xbuilderlab--cheat-on-content
wehub-resource-sync
70 行
2.2 KiB
Python
70 行
2.2 KiB
Python
"""发完帖子后跑一次：抓 LinkedIn 单帖分析 → 生成 NotebookLM 友好的 md。
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用法：
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python review.py login # 仅登录（首次，弹出浏览器）
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python review.py video <activity_id_or_url> [script.txt] # 抓单帖分析
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`video` 子命令名沿用 douyin-session / bilibili-stat 的契约（/cheat-retro 统一调
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`run.sh ... video ...`）；对 LinkedIn 而言"video"即一条帖子。
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"""
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from __future__ import annotations
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import asyncio
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import sys
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from pathlib import Path
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import crawler
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import renderer
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from paths import videos_dir
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def run_with_id(activity_raw: str, script_path: str | None) -> None:
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active_videos_dir = videos_dir()
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active_videos_dir.mkdir(parents=True, exist_ok=True)
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activity_id = crawler.extract_activity_id(activity_raw)
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script = ""
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if script_path:
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p = Path(script_path).expanduser()
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if p.is_file():
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script = p.read_text(encoding="utf-8", errors="ignore")
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print(f"稿子：{p.name}（{len(script)} 字符）")
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else:
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print(f"[警告] 找不到稿子 {p}")
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print(f"[抓取] 帖子 activity:{activity_id}")
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result = asyncio.run(crawler.fetch_all(activity_id))
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post = result["post"]
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if not post:
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print("❌ 未抓到数据（多半是未登录）。先跑：python review.py login")
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sys.exit(3)
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out_dir = renderer.output_dir_for(post, active_videos_dir)
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out_dir.mkdir(parents=True, exist_ok=True)
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if script:
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(out_dir / "script.txt").write_text(script, encoding="utf-8")
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md = renderer.render_report(post, script)
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report = out_dir / "report.md"
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report.write_text(md, encoding="utf-8")
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print(f"\n✓ {report}")
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def main() -> None:
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if len(sys.argv) > 1 and sys.argv[1] == "login":
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asyncio.run(crawler.ensure_login())
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return
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if len(sys.argv) > 1 and sys.argv[1] == "video":
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if len(sys.argv) < 3:
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print("用法：python review.py video <activity_id_or_url> [script.txt]")
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sys.exit(3)
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activity_raw = sys.argv[2]
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script_path = sys.argv[3] if len(sys.argv) > 3 else None
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run_with_id(activity_raw, script_path)
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return
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print(__doc__)
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if __name__ == "__main__":
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main()
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