未命中/空值返回通用提示。"""
    pid = (persona_id or "").strip()
    if pid in _PERSONA_STYLE_GUIDE:
        return f"【人设风格：{pid}】{_PERSONA_STYLE_GUIDE[pid]}"
    if pid:
        # 前端传了自由值，照直提示，不阻塞
        return f"【人设风格：{pid}】按该人设的口吻、话术习惯组织口播和出镜动作"
    return "【人设风格：未指定】亲切自然、像朋友分享好物"


_SCRIPT_GENERATION_PROMPT = """你是资深短视频导演，为 Seedance 2.5（单次生成最多{duration}秒）写编导分镜脚本。脚本将整体作为 prompt 一次性传给视频模型，必须让模型在连贯镜头流中清楚每段时间拍什么、画面如何、人物说什么。

## 产品
{products_summary}

## 营销参数
- 主题/意图：{intent}
- 关键信息：{key_messages}
- 调性：{tone}
- 目标客户：{target_customer}
- 用户原始卖点（必须融入口播）：{user_copy}
- 时长：{duration}秒 / 画幅：{ratio} / 产品图：{n_images}张（第1张通常是主图/首帧）
- 风格参考：{style_hint}
- 爆款结构（必须严格遵循节奏/段落顺序）：{viral_structure_block}
- 人设/出镜口吻（必须贯穿全部对白和动作描写）：{persona_hint}

## 输出格式（必须输出严格 JSON，不要 Markdown，不要解释，字段一个都不能少）

```json
{{
  "overview": {{
    "theme": "视频主题（一句话概括）",
    "total_duration": {duration},
    "aspect_ratio": "{ratio}"
  }},
  "scene_and_lighting": "整体场景描述+光线设定（100-200字，要具体：在哪拍、什么光线、什么色调、什么氛围）",
  "shots": [
    {{
      "time_range": "0-3秒",
      "shot_type_angle_movement": "景别+角度+运镜（例：近景俯拍45度，缓慢推镜；中景平视，固定镜头；特写平视，快速拉镜）",
      "scene_and_dialogue": "画面场景描述 + 人物口播台词（对白要自然口语化，像朋友聊天，不要硬广推销腔）",
      "action_details": "人物动作、表情、物品操作细节（手怎么动、表情变化、产品怎么展示）",
      "audio_bgm": "环境音+BGM提示（例：轻快流行BGM，环境嘈杂咖啡店背景音）",
      "transition": "硬切/淡入淡出/叠化（最后一镜写『结束』即可）",
      "reference_image_index": 0
    }}
    // ... 按时间顺序列出所有镜头，总时长累计 = {duration} 秒
  ],
  "hard_constraints": [
    "无字幕、无水印、无任何自动生成文字、无logo",
    "同一人物全程五官、发型、服装、身材保持一致，不得换脸变形",
    "口播语音在总时长内自然念完，语速自然，口型与语音严格同步",
    "画面流畅无闪烁、无多余肢体、无扭曲变形、无穿模",
    "色彩自然、曝光正确、电影级质感、高清细节"
  ],
  "negative_prompts": [
    "字幕","自动字幕","水印","logo","图标","错误文字","乱码文字",
    "男女声错配","中途换声","五官崩坏","脸部变形","多余手指",
    "肢体扭曲","闪烁","画面抖动","模糊","低分辨率"
  ],
  "voiceover_script": "完整口播稿（把 shots 里所有对白自然拼接成一段，口语化，不加旁白标注、不加镜头标注、不加'主播：'之类前缀，就是纯念出来的文本，长度适配{duration}秒，约{approx_chars}字）"
}}
```

## 关键要求
1. 每镜写清景别/角度/运镜（特写/近景/中景+平视/俯拍+推/拉/固定）。
2. 画面具体：主体（性别/年龄/穿着）、场景、动作、光线、镜头运动要可落地。
3. 对白自然口语化，像朋友分享好物；拒绝"家人们""宝子们""太好用了"等浮夸/硬广腔。
4. reference_image_index 填 0-based 索引（产品特写用索引0主图），人像/场景可 null。
5. shots time_range 累计={duration}秒，单镜2-8秒。
6. hard_constraints/negative_prompts 保留默认项可追加，不要删减。
7. voiceover_script 为纯口播文本（无标记/括号/前缀），{duration}秒约{approx_chars}字。
8. 严格按上方「爆款结构」的节奏/段落顺序编排（钩子/痛点/反转/案例/行动号召与结构对齐）。
9. 输出前自检：口播对白禁止错别字和语病，**严禁使用"很近"，正确用词是"最近"**（指"最近一段时间/最近在用"，绝不能写成"很近"）；其他同音字、形近字错误一律修正。
10. 必须使用产品信息中真实的品牌、品名和外观特征，不要编造与产品无关的内容。"""


def _build_products_summary(image_analysis: dict) -> str:
    """把 VLM 返回的商品分析结果拼给文案/分镜生成 prompt 用。
    优先用 summary（自然段落）；没有时用结构化字段兜底拼一段。"""
    products = (image_analysis or {}).get("products", []) or []
    if not products:
        return "- （无图片信息，请自由创作自然生活化场景）"
    lines = []
    for i, p in enumerate(products):
        if not isinstance(p, dict):
            continue
        name = p.get("name") or "产品"
        # 优先 VLM 生成的 summary 段（自然语言，给编导模型看效果最好）
        summary = (p.get("summary") or "").strip()
        if summary and len(summary) >= 30:
            lines.append(f"- 图{i+1} {name}：{summary}")
            continue
        # 结构化字段兜底
        brand = p.get("brand") or ""
        cat = p.get("category") or ""
        spec = p.get("spec") or ""
        appearance = p.get("appearance") or ""
        packaging = p.get("packaging") or ""
        colors = p.get("colors") or []
        mat = p.get("material_or_texture") or ""
        style = p.get("visual_style") or ""
        scene = p.get("scene") or ""
        audience = p.get("target_audience") or p.get("target_audience_hint") or ""
        # text_on_package 可能是数组（新格式）或字符串（旧格式）
        text_list = p.get("text_on_package") or []
        if isinstance(text_list, str):
            text_on_img = text_list
        else:
            text_on_img = "；".join([str(x) for x in text_list[:8]]) if text_list else (p.get("text_on_image") or "")
        feats = p.get("key_features") or p.get("features") or []
        sellings = p.get("selling_points") or []
        scenes = p.get("suitable_scenes") or []
        parts = [f"图{i+1} {name}"]
        if brand and brand not in ("未知", "无法判断"):
            parts.append(f"品牌={brand}")
        if cat and cat not in ("无法判断", "非产品图"):
            parts.append(f"品类={cat}")
        if spec and spec != "无法判断":
            parts.append(f"规格={spec}")
        if appearance and appearance != "无法判断":
            parts.append(f"外观={appearance}")
        if packaging and packaging != "无法判断":
            parts.append(f"包装={packaging}")
        if colors:
            parts.append(f"颜色={','.join(colors)}")
        if mat and mat != "无法判断":
            parts.append(f"材质={mat}")
        if style:
            parts.append(f"风格={style}")
        if scene and scene not in ("通用",):
            parts.append(f"展示场景={scene}")
        if scenes:
            parts.append(f"适用场景={','.join([str(x) for x in scenes[:4]])}")
        if audience and audience not in ("通用", "无法判断"):
            parts.append(f"目标人群={audience}")
        if text_on_img and text_on_img not in ("无",):
            parts.append(f"包装文字={text_on_img[:300]}")
        if feats:
            parts.append("外观特征=" + "；".join([str(x) for x in feats[:6]]))
        if sellings:
            parts.append("营销卖点=" + "；".join([str(x) for x in sellings[:5]]))
        lines.append("- " + "，".join(parts))
    return "\n".join(lines)


def _safe_json_loads(raw: str | dict | list | None):
    if raw is None:
        return None
    if isinstance(raw, (dict, list)):
        return raw
    if not isinstance(raw, str):
        return None
    s = raw.strip()
    if s.startswith("```"):
        s = s.strip("`")
        if s.startswith("json"):
            s = s[4:].lstrip()
    try:
        return json.loads(s)
    except Exception:
        # 尝试截取第一个 { ... } 或 [ ... ]
        try:
            for open_c, close_c in (("{", "}"), ("[", "]")):
                i = s.find(open_c)
                j = s.rfind(close_c)
                if i >= 0 and j > i:
                    return json.loads(s[i : j + 1])
        except Exception:
            pass
    return None


def _fallback_script(job: ViralVideoJob) -> dict:
    """脚本生成失败时的兜底脚本（极简但可用）。"""
    dur = max(5, min(30, int(getattr(job, "duration", 15) or 15)))
    ratio = getattr(job, "video_ratio", None) or "9:16"
    base = _empty_copy_result(dur, ratio)
    voiceover = job.user_copy_text or "你好，给大家分享一款我最近在用的好物，真的很不错，推荐你们也试试。"
    shots = [
        {
            "time_range": f"0-{dur}秒",
            "shot_type_angle_movement": "中景平视，缓慢推镜",
            "scene_and_dialogue": "明亮室内，人物自然出镜，微笑着看向镜头。" + voiceover,
            "action_details": "人物手持产品自然展示，表情亲切，动作流畅",
            "audio_bgm": "轻快流行BGM",
            "transition": "结束",
            "reference_image_index": 0 if job.images else None,
        }
    ]
    base["shots"] = shots
    base["voiceover_script"] = voiceover
    base["final_copy"] = voiceover
    base["suggested_copy"] = voiceover
    base["title"] = "好物分享"
    return base


def _validate_and_normalize_script(raw, job: ViralVideoJob) -> dict:
    """把 LLM 返回的脚本规范化、补默认、校验结构。"""
    dur = max(5, min(30, int(getattr(job, "duration", 15) or 15)))
    ratio = getattr(job, "video_ratio", None) or "9:16"
    base = _empty_copy_result(dur, ratio)

    if not isinstance(raw, dict):
        logger.warning("[爆款视频] 脚本返回非 dict，使用兜底")
        return _fallback_script(job)

    # overview
    ov = raw.get("overview")
    if isinstance(ov, dict):
        base["overview"] = {
            "theme": str(ov.get("theme") or "好物分享"),
            "total_duration": int(ov.get("total_duration") or dur),
            "aspect_ratio": str(ov.get("aspect_ratio") or ratio),
        }
    else:
        base["overview"]["theme"] = str(raw.get("title") or "好物分享")

    base["scene_and_lighting"] = str(raw.get("scene_and_lighting") or base["scene_and_lighting"])

    # shots
    shots_raw = raw.get("shots")
    shots: list[dict] = []
    if isinstance(shots_raw, list):
        for i, s in enumerate(shots_raw):
            if not isinstance(s, dict):
                continue
            shots.append(
                {
                    "time_range": str(s.get("time_range") or f"{i*3}-{(i+1)*3}秒"),
                    "shot_type_angle_movement": str(s.get("shot_type_angle_movement") or "中景平视，固定镜头"),
                    "scene_and_dialogue": str(s.get("scene_and_dialogue") or ""),
                    "action_details": str(s.get("action_details") or ""),
                    "audio_bgm": str(s.get("audio_bgm") or "轻快BGM"),
                    "transition": str(s.get("transition") or ("硬切" if i < len(shots_raw) - 1 else "结束")),
                    "reference_image_index": s.get("reference_image_index"),
                }
            )
    if not shots:
        shots = [
            {
                "time_range": f"0-{dur}秒",
                "shot_type_angle_movement": "中景平视，缓慢推镜",
                "scene_and_dialogue": "明亮室内场景，人物自然出镜。",
                "action_details": "自然展示产品",
                "audio_bgm": "轻快BGM",
                "transition": "结束",
                "reference_image_index": 0 if job.images else None,
            }
        ]
    base["shots"] = shots

    # hard_constraints / negative_prompts
    hc = raw.get("hard_constraints")
    if isinstance(hc, list) and hc:
        merged = list(_DEFAULT_HARD_CONSTRAINTS)
        for x in hc:
            if isinstance(x, str) and x and x not in merged:
                merged.append(x)
        base["hard_constraints"] = merged
    np = raw.get("negative_prompts")
    if isinstance(np, list) and np:
        merged = list(_DEFAULT_NEGATIVE_PROMPTS)
        for x in np:
            if isinstance(x, str) and x and x not in merged:
                merged.append(x)
        base["negative_prompts"] = merged

    # voiceover_script: 优先从字段取，否则从各镜 scene_and_dialogue 提取（粗暴拼接冒号后部分 / 中文句）
    voiceover = str(raw.get("voiceover_script") or "").strip()
    if not voiceover:
        # 兜底：把所有 scene_and_dialogue 拼接起来，去除镜头描述部分（含"景"、"俯拍"、"平视"等词的前缀）
        import re

        parts = []
        for s in shots:
            txt = s.get("scene_and_dialogue", "")
            # 去除开头到第一个句号/逗号前的"镜头描述"部分
            # 简单策略：找第一个中文说话片段——按句号切，后半段更像对白
            segs = re.split(r"[。！？]", txt)
            for seg in segs:
                seg = seg.strip(" ，,。.!?！？：:")
                if len(seg) >= 4 and not any(
                    k in seg for k in ("景别", "俯拍", "仰拍", "平视", "镜头", "特写", "中景", "全景", "近景", "运镜")
                ):
                    parts.append(seg)
        voiceover = "。".join(parts) if parts else (job.user_copy_text or "你好，给大家分享一款好物。")
    base["voiceover_script"] = voiceover
    base["final_copy"] = voiceover
    base["suggested_copy"] = voiceover
    base["title"] = base["overview"]["theme"]
    return base


def _step_script_generation(job: ViralVideoJob, intent: dict, image_analysis: dict) -> dict:
    """步骤 3: 编导分镜脚本生成（v1.6 核心，输出 copy_result 结构）。"""
    try:
        from packages.shared.ai_service import call_llm
    except ImportError:
        return _fallback_script(job)

    products_summary = _build_products_summary(image_analysis)
    style_hint = "无"
    if isinstance(job.style_guide, dict):
        style_hint = (
            f"节奏{job.style_guide.get('cut_speed','')}、转场{job.style_guide.get('transition','')}、"
            f"色调{job.style_guide.get('color_grade','')}、能量{job.style_guide.get('energy','')}"
        )

    dur = max(5, min(30, int(getattr(job, "duration", 15) or 15)))
    ratio = getattr(job, "video_ratio", None) or "9:16"
    approx_chars = max(20, dur * 4)

    intent_str = ""
    key_msgs = ""
    tone = ""
    if isinstance(intent, dict):
        intent_str = intent.get("intent") or "推广产品"
        key_msgs = "、".join(intent.get("key_messages") or [])
        tone = intent.get("tone") or "亲切自然"
    else:
        intent_str = "推广产品"
        tone = "亲切自然"

    # 爆款结构：用户在 STEP1/STEP2 选的中文结构名，必须严格注入 prompt 指导 AI 编排
    _vs = (job.viral_structure or "").strip()
    if _vs:
        viral_structure_block = f"【{_vs}】—— 请严格按照这个爆款结构的节奏/段落顺序编排镜头、台词和情绪节点（开场钩子、痛点、反转、案例、行动号召等按结构走），不要打乱顺序"
    else:
        viral_structure_block = "未指定（自由编排，但仍需有钩子开头+产品展示+行动号召的基本节奏）"

    persona_hint = _persona_style_hint(getattr(job, "persona_id", ""))
    prompt = _SCRIPT_GENERATION_PROMPT.format(
        products_summary=products_summary,
        intent=intent_str,
        key_messages=key_msgs or "产品亮点",
        tone=tone,
        target_customer=job.target_customer or "通用人群",
        user_copy=job.user_copy_text or "（未提供，自由创作）",
        duration=dur,
        ratio=ratio,
        n_images=len(job.images or []),
        style_hint=style_hint,
        approx_chars=approx_chars,
        viral_structure_block=viral_structure_block,
        persona_hint=persona_hint,
    )

    _s = get_shared_settings()
    _fast = _s.doubao_fast_model
    _pro = getattr(_s, "doubao_model", None) or _fast

    def _try_gen(model: str, temp: float, max_tok: int, label: str):
        logger.info("[爆款视频] 编导脚本生成 model=%s label=%s", model, label)
        r = call_llm(prompt, temperature=temp, max_tokens=max_tok, model=model)
        if r is None:
            logger.warning("[爆款视频] 编导脚本返回None label=%s", label)
            return None
        parsed = _safe_json_loads(r)
        normalized = _validate_and_normalize_script(parsed, job)
        voiceover = (normalized or {}).get("voiceover_script") or ""
        voiceover_len = len(voiceover)
        shots_cnt = len((normalized or {}).get("shots") or [])
        # 判定是否"退化到兜底质量"：口播过短（<20字）或镜头数<1；正常的短口播（如15s视频~40字）不视为兜底
        # v1.6.1 双保险：先做 hard fix 字符串替换（"很近" → "最近"），再做不合格判定
        if "很近" in voiceover:
            logger.warning("[爆款视频] 编导脚本含错别字'很近'，hard fix 替换为'最近' label=%s", label)
            voiceover = voiceover.replace("很近", "最近")
            normalized["voiceover_script"] = voiceover
            # 同时在 shots 对白里替换
            for sh in normalized.get("shots") or []:
                if isinstance(sh, dict):
                    sd = sh.get("scene_and_dialogue") or ""
                    if "很近" in sd:
                        sh["scene_and_dialogue"] = sd.replace("很近", "最近")
        fallback_marker = "我最近在用的好物" in voiceover  # _fallback_script 的特征串
        has_typo_henjin = "很近" in voiceover  # v1.6.1: 错别字"很近"视为不合格，触发重试
        is_fallback = fallback_marker or shots_cnt < 1 or voiceover_len < 20 or has_typo_henjin
        logger.info(
            "[爆款视频] 编导脚本结果 label=%s voiceover_len=%d shots=%d fallback=%s raw_type=%s",
            label,
            voiceover_len,
            shots_cnt,
            is_fallback,
            type(r).__name__,
        )
        if is_fallback:
            return None  # 触发重试
        return normalized

    try:
        # 第一次：快模型
        normalized = _try_gen(_fast, 0.8, 2500, "fast-first")
        if normalized is not None:
            return normalized
        # 第二次：快模型降温度+加大 max_tokens
        normalized = _try_gen(_fast, 0.6, 3200, "fast-retry")
        if normalized is not None:
            return normalized
        # 第三次：用主力模型兜底
        if _pro and _pro != _fast:
            normalized = _try_gen(_pro, 0.7, 3500, "pro-fallback")
            if normalized is not None:
                return normalized
        logger.warning("[爆款视频] 编导脚本三次都未生成合格结果，使用兜底脚本")
        return _fallback_script(job)
    except Exception as e:
        logger.warning("[爆款视频] 编导脚本生成异常: %s，使用兜底脚本", e, exc_info=True)
        return _fallback_script(job)


def _step_review(job: ViralVideoJob, copy_result: dict) -> dict:
    """步骤 4: 合规审核（简化版：基于脚本的 voiceover_script+shots 文本）。"""
    dimensions = ["广告法合规", "平台规范", "内容真实性", "版权安全", "价值观", "风格一致性"]
    try:
        from packages.shared.ai_service import call_llm
    except ImportError:
        return {"passed": True, "score": 90, "details": {d: "通过" for d in dimensions}}

    voiceover = (copy_result or {}).get("voiceover_script", "")
    shots_preview = json.dumps((copy_result or {}).get("shots", [])[:3], ensure_ascii=False)
    prompt = f"""请对以下短视频编导脚本进行合规审核，检查6个维度：{", ".join(dimensions)}

口播文案：{voiceover}
前3个镜头：{shots_preview}
行业：{job.industry}

请以JSON格式返回：
- passed: bool（是否全部通过）
- score: int（0-100分）
- details: 各维度评分和说明
- issues: 需要修改的问题列表（如有）"""
    _s = get_shared_settings()
    _fast = _s.doubao_fast_model
    try:
        # 合规审核用快模型 + 短输出（结构化判断）
        result = call_llm(prompt, temperature=0.1, max_tokens=500, model=_fast)
        return result if isinstance(result, dict) else {"passed": True, "score": 80, "details": {}}
    except Exception as e:
        logger.warning("[爆款视频] 合规审核失败: %s", e)
        return {"passed": True, "score": 75, "details": {d: "默认通过" for d in dimensions}}


def _step_tts(job: ViralVideoJob, voiceover_script: str):
    """步骤 5: CosyVoice 整段配音 → 返回本地 MP3 Path；失败返回 None。"""
    try:
        from pathlib import Path as _Path

        from apps.worker.services.tts_service_factory import get_tts_service

        tts_service = get_tts_service()
        voice_id = (getattr(job, "voice_id", "") or "").strip()
        text = (voiceover_script or "").strip()
        if not text:
            logger.warning("[爆款视频] voiceover_script 为空，跳过 TTS")
            return None
        try:
            result = tts_service.synthesize(
                text=text,
                voice_id=voice_id or "longxiaochun_v3",
                format="mp3",
            )
        except TypeError:
            try:
                result = tts_service.synthesize(text=text, voice_id=voice_id or "longxiaochun_v3")
            except TypeError:
                result = tts_service.synthesize(text=text)
        if result is None:
            return None
        p = _Path(result) if not isinstance(result, _Path) else result
        if p.exists() and p.stat().st_size > 0:
            logger.info(
                "[爆款视频] TTS 合成完成: voice=%s path=%s size=%d", voice_id or "longxiaochun_v3", p, p.stat().st_size
            )
            return p
        logger.warning("[爆款视频] TTS 返回路径不存在或空文件: %s", p)
        return None
    except Exception as e:
        logger.warning("[爆款视频] TTS 配音失败: %s", e, exc_info=True)
        return None


def _upload_tts_to_oss(job: ViralVideoJob, tts_path) -> str | None:
    """把 TTS 本地 mp3 上传到 OSS，返回公网 URL（供 Seedance 做 reference_audios 口型驱动用）。"""
    if tts_path is None:
        return None
    try:
        from video_processing.oss_helpers import upload_to_oss

        local = Path(tts_path) if not isinstance(tts_path, Path) else tts_path
        if not local.exists():
            return None
        storage_key = f"generated/viral-video/{job.user_id}/{job.id}/tts_voiceover.mp3"
        url = upload_to_oss(local, storage_key)
        if url:
            logger.info("[爆款视频] TTS 音频已上传 OSS: %s", url[:160])
        return url
    except Exception as e:
        logger.warning("[爆款视频] TTS 上传 OSS 失败: %s", e, exc_info=True)
        return None


def _assemble_seedance_prompt(copy_result: dict, job: ViralVideoJob) -> str:
    """把编导脚本拼成 Seedance 长 prompt。"""
    if not isinstance(copy_result, dict) or not copy_result:
        return "产品展示短视频，清晰明亮，自然讲解"
    ov = copy_result.get("overview") or {}
    theme = ov.get("theme", "")
    total_duration = ov.get("total_duration") or getattr(job, "duration", 15)
    aspect_ratio = ov.get("aspect_ratio") or getattr(job, "video_ratio", "9:16")
    scene_lighting = copy_result.get("scene_and_lighting", "")
    shots = copy_result.get("shots") or []
    hc = copy_result.get("hard_constraints") or _DEFAULT_HARD_CONSTRAINTS
    np = copy_result.get("negative_prompts") or _DEFAULT_NEGATIVE_PROMPTS

    lines: list[str] = []
    lines.append("【视频总览】")
    lines.append(f"- 整体主题：{theme}")
    lines.append(f"- 总时长：{total_duration}秒（单次生成，时长必须严格匹配）")
    lines.append(f"- 画幅：{aspect_ratio}")
    lines.append("")
    lines.append("【场景与光线】")
    lines.append(scene_lighting)
    lines.append("")
    lines.append("【逐镜头时间轴】（按时间顺序连贯拍摄，镜头之间自然衔接）")
    for i, s in enumerate(shots):
        if not isinstance(s, dict):
            continue
        tr = s.get("time_range", "")
        cam = s.get("shot_type_angle_movement", "")
        sd = s.get("scene_and_dialogue", "")
        act = s.get("action_details", "")
        ab = s.get("audio_bgm", "")
        t = s.get("transition", "")
        ref = s.get("reference_image_index")
        lines.append(f"- 镜头{i+1}（{tr}）：")
        lines.append(f"  景别/运镜：{cam}")
        lines.append(f"  画面与对白：{sd}")
        lines.append(f"  动作细节：{act}")
        lines.append(f"  音效/BGM：{ab}")
        lines.append(f"  转场：{t}")
        if ref is not None and isinstance(ref, int):
            lines.append(f"  参考图片：第{ref+1}张产品图")
    lines.append("")
    lines.append("【硬性约束】")
    for c in hc:
        lines.append(f"- {c}")
    lines.append("")
    lines.append("【负面提示词】（必须避免）")
    lines.append("，".join([str(x) for x in np if x]))
    return "\n".join(lines)


def _step_render(job: ViralVideoJob, copy_result: dict, tts_audio_url: str | None) -> tuple[str, dict | None]:
    """步骤 6: v1.6 单次 Seedance 生成（不再分段/拼接）。

    返回 (本地视频路径, usage dict|None)。失败抛异常。
    """
    from packages.shared.ai_service import call_video_generation

    prompt = _assemble_seedance_prompt(copy_result, job)
    dur = max(5, min(30, int(getattr(job, "duration", 15) or 15)))
    ratio = getattr(job, "video_ratio", None) or "9:16"
    model = getattr(job, "video_model", "") or None
    resolution = getattr(job, "video_resolution", "720p") or "720p"

    # reference_audios: TTS 音频驱动口型
    ref_audios = [tts_audio_url] if tts_audio_url else []
    # reference_images: 产品图（除首帧外的其他图作为多参考；首帧通过 image_url 传）
    images = list(job.images or [])
    first_image = images[0] if images else None
    rest_images = images[1:30] if len(images) > 1 else []
    # reference_videos: 参考视频（可选）
    ref_videos = [job.reference_video_url] if getattr(job, "reference_video_url", "") else []

    tmpdir = Path(tempfile.mkdtemp(prefix=f"viral_{job.id}_"))
    logger.info(
        "[爆款视频] 开始单次 Seedance 生成 dur=%ds ratio=%s model=%s ref_imgs=%d ref_audios=%d ref_videos=%d tmpdir=%s",
        dur,
        ratio if not first_image else "(follow-image)",
        model or "default",
        len(rest_images) + (1 if first_image else 0),
        len(ref_audios),
        len(ref_videos),
        tmpdir,
    )
    logger.info("[爆款视频] Seedance prompt (前300字): %s", prompt[:300])

    result = call_video_generation(
        prompt=prompt,
        image_url=first_image,
        duration=dur,
        ratio=ratio,
        resolution=resolution,
        output_dir=str(tmpdir),
        model=model,
        generate_audio=True,  # Seedance 原生生成环境音效/BGM；口型由 reference_audios 的 TTS 驱动
        reference_images=rest_images,
        reference_audios=ref_audios,
        reference_videos=ref_videos,
    )
    if not result or not isinstance(result, dict):
        raise RuntimeError("Seedance 视频生成失败：返回为空")
    video_path = result.get("video_path") or ""
    usage = result.get("usage")
    if not video_path or not Path(video_path).exists() or Path(video_path).stat().st_size == 0:
        raise RuntimeError("Seedance 视频生成失败：返回空文件或路径不存在")
    logger.info(
        "[爆款视频] Seedance 单次生成完成: %s size=%d usage=%s", video_path, Path(video_path).stat().st_size, usage
    )
    return str(video_path), (usage if isinstance(usage, dict) else None)


def _step_upload(job: ViralVideoJob, video_path: str) -> str:
    """步骤 7: OSS 上传。"""
    from video_processing.oss_helpers import upload_to_oss

    local = Path(video_path)
    storage_key = f"generated/viral-video/{job.user_id}/{job.id}/{local.name}"
    logger.info("[爆款视频] 开始上传成片: local=%s key=%s size=%d", local, storage_key, local.stat().st_size)
    video_url = upload_to_oss(local, storage_key)
    if not video_url:
        raise RuntimeError(f"OSS 上传失败: storage_key={storage_key}")
    return video_url


def _wait_oss_ready(url: str, timeout_sec: int = 10) -> bool:
    """轮询 OSS 公网 URL，直到 HEAD 返回 200 或超时。
    用于缓解 OSS 上传后 1-5s 公网 eventual consistency 导致的 NoSuchKey。
    """
    import httpx

    deadline = time.monotonic() + timeout_sec
    last_status = 0
    while time.monotonic() < deadline:
        try:
            r = httpx.head(url, follow_redirects=True, timeout=3.0)
            last_status = r.status_code
            if r.status_code == 200 and int(r.headers.get("content-length", "0") or 0) > 0:
                return True
        except Exception as e:
            logger.debug("[爆款视频] OSS head 轮询失败: %s", e)
        time.sleep(1.0)
    logger.warning("[爆款视频] OSS 成片在 %ds 内未就绪 last_status=%s url=%s", timeout_sec, last_status, url[:120])
    return False


# ── 主编排器 ────────────────────────────────────────────────────────────


@shared_task(
    bind=True,
    max_retries=2,
    name="worker.run_viral_video_pipeline",
    soft_time_limit=900,  # 15min（完整流水线）
    time_limit=960,
)
def run_viral_video_pipeline(self: Task, job_id: str) -> dict:
    """旧一键流水线（保留兼容）：图片分析→风格分析→意图解析→WAIT_USER_CONFIRM。"""
    session = None
    _hb_stop = None
    try:
        _recover_stale_jobs()  # 顺带回收僵尸任务
        session, repo, job = _get_repo_and_job(job_id)
        if job is None:
            return {"ok": False, "error": "job not found"}

        job.mark_running()
        _save_job(repo, job, session)
        _hb_stop, _hb_thread = _start_heartbeat_thread(job_id)
        _set_stage(job, repo, session, ViralVideoStage.IMAGE_ANALYSIS, "正在分析商品特征...")

        image_analysis = _step_image_analysis(job)
        job.image_analysis = image_analysis
        _save_job(repo, job, session)
        _emit_progress(job_id, ViralVideoStage.IMAGE_ANALYSIS, 15.0, "图片分析完成", {"result": image_analysis})

        style_guide = None
        if job.reference_video_url or job.style_template_id:
            _set_stage(job, repo, session, ViralVideoStage.VIDEO_ANALYSIS, "正在分析参考视频风格...")
            style_guide = _step_video_analysis(job)
            job.style_guide = style_guide
            _save_job(repo, job, session)
            _emit_progress(job_id, ViralVideoStage.VIDEO_ANALYSIS, 25.0, "风格分析完成", {"style_guide": style_guide})

        _set_stage(job, repo, session, ViralVideoStage.INTENT_PARSING, "正在解析文案意图...")
        intent_result = _step_intent_parsing(job, image_analysis)

        job.mark_wait_user_confirm(intent_result)
        job.current_stage = ViralVideoStage.INTENT_PARSING
        job.phase_message = "意图解析完成，等待用户确认"
        _save_job(repo, job, session)
        _emit_progress(
            job_id,
            ViralVideoStage.INTENT_PARSING,
            35.0,
            "意图解析完成，等待用户确认",
            {"intent_result": intent_result, "waiting_confirm": True},
        )
        _emit_progress(
            job_id,
            ViralVideoStage.INTENT_PARSING,
            35.0,
            "等待用户确认意图文案",
            {"intent_result": intent_result},
            event_type="viral_video:wait_user",
        )
        return {"ok": True, "job_id": job_id, "status": "wait_user_confirm", "intent_result": intent_result}

    except Retry:
        raise
    except Exception as e:
        logger.error("[爆款视频] 流水线异常: %s", e, exc_info=True)
        _mark_failed_and_notify(job_id, session, None, None, str(e), "")
        return {"ok": False, "job_id": job_id, "error": str(e)}
    finally:
        if _hb_stop is not None:
            _hb_stop.set()
        if session:
            session.close()


@shared_task(bind=True, max_retries=2, name="worker.resume_viral_video_pipeline")
def resume_viral_video_pipeline(self: Task, job_id: str) -> dict:
    """旧 confirm-intent 路径兼容：从 WAIT_USER_CONFIRM 跑完整个渲染。"""
    session = None
    try:
        session, repo, job = _get_repo_and_job(job_id)
        if job is None:
            return {"ok": False, "error": "job not found"}
        if job.status != ViralVideoStatus.RUNNING:
            return {"ok": False, "error": f"unexpected status: {job.status}"}
        return _run_render_pipeline(job_id, session, repo, job)
    except Retry:
        raise
    except Exception as e:
        logger.error("[爆款视频] 恢复流水线异常: %s", e, exc_info=True)
        _mark_failed_and_notify(job_id, session, None, None, str(e), "")
        return {"ok": False, "job_id": job_id, "error": str(e)}
    finally:
        if session:
            session.close()


@shared_task(bind=True, max_retries=1, name="worker.run_video_style_analysis")
def run_video_style_analysis(self: Task, job_id: str) -> dict:
    """独立的视频风格分析任务。"""
    session = None
    try:
        session, repo, job = _get_repo_and_job(job_id)
        if job is None:
            return {"ok": False, "error": "job not found"}
        _emit_progress(job_id, ViralVideoStage.VIDEO_ANALYSIS, 10.0, "正在分析参考视频风格...")
        style_guide = _step_video_analysis(job)
        job.style_guide = style_guide
        _save_job(repo, job, session)
        _emit_progress(job_id, ViralVideoStage.VIDEO_ANALYSIS, 100.0, "风格分析完成", {"style_guide": style_guide})
        return {"ok": True, "job_id": job_id, "style_guide": style_guide}
    except Retry:
        raise
    except Exception as e:
        logger.error("[爆款视频] 风格分析失败: %s", e)
        return {"ok": False, "job_id": job_id, "error": str(e)}
    finally:
        if session:
            session.close()


# ── 失败处理 ────────────────────────────────────────────────────────────


def _mark_failed_and_notify(job_id: str, session, repo, job, err_msg: str, stage: str = "") -> None:
    try:
        if session is None:
            session = SessionLocal()
            repo = SQLAlchemyViralVideoJobRepository(session)
            job = repo.get(job_id)
        if job is not None and not job.is_terminal:
            job.mark_failed(err_msg)
            _save_job(repo, job, session)
    except Exception as inner:
        logger.warning("[爆款视频] 标记失败状态时出错: %s", inner)
    _emit_progress(
        job_id,
        stage,
        0,
        f"任务失败: {err_msg}",
        {"error": err_msg},
        event_type="viral_video:failed",
    )


# ── v1.5/v1.6 三步分步流水线 Celery 任务 ─────────────────────────────────


@shared_task(
    bind=True,
    max_retries=1,
    name="worker.run_viral_video_analyze",
    soft_time_limit=240,  # 4min（VLM 并行分析）
    time_limit=300,
)
def run_viral_video_analyze(self: Task, job_id: str) -> dict:
    """v1.5+ 阶段1：图片 VLM 分析 + 可选视频风格分析。"""
    session = None
    _hb_stop = None
    try:
        _recover_stale_jobs()  # 顺带回收僵尸任务
        session, repo, job = _get_repo_and_job(job_id)
        if job is None:
            return {"ok": False, "error": "job not found"}

        job.mark_running()
        _save_job(repo, job, session)
        _hb_stop, _hb_thread = _start_heartbeat_thread(job_id)
        _set_stage(job, repo, session, ViralVideoStage.IMAGE_ANALYSIS, "正在分析商品特征...")

        image_analysis = _step_image_analysis(job)
        job.image_analysis = image_analysis
        _save_job(repo, job, session)
        _emit_progress(job_id, ViralVideoStage.IMAGE_ANALYSIS, 60.0, "图片分析完成", {"result": image_analysis})

        style_guide = None
        if job.reference_video_url or job.style_template_id:
            _set_stage(job, repo, session, ViralVideoStage.VIDEO_ANALYSIS, "正在分析参考视频风格...")
            style_guide = _step_video_analysis(job)
            job.style_guide = style_guide
            _save_job(repo, job, session)
            _emit_progress(
                job_id,
                ViralVideoStage.VIDEO_ANALYSIS,
                90.0,
                "风格分析完成",
                {"style_analyzed": True, "style_guide": style_guide},
            )

        job.mark_image_analyzed()
        job.current_stage = ViralVideoStage.IMAGE_ANALYSIS
        job.phase_message = "图片分析完成，请填写营销参数以生成编导脚本"
        _save_job(repo, job, session)
        _emit_progress(
            job_id,
            ViralVideoStage.IMAGE_ANALYSIS,
            100.0,
            "图片分析完成，请填写营销参数以生成编导脚本",
            {"image_analysis": image_analysis, "status": "image_analyzed"},
            event_type="viral_video:image_analyzed",
        )
        return {"ok": True, "job_id": job_id, "status": "image_analyzed", "image_analysis": image_analysis}

    except Retry:
        raise
    except Exception as e:
        logger.error("[爆款视频][阶段1] 异常: %s", e, exc_info=True)
        _mark_failed_and_notify(job_id, session, None, None, str(e), ViralVideoStage.IMAGE_ANALYSIS)
        return {"ok": False, "job_id": job_id, "error": str(e)}
    finally:
        if _hb_stop is not None:
            _hb_stop.set()
        if session:
            session.close()


@shared_task(
    bind=True,
    max_retries=1,
    name="worker.run_viral_video_generate_copy",
    soft_time_limit=180,  # 3min（编导脚本生成含三级重试）
    time_limit=240,
)
def run_viral_video_generate_copy(self: Task, job_id: str) -> dict:
    """v1.6 阶段2（v1.6.1 提速版）：意图解析 → 编导分镜脚本生成 → 直接返回，合规审核后置到出片前。

    优化点（#2134 问题7）：
    - 意图解析/编导脚本均使用快模型（doubao_fast_model，非推理模型），max_tokens 收紧
    - _SCRIPT_GENERATION_PROMPT 精简冗余描述
    - 合规审核改为异步后置：不阻塞前端，在 confirm-copy（阶段3 TTS前）再做最终审核
    - 每个阶段通过 _set_stage 持久化 current_stage/phase_message 到 DB（问题8）
    - v1.6.1: lite VLM 15s 快速失败，pro 25s；三级编导重试
    """
    session = None
    _hb_stop = None
    try:
        _recover_stale_jobs()  # 顺带回收僵尸任务
        session, repo, job = _get_repo_and_job(job_id)
        if job is None:
            return {"ok": False, "error": "job not found"}
        if job.status != ViralVideoStatus.RUNNING:
            return {"ok": False, "error": f"unexpected status: {job.status}"}
        job.touch_heartbeat()
        _save_job(repo, job, session)
        _hb_stop, _hb_thread = _start_heartbeat_thread(job_id)

        # 阶段：意图解析
        _set_stage(job, repo, session, ViralVideoStage.INTENT_PARSING, "正在解析文案意图...")
        image_analysis = job.image_analysis or {"products": []}
        intent_result = _step_intent_parsing(job, image_analysis)
        job.intent_result = intent_result
        _save_job(repo, job, session)
        _emit_progress(job_id, ViralVideoStage.INTENT_PARSING, 35.0, "意图解析完成")

        # 阶段：编导脚本生成（核心耗时环节，已用快模型）
        _set_stage(job, repo, session, ViralVideoStage.SCRIPT_GENERATION, "正在编排分镜脚本...")
        copy_result = _step_script_generation(job, intent_result, image_analysis)
        _emit_progress(
            job_id,
            ViralVideoStage.SCRIPT_GENERATION,
            85.0,
            "分镜脚本生成完成",
            {"shots": len(copy_result.get("shots", []))},
        )

        # 合规审核后置：不再阻塞前端返回；在阶段3（confirm-copy 出片前）_run_render_pipeline 里再做最终审核。
        # 这里只做一个快速轻量检查（关键字黑名单），发现明显违规再触发重写；LLM 深度审核放到出片前。
        _set_stage(job, repo, session, ViralVideoStage.REVIEW, "正在快速检查脚本合规性...")
        voiceover = (copy_result or {}).get("voiceover_script", "") or ""
        _quick_compliance_blacklist_check(copy_result)
        _emit_progress(job_id, ViralVideoStage.REVIEW, 95.0, "脚本合规初检完成")

        # 标记 copy_generated 并持久化
        job.mark_copy_generated(copy_result)
        job.current_stage = ViralVideoStage.REVIEW
        job.phase_message = "分镜脚本已生成，请确认或编辑口播文案"
        _save_job(repo, job, session)

        _emit_progress(
            job_id,
            ViralVideoStage.REVIEW,
            100.0,
            "分镜脚本已生成，请确认或编辑口播文案",
            {
                "copy_result": copy_result,
                "generated_copy_text": voiceover,
                "storyboard": copy_result.get("shots", []),
                "status": "copy_generated",
            },
            event_type="viral_video:copy_generated",
        )
        logger.info(
            "[爆款视频][阶段2] 编导脚本生成完成 job_id=%s voiceover_len=%d shots=%d",
            job_id,
            len(voiceover),
            len(copy_result.get("shots", [])),
        )
        return {
            "ok": True,
            "job_id": job_id,
            "status": "copy_generated",
            "copy_result": copy_result,
            "generated_copy_text": voiceover,
            "storyboard": copy_result.get("shots", []),
        }

    except Retry:
        raise
    except Exception as e:
        logger.error("[爆款视频][阶段2] 异常: %s", e, exc_info=True)
        _mark_failed_and_notify(job_id, session, None, None, str(e), ViralVideoStage.SCRIPT_GENERATION)
        return {"ok": False, "job_id": job_id, "error": str(e)}
    finally:
        if _hb_stop is not None:
            _hb_stop.set()
        if session:
            session.close()


def _quick_compliance_blacklist_check(copy_result: dict) -> None:
    """阶段2快速黑名单检查：不调用 LLM，只扫描高风险关键词；命中则在 voiceover 中就地替换。

    LLM 深度合规审核（_step_review）在阶段3 confirm-copy 出片前执行。
    """
    if not isinstance(copy_result, dict):
        return
    voiceover = copy_result.get("voiceover_script", "") or ""
    # 广告法绝对化用语黑名单（常见速查，远非完整，仅挡住最明显违规）
    BLACKLIST = {
        "最": "很",
        "第一": "领先",
        "国家级": "高品质",
        "世界级": "高品质",
        "顶级": "优质",
        "极品": "优质",
        "独家": "特色",
        "绝无仅有": "少见",
        "100%": "大幅",
        "百分百": "大幅",
        "永久": "长久",
        "万能": "多用途",
        "特效": "效果好",
        "速效": "快速见效",
        "根治": "改善",
        "包治": "改善",
        "药到病除": "缓解不适",
    }
    changed = False
    for k, v in BLACKLIST.items():
        if k in voiceover:
            voiceover = voiceover.replace(k, v)
            changed = True
    if changed:
        copy_result["voiceover_script"] = voiceover
        # 同步 final_copy/suggested_copy（如果存在）
        for k in ("final_copy", "suggested_copy"):
            if isinstance(copy_result.get(k), str) and copy_result[k]:
                for bk, bv in BLACKLIST.items():
                    copy_result[k] = copy_result[k].replace(bk, bv)


def _try_refund_viral_video(job: ViralVideoJob) -> None:
    """爆款视频生成失败：若已预扣积分则全额退款。"""
    try:
        from packages.shared import get_shared_settings

        _s = get_shared_settings()
        if not _s.points_enabled:
            return
        prepaid = float(getattr(job, "credits_prepaid", 0) or 0)
        if prepaid <= 0:
            return
        from packages.domain.points_service import PointsService

        svc = PointsService()
        # 使用独立 session（避免污染外层事务）
        ssn = SessionLocal()
        try:
            svc.refund_viral_video(
                job.user_id,
                prepaid,
                getattr(job, "credits_transaction_id", "") or "",
                ssn,
            )
            job.credits_prepaid = 0.0
        finally:
            ssn.close()
    except Exception:
        logger.exception("[爆款视频] 失败退款异常 job_id=%s", job.id)


def _settle_viral_video(job: ViralVideoJob, usage: dict | None) -> None:
    """爆款视频生成成功：按实际 usage 结算，多退少补，写 credits_cost。"""
    try:
        from packages.shared import get_shared_settings

        _s = get_shared_settings()
        if not _s.points_enabled:
            job.credits_cost = 0.0
            job.credits_prepaid = 0.0
            return
        prepaid = float(getattr(job, "credits_prepaid", 0) or 0)
        if prepaid <= 0:
            job.credits_cost = 0.0
            return
        from packages.domain.points_rules import calculate_viral_video_credits, resolve_video_dimensions
        from packages.domain.points_service import PointsService

        w, h = resolve_video_dimensions(
            getattr(job, "video_resolution", "720p") or "720p",
            getattr(job, "video_ratio", "9:16") or "9:16",
        )
        fps = 24
        duration = int(getattr(job, "duration", 15) or 15)
        est_tokens = duration * w * h * fps // 1024
        actual_tokens = None
        if isinstance(usage, dict):
            at = usage.get("completion_tokens")
            if isinstance(at, (int, float)) and at > 0:
                actual_tokens = int(at)
        diff_pct = None
        if actual_tokens and est_tokens > 0:
            diff_pct = (actual_tokens - est_tokens) * 100.0 / est_tokens
        logger.info(
            "[爆款视频] tokens估算vs实际 job_id=%s duration=%s %sx%s model=%s est=%s actual=%s diff=%.1f%%",
            job.id, duration, w, h,
            getattr(job, "video_model", "seedance-2.5"),
            est_tokens, actual_tokens,
            diff_pct if diff_pct is not None else 0.0,
        )
        actual_credits = calculate_viral_video_credits(
            duration,
            w,
            h,
            getattr(job, "video_model", "") or "seedance-2.5",
            actual_tokens=actual_tokens,
        )
        svc = PointsService()
        ssn = SessionLocal()
        try:
            svc.settle_viral_video(
                job.user_id,
                prepaid,
                actual_credits,
                getattr(job, "credits_transaction_id", "") or "",
                ssn,
            )
            job.credits_cost = actual_credits
            job.credits_prepaid = 0.0
        finally:
            ssn.close()
    except Exception:
        logger.exception("[爆款视频] 积分结算异常 job_id=%s", job.id)
        # 结算异常不阻塞任务完成：保守按预扣值记 credits_cost
        job.credits_cost = float(getattr(job, "credits_prepaid", 0) or 0)
        job.credits_prepaid = 0.0


def _run_render_pipeline(job_id: str, session, repo, job) -> dict:
    """v1.6.1 阶段3：出片前合规审核（LLM 深度）→ TTS → Seedance → Upload → Completed。

    阶段2 generate-copy 已把 LLM 深度审核后置，这里在 TTS 前做最终审核（不通过则自动重写1次）。
    所有阶段通过 _set_stage 持久化 current_stage/phase_message。
    """
    image_analysis = job.image_analysis or {"products": []}

    # 如果没有 copy_result（旧数据/失败重试），现场补生成（意图+脚本，不走 LLM 审核，出片前会统一做）
    copy_result = job.copy_result
    if not isinstance(copy_result, dict) or not copy_result:
        _set_stage(job, repo, session, ViralVideoStage.SCRIPT_GENERATION, "正在补生成编导脚本...")
        intent = job.intent_result or _step_intent_parsing(job, image_analysis)
        copy_result = _step_script_generation(job, intent, image_analysis)
        job.mark_copy_generated(copy_result)
        _save_job(repo, job, session)

    # 出片前 LLM 深度合规审核（#2134 问题7：审核从阶段2后置到这里，不阻塞前端预览脚本）
    _set_stage(job, repo, session, ViralVideoStage.REVIEW, "正在进行出片前合规审核...")
    try:
        review_result = _step_review(job, copy_result)
        if not review_result.get("passed", True):
            _emit_progress(job_id, ViralVideoStage.REVIEW, 67.0, "审核未通过，正在自动重写...")
            intent = job.intent_result or _step_intent_parsing(job, image_analysis)
            copy_result = _step_script_generation(job, intent, image_analysis)
            _step_review(job, copy_result)  # 二次审核，不通过也继续出片（避免反复循环）
            job.copy_result = copy_result
            job.generated_copy_text = copy_result.get("voiceover_script", "") or ""
            _save_job(repo, job, session)
    except Exception as e:
        logger.warning("[爆款视频][阶段3] 合规审核异常，继续出片: %s", e)
    _emit_progress(job_id, ViralVideoStage.REVIEW, 70.0, "合规审核完成")

    voiceover = copy_result.get("voiceover_script", "") or job.effective_copy_text

    # Step 5: TTS 整段合成
    _set_stage(job, repo, session, ViralVideoStage.TTS, "正在合成AI配音...")
    tts_path = _step_tts(job, voiceover)
    tts_url = _upload_tts_to_oss(job, tts_path)
    _emit_progress(job_id, ViralVideoStage.TTS, 78.0, "配音完成", {"has_tts": tts_url is not None})

    # Step 6: 单次 Seedance（失败自动退款）
    _set_stage(job, repo, session, ViralVideoStage.RENDERING, "正在生成视频（约1-3分钟）...")
    video_path = None
    usage = None
    try:
        video_path, usage = _step_render(job, copy_result, tts_url)
    except Exception as e:
        logger.error("[爆款视频][阶段3] Seedance 生成失败，触发退款: %s", e, exc_info=True)
        # 退款
        _try_refund_viral_video(job)
        raise
    _emit_progress(job_id, ViralVideoStage.RENDERING, 92.0, "视频生成完成")

    # Step 7: Upload
    _set_stage(job, repo, session, ViralVideoStage.UPLOADING, "正在上传视频...")
    video_url = _step_upload(job, video_path)

    # P2-2 OSS 一致性：上传后循环 head 确认公网可访问（最多等 10s），
    # 避免前端拿到 completed 立即下载时命中 NoSuchKey。
    if video_url:
        _wait_oss_ready(video_url, timeout_sec=10)

    # 积分结算：按实际 tokens 多退少补
    _settle_viral_video(job, usage)
    job.mark_completed(video_url)
    job.current_stage = ViralVideoStage.UPLOADING
    job.phase_message = "视频生成完成"
    _save_job(repo, job, session)
    _emit_progress(job_id, ViralVideoStage.UPLOADING, 100.0, "视频生成完成！", {"video_url": video_url})
    _emit_progress(
        job_id,
        ViralVideoStage.UPLOADING,
        100.0,
        "视频生成完成",
        {"video_url": video_url},
        event_type="viral_video:completed",
    )
    logger.info("[爆款视频] 任务完成: job_id=%s video_url=%s", job_id, video_url)
    return {"ok": True, "job_id": job_id, "video_url": video_url}


@shared_task(bind=True, max_retries=2, name="worker.run_viral_video_render")
def run_viral_video_render(self: Task, job_id: str) -> dict:
    """v1.6 阶段3：TTS + 单次 Seedance 生成 + 上传。"""
    session = None
    try:
        _recover_stale_jobs()  # 顺带回收僵尸任务
        _hb_stop = None
        session, repo, job = _get_repo_and_job(job_id)
        if job is None:
            return {"ok": False, "error": "job not found"}
        if job.status != ViralVideoStatus.RUNNING:
            return {"ok": False, "error": f"unexpected status: {job.status}"}
        job.touch_heartbeat()
        _save_job(repo, job, session)
        _hb_stop, _hb_thread = _start_heartbeat_thread(job_id)
        return _run_render_pipeline(job_id, session, repo, job)
    except Retry:
        raise
    except Exception as e:
        logger.error("[爆款视频][阶段3] 异常: %s", e, exc_info=True)
        # 兜底：任何阶段3异常都尝试退款（_step_render 内部异常已经退过，但 upload 等后续失败也需退）
        try:
            if session is not None:
                job_safe = None
                try:
                    repo_safe = SQLAlchemyViralVideoJobRepository(session)
                    job_safe = repo_safe.get(job_id)
                except Exception:
                    pass
                if job_safe is not None and float(getattr(job_safe, "credits_prepaid", 0) or 0) > 0:
                    _try_refund_viral_video(job_safe)
                    try:
                        repo_safe.update(job_safe)
                    except Exception:
                        pass
        except Exception:
            logger.exception("[爆款视频][阶段3] 兜底退款异常")
        _mark_failed_and_notify(job_id, session, None, None, str(e), ViralVideoStage.RENDERING)
        return {"ok": False, "job_id": job_id, "error": str(e)}
    finally:
        if _hb_stop is not None:
            _hb_stop.set()
        if session:
            session.close()