Yuzhiran Dev
233e23016c
- 文章 HTML 存储从文件系统迁移至 articles 表，删除 releases 目录 - 合规审查从 DB 读取 HTML，审查结果写回 DB，通过后自动推进至待发布 - 新增 todayCount 筛选按钮，与系统概览统计数据一致 - 全屏预览修复：提升 z-index 超过侧边栏，添加退出全屏/关闭按钮 - 统一 '优化' → '审查' 命名，消除前后端术语不一致 - 调度器创作完成后自动触发审查（生成 → 审查 → 待发布） - 清理旧备份/调试文件、过期大纲和研究笔记
445 lines
17 KiB
Python
445 lines
17 KiB
Python
#!/usr/bin/env python3
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import json, datetime, logging, sys, re
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from pathlib import Path
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from typing import Dict, List
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PROJECT_ROOT = Path(__file__).parent.parent
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sys.path.insert(0, str(PROJECT_ROOT))
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sys.path.insert(0, str(PROJECT_ROOT / 'platform' / 'backend'))
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from db_helper import get_topic_by_id, update_topic_status
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try:
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from app.core.nvidia_client import call_llm
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HAVE_LLM = True
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except ImportError:
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HAVE_LLM = False
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import mistune
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DATA_DIR = PROJECT_ROOT / "automation" / "data"
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OUTLINE_DIR = DATA_DIR / "outlines"
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RELEASE_DIR = DATA_DIR / "releases"
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TEMPLATES_DIR = PROJECT_ROOT / "automation" / "templates"
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LOGS_DIR = PROJECT_ROOT / "automation" / "logs"
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TODAY = datetime.datetime.now().strftime("%Y-%m-%d")
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GEN_TIME = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(levelname)s - %(message)s',
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handlers=[
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logging.FileHandler(LOGS_DIR / f"writer_{TODAY}.log"),
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logging.StreamHandler()
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]
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)
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logger = logging.getLogger(__name__)
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_md_parser = mistune.create_markdown()
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PLATFORM_CONFIG = {
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"zhihu": {
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"max_chars": 3000,
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"style": "深度长文分析",
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},
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"wechat": {
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"max_chars": 1500,
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"style": "亲切口语化",
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},
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"xiaohongshu": {
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"max_chars": 800,
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"style": "图文笔记，emoji+标签",
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},
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}
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class Writer:
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def __init__(self, topic_id: str):
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self.topic_id = topic_id
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self.topic = self._load_topic()
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outline_file = OUTLINE_DIR / TODAY / f"{topic_id}_outline.md"
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if not outline_file.exists():
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raise FileNotFoundError(f"Outline not found: {outline_file}")
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self.outline_content = outline_file.read_text(encoding='utf-8')
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self.release_dir = RELEASE_DIR / TODAY
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self.release_dir.mkdir(parents=True, exist_ok=True)
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research_file = DATA_DIR / "research" / TODAY / f"{topic_id}_research.md"
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self.research_notes = research_file.read_text(encoding='utf-8') if research_file.exists() else ""
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def _load_topic(self) -> Dict:
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topic = get_topic_by_id(self.topic_id)
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if not topic:
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raise ValueError(f"Topic {self.topic_id} not found")
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return topic
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def _clean_title(self, title: str) -> str:
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title = re.sub(r'[（(]约\s*\d+字[）)]', '', title)
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title = re.sub(r'[（(]MVP[）)]', '', title)
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title = re.sub(r'[（(][^）)]*?[）)]', '', title)
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return title.strip()
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def _parse_outline_sections(self) -> List[Dict]:
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sections = []
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current = None
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for line in self.outline_content.splitlines():
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if line.startswith("# "):
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if current:
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sections.append(current)
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current = {"level": 1, "title": line[2:].strip(), "content": ""}
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elif line.startswith("## "):
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if current:
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sections.append(current)
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current = {"level": 2, "title": line[3:].strip(), "content": ""}
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elif line.startswith("### "):
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if current:
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sections.append(current)
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current = {"level": 3, "title": line[4:].strip(), "content": ""}
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else:
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if current and line.strip():
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current['content'] = current.get('content', '') + line + "\n"
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if current:
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sections.append(current)
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return sections
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def _expand_section(self, section: Dict) -> str:
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content = section.get('content', '').strip()
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if len(content) > 200:
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return content
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if HAVE_LLM and len(content) < 150:
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logger.info(f"使用 LLM 扩写章节: {section['title']}")
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prompt = f"""你是一个真人写作者，在写一篇关于「{self.topic['title']}」的文章。现在要写「{section['title']}」这一节，你的笔记要点如下：
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{content}
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要求：
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- 200-300字，用自己的话展开
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- 用「你」或「我们」视角，不要用「我」
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- 读起来像人在自然说话，不是AI在组装文字
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- 避免「首先」「其次」「总的来说」「综上所述」这类套路表达
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- 如果合适，可以加一句反问
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直接输出段落正文。"""
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try:
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expanded = call_llm(prompt, temperature=0.6, max_tokens=1500)
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if expanded and len(expanded.strip()) > len(content):
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return expanded.strip()
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except Exception as e:
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logger.warning(f"LLM 扩写失败: {e}")
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# Fallback: 将 bullet points 展开为段落
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lines = [l.strip() for l in content.split('\n') if l.strip()]
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if lines:
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sentences = []
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for line in lines:
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text = line.lstrip('- *').strip()
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if text:
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for prefix in ['- ', '* ', '1. ', '2. ', '3. ', '4. ', '5. ']:
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if line.startswith(prefix):
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text = line[len(prefix):]
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break
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if text[-1] not in '。！？；':
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text += '。'
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sentences.append(text)
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if sentences:
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return ' '.join(sentences)
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return content
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def generate_full_markdown(self) -> str:
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sections = self._parse_outline_sections()
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parts = []
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for sec in sections:
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if sec['level'] == 1:
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continue
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heading = f"{'#' * sec['level']} {sec['title']}"
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parts.append(heading)
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if sec.get('content'):
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expanded = self._expand_section(sec)
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parts.append(expanded + "\n")
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full_md = "\n".join(parts).strip()
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return full_md
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def _adapt_for_platform(self, markdown: str, platform: str) -> str:
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cfg = PLATFORM_CONFIG[platform]
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platform_prompts = {
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"zhihu": f"""你是一个知乎答主。把以下文章改写成知乎回答。
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## 知乎回答的特点
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- 知乎用户习惯理性分析、数据支撑、逻辑递进
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- 开头直击问题本质，不绕弯子
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- 每一段讲一个观点，段与段之间有逻辑推进
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## 改写要求
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- 用第三人称或「我们」视角，不要用「我」
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- 不要编个人经历——知乎读者在意的是分析质量，不是故事
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- 开头可以抛数据、抛现象、抛一个矛盾点
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- 不用"首先其次最后"，用自然过渡
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- 避免AI感表达：「总的来说」「综上所述」「值得注意的是」
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- 字数：{cfg['max_chars']}字以内
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## 原文
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{markdown[:3000]}
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## 输出
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直接输出改写后的完整内容（仅正文），每段后空一行。""",
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"wechat": f"""你是一个公众号作者。把以下文章改写成公众号推文。
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## 公众号推文的特点
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- 开头必须制造共鸣或好奇心，让读者愿意往下读
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- 短段落，有节奏感，每段2-3行
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- 核心观点加粗突出
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## 改写要求
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- 用「你」视角，**通篇不允许出现「我」字**
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- 把原文中所有的「我」改成「你」或「很多人」或「有人」
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- 结构可以和原文完全不同——公众号不需要全面的分析，抓住1-2个痛点打透就行
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- 可以删减原文内容，保留最有力的观点
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- 避免「综上所述」「值得注意的是」「换言之」
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- 字数：{cfg['max_chars']}字以内
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## 原文
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{markdown[:3000]}
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## 输出
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直接输出改写后的完整内容（仅正文），每段后空一行。""",
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"xiaohongshu": f"""你是一个小红书用户。把以下文章改写成小红书笔记。
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## 小红书笔记的特点
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- 极短！极短！极短！小红书用户没耐心看长文
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- 直接给结论、给清单、给步骤
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- 原文通常很深很全，但笔记只取最关键的2-3个点
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## 改写要求
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- **全文控制在 {cfg['max_chars']} 字以内**，多一个字都不要
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- 用「你」视角，不要用「我」
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- 开头一句抓住注意力，可以是结论、可以是一个反常识的观点
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- 正文每段1-2句，可以完全打乱原文结构
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- emoji每人段最多1个点缀，不要堆砌（✨💡✅🔸）
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- 结尾加 #话题标签 3-5个
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- 原文的深度分析全部砍掉，只留最 actionable 的内容
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## 原文
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{markdown[:3000]}
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## 输出
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直接输出改写后的完整内容（仅正文）。""",
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}
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if HAVE_LLM:
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prompt = platform_prompts.get(platform, f"""将以下文章改写为适合{platform}平台版本，{cfg['max_chars']}字以内：
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{markdown[:3000]}""")
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try:
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adapted = call_llm(prompt, temperature=0.7, max_tokens=2000)
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if adapted and len(adapted.strip()) > 50:
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adapted = adapted.strip()
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# 字数强校验
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max_c = cfg['max_chars']
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if platform == "xiaohongshu" and len(adapted) > max_c * 1.2:
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adapted = adapted[:max_c]
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last_break = max(adapted.rfind('。'), adapted.rfind('\n'), adapted.rfind('！'), adapted.rfind('？'))
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if last_break > max_c // 2:
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adapted = adapted[:last_break + 1]
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# 微信/知乎清除残留的"我"
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if platform in ("wechat", "zhihu"):
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adapted = adapted.replace('我', '你')
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logger.info(f"LLM 平台适配完成: {platform} ({len(adapted)}字, \"我\"x{adapted.count('我')})")
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return adapted
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except Exception as e:
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logger.warning(f"LLM {platform} 适配失败: {e}")
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except Exception as e:
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logger.warning(f"LLM {platform} 适配失败: {e}")
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if platform == "xiaohongshu":
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lines = markdown.split('\n')
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result = []
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char_count = 0
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for line in lines:
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if char_count >= cfg['max_chars']:
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break
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if line.startswith('## '):
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line = f"## ✨ {line[3:]}"
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elif line.startswith('### '):
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line = f"### 💡 {line[4:]}"
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result.append(line)
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char_count += len(line)
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adapted = '\n'.join(result)
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if adapted.count('#') == 0:
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adapted = f"# {self.topic['title']}\n\n{adapted}"
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return adapted
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return markdown
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def _get_platform_tags(self, platform: str) -> str:
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field = self.topic.get('field', '')
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title = self.topic.get('title', '')
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core = self.topic.get('core_concept', '')
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tag_prompts = {
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"zhihu": f"根据文章信息，生成知乎文章分类标签（3-5个，每个2-4字）。知乎标签偏学术/行业分类，如「经济学」「消费心理学」「科技趋势」。标题：{title} 领域：{field} 核心观点：{core} 直接输出标签，空格分隔。",
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"wechat": f"根据文章信息，生成微信公众号文章标签（3-5个，每个2-4字）。公众号标签偏话题/兴趣分类，如「省钱攻略」「生活方式」「成长干货」。标题：{title} 领域：{field} 核心观点：{core} 直接输出标签，空格分隔。",
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"xiaohongshu": f"根据文章信息，生成小红书笔记标签（3-5个，每个2-4字）。小红书标签偏场景/人群分类，如「实用干货」「学生党」「打工人必看」「好物分享」。标题：{title} 领域：{field} 核心观点：{core} 直接输出标签，空格分隔。",
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}
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if HAVE_LLM:
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prompt = tag_prompts.get(platform, f"根据文章信息生成适合{platform}的标签。标题：{title} 领域：{field} 核心观点：{core} 直接输出标签，空格分隔。")
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try:
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tags_text = call_llm(prompt, temperature=0.2, max_tokens=100)
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if tags_text:
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tags = [t.strip('#') for t in tags_text.strip().split() if t.strip('#')]
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if tags:
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return " ".join(f'<span class="tag">{t}</span>' for t in tags[:5])
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except Exception:
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pass
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tags = []
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if field:
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import re
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parts = re.split(r'[/、与和及]', field)
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for p in parts:
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p = p.strip()
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if len(p) >= 2:
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tags.append(p)
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if len(parts) == 1 and len(parts[0]) > 4:
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for i in range(0, len(parts[0]), 2):
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chunk = parts[0][i:i+2]
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if len(chunk) == 2:
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tags.append(chunk)
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tags.pop(0)
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platform_extra = {"zhihu": ["职场"], "xiaohongshu": ["生活"]}
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for t in platform_extra.get(platform, []):
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if t not in tags:
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tags.append(t)
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if not tags:
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tags = ["科技"]
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seen = set()
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return " ".join(f'<span class="tag">{t}</span>' for t in tags if t not in seen and not seen.add(t))
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def _optimize_title(self, platform: str) -> str:
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original = self.topic['title']
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if not HAVE_LLM:
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return original
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title_templates = {
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"zhihu": f"""你是一个知乎用户，在写一个回答的标题。
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原文标题：{original}
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领域：{self.topic.get('field', '')}
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要求：
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- 有信息量、带数字或对比最好
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- 不要太长，20字以内
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- 风格参考知乎真实高赞标题，不要套路句式
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- 避免：「如何……」废句式、「XXX指南/手册/全攻略」
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生成 3 个选项，每行一个。""",
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"wechat": f"""你是一个公众号作者，在给文章起标题。
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原文标题：{original}
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领域：{self.topic.get('field', '')}
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要求：
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- 制造点好奇心，让人想点开看
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- 口语化，不要书面腔
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- 不要感叹号堆砌，不要「重磅/震惊/紧急」
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- 参考真实公众号标题的感觉
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生成 3 个选项，每行一个。""",
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"xiaohongshu": f"""你是一个小红书用户，在给笔记起标题。
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原文标题：{original}
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领域：{self.topic.get('field', '')}
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要求：
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- 短，20字以内
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- 带1个emoji点缀就行，不用多
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- 有场景感或结果感
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- 不要「必看/收藏/码住」
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- 像真实用户写的，不是运营写的
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生成 3 个选项，每行一个。""",
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}
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prompt = title_templates.get(platform, f"给以下文章改个吸引人的{platform}标题：{original}")
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try:
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resp = call_llm(prompt, temperature=0.7, max_tokens=200)
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titles = []
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for line in resp.strip().split('\n'):
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line = line.strip()
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if not line:
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continue
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line = re.sub(r'^\d+[.、)\s]+', '', line)
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line = line.strip('*#- \t')
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if line:
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titles.append(line)
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if titles:
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logger.info(f"标题优化 [{platform}]: {titles[0][:50]}...")
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return titles[0]
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except Exception as e:
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logger.warning(f"标题优化失败: {e}")
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return original
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def generate_platform_html(self, markdown: str, platform: str) -> str:
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title = self._optimize_title(platform)
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adapted = self._adapt_for_platform(markdown, platform)
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tpl_path = TEMPLATES_DIR / f"{platform}.html"
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if tpl_path.exists():
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template = tpl_path.read_text(encoding='utf-8')
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else:
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template = "<!DOCTYPE html><html><head><meta charset='UTF-8'><title>{{TITLE}}</title><meta name='viewport' content='width=device-width'><style>body{max-width:800px;margin:0 auto;padding:20px;font-family:-apple-system,sans-serif;line-height:1.8}</style></head><body><h1>{{TITLE}}</h1><!-- CONTENT --></body></html>"
|
||
|
||
html = template.replace("{{TITLE}}", title).replace("{{DATE}}", TODAY).replace("{{GEN_TIME}}", GEN_TIME)
|
||
html_content = _md_parser(adapted)
|
||
html = html.replace("<!-- CONTENT -->", html_content)
|
||
|
||
tags_html = self._get_platform_tags(platform)
|
||
if tags_html:
|
||
html = html.replace("<!-- TAGS -->", tags_html)
|
||
else:
|
||
html = html.replace("<!-- TAGS -->", "")
|
||
|
||
return html
|
||
|
||
def save_html(self, html: str, platform: str) -> Path:
|
||
out_dir = self.release_dir / platform
|
||
out_dir.mkdir(parents=True, exist_ok=True)
|
||
filename = f"{platform}_{self.topic_id}.html"
|
||
out_path = out_dir / filename
|
||
out_path.write_text(html, encoding='utf-8')
|
||
logger.info(f"HTML 生成: {out_path}")
|
||
return out_path
|
||
|
||
def mark_draft(self):
|
||
update_topic_status(self.topic_id, 'review')
|
||
logger.info(f"选题 {self.topic_id} 状态已更新为待审查（数据库）")
|
||
|
||
def run(self):
|
||
logger.info("开始撰写阶段")
|
||
markdown = self.generate_full_markdown()
|
||
results = {}
|
||
for platform in ["zhihu", "wechat", "xiaohongshu"]:
|
||
html = self.generate_platform_html(markdown, platform)
|
||
results[platform] = str(self.save_html(html, platform))
|
||
self.mark_draft()
|
||
logger.info(f"撰写完成，状态已更新为待审查")
|
||
return {"ok": True, "files": results}
|
||
|
||
def main():
|
||
import argparse
|
||
parser = argparse.ArgumentParser()
|
||
parser.add_argument('--topic-id', required=True, help='选题ID')
|
||
args = parser.parse_args()
|
||
|
||
w = Writer(args.topic_id)
|
||
result = w.run()
|
||
print(json.dumps(result, ensure_ascii=False))
|
||
sys.exit(0 if result['ok'] else 1)
|
||
|
||
if __name__ == "__main__":
|
||
main()
|