Initial commit: yu-zhi-ran platform with automation integration
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#!/usr/bin/env python3
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"""
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撰写阶段：基于大纲和选题生成完整文章（三平台版本）
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"""
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import json, datetime, logging, sys, re, subprocess
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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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# 添加项目根和 backend 路径，以导入 app.core.llm_client
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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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# 导入 LLM 客户端（NVIDIA）
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try:
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from app.core.nvidia_client import expand_content_with_llm # type: ignore
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HAVE_LLM = True
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except ImportError as e:
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logging.warning(f"LLM client unavailable: {e}")
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HAVE_LLM = False
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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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DATA_DIR = PROJECT_ROOT / "automation" / "data"
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TOPICS_FILE = DATA_DIR / "sustainability_topics.json"
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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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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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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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# 加载研究笔记（作为 LLM 上下文）
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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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topics = json.loads(TOPICS_FILE.read_text(encoding='utf-8'))
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for t in topics:
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if t['id'] == self.topic_id:
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return t
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raise ValueError(f"Topic {self.topic_id} not found")
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def _clean_title(self, title: str) -> str:
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"""去除标题中的指导性文字（如字数说明、MVP标记等）"""
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import re
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# 去掉括号中的说明：约200字、约300字、MVP、试行等
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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) # 保守移除任意括号内容（可能误伤，但大纲通常不包含重要括号信息）
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return title.strip()
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def _parse_outline_sections(self) -> List[Dict]:
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"""将大纲 Markdown 解析为结构化列表，保留层级和内容"""
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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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"""将大纲中的简短描述扩展为完整段落"""
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content = section.get('content', '').strip()
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# 如果有足够内容（>200字），直接返回
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if len(content) > 200:
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return content
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# 如果内容极少，需要 LLM 扩写
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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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try:
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expanded = expand_content_with_llm(
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topic=self.topic,
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section_title=section['title'],
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section_content=content,
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context=self.research_notes
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)
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if expanded and len(expanded.strip()) > len(content):
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return expanded.strip()
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else:
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logger.warning("LLM 扩写结果为空或过短，使用占位")
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raise ValueError("Empty expansion")
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except Exception as e:
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logger.warning(f"LLM 扩写失败: {e}，使用占位内容")
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# 返回占位内容，保持流程继续
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return f"{content}\n\n（本段内容需要人工补充：当前模型调用失败或未配置）"
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# 否则返回原内容
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return content
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def generate_full_markdown(self) -> str:
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"""根据大纲生成完整 Markdown 正文（不用原标题，全部由 LLM 扩写生成）"""
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sections = self._parse_outline_sections()
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parts = []
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# 只保留 LLM 扩写的内容，不添加任何原始标题标记
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for sec in sections:
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# 如果内容极短，LLM 扩写后返回的完整段落中可能包含标题，我们不过滤
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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\n")
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full_md = "\n".join(parts).strip()
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# 添加文末声明
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full_md += f"\n<p>（本文由宇之然AI助手生成，数据来源可靠，内容经合规审查）</p>\n"
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full_md += f"<p><em>生成时间：{TODAY}</em></p>\n"
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return full_md
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def generate_platform_html(self, markdown: str, platform: str) -> str:
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"""将 Markdown 转换为平台 HTML（基于模板）"""
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title = self.topic['title']
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# 加载模板
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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></head><body><h1>{{TITLE}}</h1><!-- CONTENT --></body></html>"
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# 替换变量
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html = template.replace("{{TITLE}}", title).replace("{{DATE}}", TODAY)
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# 注入内容 (简单处理：markdown 转 HTML 可以用 marked.js 或 simple转换，这里暂时用 <pre> 包裹或简单段落化)
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# 为了快速展示，我们将 markdown 的段落转换为 <p> 标签
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# 实际中建议使用 markdown 库（如 python-markdown）转换
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html_content = self._markdown_to_html(markdown)
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html = html.replace("<!-- CONTENT -->", html_content)
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# 平台特定标签补充
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if platform == "zhihu":
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tags = '<div class="tags">#科技 #职场</div>'
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html = html.replace("<!-- TAGS -->", tags)
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elif platform == "xiaohongshu":
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hashtags = '<div class="hashtags">#AI #可持续 #生活方式</div>'
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html = html.replace("<!-- HASHTAGS -->", hashtags)
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elif platform == "wechat":
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# 微信公众号可能还需要摘要等，模板已处理
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pass
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return html
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def _markdown_to_html(self, md: str) -> str:
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"""极简 markdown 转换（仅本场景使用）"""
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lines = md.split('\n')
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html_parts = []
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for line in lines:
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if line.startswith('# '):
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html_parts.append(f"<h1>{line[2:]}</h1>")
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elif line.startswith('## '):
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html_parts.append(f"<h2>{line[3:]}</h2>")
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elif line.startswith('### '):
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html_parts.append(f"<h3>{line[4:]}</h3>")
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elif line.strip().startswith('- '):
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html_parts.append(f"<li>{line[2:]}</li>")
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elif re.match(r'^\d+\. ', line):
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content = re.sub(r'^\d+\. ', '', line)
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html_parts.append(f"<li>{content}</li>")
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elif line.strip():
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html_parts.append(f"<p>{line}</p>")
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else:
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html_parts.append("") # 空行
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return "\n".join(html_parts)
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def save_html(self, html: str, platform: str) -> Path:
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out_dir = self.release_dir / platform
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out_dir.mkdir(parents=True, exist_ok=True)
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filename = f"{platform}_{self.topic_id}_{platform}.html"
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out_path = out_dir / filename
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out_path.write_text(html, encoding='utf-8')
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logger.info(f"HTML 生成: {out_path}")
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return out_path
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def mark_draft(self):
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"""标记选题为「待发布」，同时更新数据库"""
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# 更新 JSON 文件
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with open(TOPICS_FILE, 'r', encoding='utf-8') as f:
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topics = json.load(f)
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updated = False
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for t in topics:
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if t.get('id') == self.topic_id:
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t[status'] = 'draft'
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updated = True
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break
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if updated:
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with open(TOPICS_FILE, 'w', encoding='utf-8') as f:
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json.dump(topics, f, ensure_ascii=False, indent=2)
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# 更新数据库
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db = SessionLocal()
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try:
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topic_db = db.query(Topic).filter(Topic.id == self.topic_id).first()
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if topic_db:
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topic_db.status = 'draft'
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db.commit()
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logger.info(f"选题 {self.topic_id} 状态已更新为 draft（数据库）")
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else:
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logger.warning(f"数据库中未找到选题 {self.topic_id}")
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except Exception as e:
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logger.error(f"更新数据库失败: {e}")
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db.rollback()
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finally:
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db.close()
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logger.info(f"选题 {self.topic_id} 状态更新为「待发布」（JSON）")
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"""标记选题为「待发布」"""
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with open(TOPICS_FILE, 'r', encoding='utf-8') as f:
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topics = json.load(f)
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for t in topics:
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if t.get('id') == self.topic_id:
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t['status'] = 'draft'
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# ready_at 留空，待合规审核通过后设置
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break
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with open(TOPICS_FILE, 'w', encoding='utf-8') as f:
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json.dump(topics, f, ensure_ascii=False, indent=2)
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logger.info(f"选题 {self.topic_id} 状态更新为「待发布」")
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def run(self):
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logger.info("开始撰写阶段")
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markdown = self.generate_full_markdown()
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results = {}
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for platform in ["zhihu", "wechat", "xiaohongshu"]:
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html = self.generate_platform_html(markdown, platform)
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results[platform] = str(self.save_html(html, platform))
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self.mark_draft()
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logger.info(f"撰写完成，状态改为 draft，待合规审核")
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return {"ok": True, "files": results}
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def main():
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument('--topic-id', required=True, help='选题ID')
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args = parser.parse_args()
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w = Writer(args.topic_id)
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result = w.run()
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print(json.dumps(result, ensure_ascii=False))
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sys.exit(0 if result['ok'] else 1)
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if __name__ == "__main__":
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main()
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