feat(monitor): 抖音 Web 接口客户端 —— 绕开爬虫子进程，直接发 HTTP
移植自 mac-agent-os 的 mediacrawler_adapter：不起子进程、不开页面，用浏览器里那份
登录态直接调抖音 Web 接口。产物键名照抄 store/douyin，所以 ingest 那条链路一个字不用改。
**目前能用的（真环境实测，非推断）**：
profile/other : 200, 7075 字节 —— 博主主页指标（粉丝/获赞/作品数/昵称）
aweme/detail : 200, 45425 字节 —— 单条作品详情（含点赞/评论/收藏/分享）
**目前不能用的：作品列表 `aweme/post`。** 两个互相独立的原因：
1. 这个接口被抖音单独升级成了真校验：不带 x-tt-argus 回 403「Uifid Not Found」，
带上 dummy 值回 200 + **空 body**。也就是说「头在不在」骗得过，「真校验」过不了。
同一套头打 profile/other 和 aweme/detail 都是通的 —— 抖音是挑着接口加保护的，
挑中的恰好是「批量拉作品列表」这个最敏感的动作。
2. 改走页面截获也不行：CDP 浏览器打开博主主页会落到「验证码中间页」（当天大量探测的
代价，过几小时要重测）。
所以现在的边界是：**已知作品的指标刷新能做，自动发现新作品做不了**。
**排查中控住变量后得到的两条事实**（都写进注释了）：
· `Accept` / `Accept-Language` / `Referer` 才是主页接口能返回真数据的原因 —— 只有
UA+client hints+Cookie 时是 200 但仅 121 字节的空壳，补上这三个头变 7074 字节。
（我先前猜的 sec-ch-ua 不是关键。）
· 因此 UA 与 client hints 必须**成套地取自同一个浏览器**，所以 BrowserIdentity 一次
从 CDP 取齐 cookie + UA + hints，而不是各自写死。
「200 + 空 body 必须当场报错」也是刻意写死的：放过去它会在下游变成「这个博主没作品」，
把一次失败伪装成一条正常结果 —— 爬虫那条路正是这么栽的，还被翻译成「账号被封」。
测试 +11：cookie 解析、请求头成套性（含 uifid 缺失/回退）、产物键名与 store 对齐、
以及 _get 的三条失败路径（空 body / 403 带网关原话 / 正常返回）。
This commit is contained in:
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"""
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video_analyzer.py — 视频分析引擎（仅 Master 节点运行）
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职责:
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- 通过浏览器打开视频 URL 提取标题/描述
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- 关键词匹配法判断行业分类
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- 从对应语料池选评论
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设计: 每个 URL 启动一个临时无头浏览器，分析完成即关闭。
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不经过 Camoufox，不保留身份状态。
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"""
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import asyncio
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import logging
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import random
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from typing import Optional
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logger = logging.getLogger("dashboard.video_analyzer")
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# ── 行业关键词映射（与 mc/corpus.py 的 INDUSTRY_TAGS 保持一致）──
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INDUSTRY_TAGS = {
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"health": ["医生","医院","药","健康","养生","中医","体检","症状",
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"治疗","康复","营养","饮食","锻炼","专家","疾病","病症",
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"胃镜","肠镜","肠胃","手术","护理","方子","药材","食疗",
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"养生","保健","肠胃镜","医保","体检报告","专家门诊"],
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"finance": ["股票","基金","理财","投资","经济","A股","财经","行情","股民"],
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"tech": ["手机","数码","电脑","科技","AI","人工智能","评测","软件","芯片"],
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"food": ["美食","做饭","菜谱","餐厅","好吃","探店","吃播","烹饪","菜肴"],
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}
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# ── 万能评论池（所有账号通用）──
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UNIVERSAL_COMMENTS = {
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"praise": [
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"博主说得很对",
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"讲得不错，学到了",
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"分析得很到位",
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"说得太对了",
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"很实用的分享，谢谢博主",
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"讲得很清楚，一下就懂了",
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"受益匪浅，谢谢分享",
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"这个观点很新颖，学习了",
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],
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"question": [
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"请问这个怎么学？",
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"大概需要多长时间？",
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"这个在哪里可以买到？",
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"有推荐的教程吗？",
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],
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"empathy": [
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"太真实了",
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"说出了我的心声",
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"确实是这样，深有体会",
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],
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"agree": [
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"同意博主的观点",
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"说得很好，支持",
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],
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}
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# ── 行业评论池 ──
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INDUSTRY_COMMENTS = {
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"health": {
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"praise": [
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"医生讲得很清楚，通俗易懂",
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"这个方子很实用，收藏了",
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"讲得很专业，学到了",
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"科普得很好，应该让更多人看到",
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"简单明了，受益匪浅",
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"这个知识点太重要了，感谢科普",
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],
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"question": [
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"这个症状一般怎么处理？",
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"平时饮食有什么需要注意的？",
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"这个检查大概需要多久？",
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],
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"empathy": [
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"健康真的太重要了",
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"身体是革命的本钱，感谢提醒",
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],
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},
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"general": { # 兜底
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"praise": UNIVERSAL_COMMENTS["praise"],
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"question": UNIVERSAL_COMMENTS["question"],
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"empathy": UNIVERSAL_COMMENTS["empathy"],
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},
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}
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class VideoContext:
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"""视频分析结果"""
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def __init__(self, title: str = "", description: str = "",
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url: str = "", industry: str = "general",
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tags: list = None):
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self.title = title
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self.description = description
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self.url = url
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self.industry = industry
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self.tags = tags or []
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def to_dict(self):
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return {
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"title": self.title,
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"description": self.description,
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"url": self.url,
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"industry": self.industry,
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"tags": self.tags,
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}
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class VideoAnalyzer:
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"""视频分析器：提取信息 → 行业分类 → 选评论"""
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def __init__(self, max_concurrent: int = 2):
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self.max_concurrent = max_concurrent
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self._rate_limit = 0 # 分析间隔，避免被抖音反爬
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async def analyze_batch(self, urls: list[str],
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account_industry: Optional[str] = None,
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direction: str = "praise") -> dict[str, dict]:
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"""批量分析视频URL（最多 max_concurrent 并发）
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Args:
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urls: 视频URL列表
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account_industry: 执行评论的账号所属行业（health/general）
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direction: 评论方向（praise/question/empathy/agree）
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Returns:
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{url: {title, description, industry, tags, comment}}
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"""
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sem = asyncio.Semaphore(self.max_concurrent)
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results = {}
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async def _analyze_one(url: str):
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async with sem:
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context = await self._extract_context(url)
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context.industry = self._classify(context)
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context.comment = self._pick_comment(
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context.industry, account_industry, direction
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)
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results[url] = context.to_dict()
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results[url]["comment"] = context.comment
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logger.info(f" ✅ {url[:50]} → [{context.industry}] {context.comment[:30]}...")
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tasks = [asyncio.create_task(_analyze_one(url)) for url in urls]
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await asyncio.gather(*tasks, return_exceptions=True)
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# 失败兜底
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for url in urls:
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if url not in results:
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results[url] = {
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"url": url, "title": "", "description": "",
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"industry": "general", "tags": [],
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"comment": self._random("praise"),
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}
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return results
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async def _extract_context(self, url: str) -> VideoContext:
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"""通过无头浏览器提取视频信息"""
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from playwright.async_api import async_playwright
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async with async_playwright() as p:
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browser = await p.chromium.launch(headless=True)
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page = await browser.new_page()
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try:
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await page.goto(url, timeout=20000, wait_until="domcontentloaded")
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await page.wait_for_timeout(3000)
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title = await page.title()
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title = title.replace(" - 抖音", "").replace(" - 今日头条", "").strip()
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desc = ""
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meta = await page.query_selector('meta[name="description"]')
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if meta:
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desc = await meta.get_attribute("content") or ""
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# 提取标签/话题
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tags = []
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tag_els = await page.query_selector_all('[data-e2e="search-topic"], .topic-link, a[href*="tag"]')
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for el in tag_els[:5]:
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t = await el.inner_text()
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if t.strip():
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tags.append(t.strip())
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ctx = VideoContext(
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title=title,
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description=desc,
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url=url,
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tags=tags,
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)
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return ctx
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finally:
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await browser.close()
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def _classify(self, context: VideoContext) -> str:
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"""关键词匹配法判断行业"""
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text = f"{context.title} {context.description} {' '.join(context.tags)}"
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for ind, tags in INDUSTRY_TAGS.items():
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for tag in tags:
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if tag in text:
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logger.debug(f" 🏷️ 匹配行业 {ind}: 关键词='{tag}'")
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return ind
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return "general"
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def _pick_comment(self, video_industry: str,
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account_industry: Optional[str],
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direction: str = "praise") -> str:
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"""选评论:
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- 账号行业匹配视频行业 → 行业池
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- 不匹配 → 万能池
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- 无账号行业 → 万能池
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"""
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# 账号行业匹配视频行业 → 行业池
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if account_industry and video_industry == account_industry:
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pool = INDUSTRY_COMMENTS.get(video_industry, {})
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comments = pool.get(direction, pool.get("praise", UNIVERSAL_COMMENTS["praise"]))
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return random.choice(comments)
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# 不匹配 → 万能池
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return self._random(direction)
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def _random(self, direction: str = "praise") -> str:
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pool = UNIVERSAL_COMMENTS.get(direction, UNIVERSAL_COMMENTS["praise"])
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return random.choice(pool)
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