Initial commit: yu-zhi-ran platform with automation integration

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2026-04-19 14:05:09 +08:00
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#!/usr/bin/env python3
"""
撰写阶段:基于大纲和选题生成完整文章(三平台版本)
"""
import json, datetime, logging, sys, re, subprocess
from pathlib import Path
from typing import Dict, List
import base64
from io import BytesIO
PROJECT_ROOT = Path(__file__).parent.parent
# 添加项目根和 backend 路径,以导入 app.core.llm_client
sys.path.insert(0, str(PROJECT_ROOT))
sys.path.insert(0, str(PROJECT_ROOT / 'platform' / 'backend'))
# 导入 LLM 客户端(NVIDIA
try:
from app.core.qnaigc_client import expand_content_with_llm # type: ignore
HAVE_LLM = True
except ImportError as e:
logging.warning(f"LLM client unavailable: {e}")
HAVE_LLM = False
PROJECT_ROOT = Path(__file__).parent.parent
sys.path.insert(0, str(PROJECT_ROOT))
# 导入数据库模型
from app.database import SessionLocal
from app.models import Topic
DATA_DIR = PROJECT_ROOT / "automation" / "data"
TOPICS_FILE = DATA_DIR / "sustainability_topics.json"
OUTLINE_DIR = DATA_DIR / "outlines"
RELEASE_DIR = DATA_DIR / "releases"
TEMPLATES_DIR = PROJECT_ROOT / "automation" / "templates"
LOGS_DIR = PROJECT_ROOT / "automation" / "logs"
TODAY = datetime.datetime.now().strftime("%Y-%m-%d")
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler(LOGS_DIR / f"writer_{TODAY}.log"),
logging.StreamHandler()
]
)
logger = logging.getLogger(__name__)
class Writer:
def __init__(self, topic_id: str):
self.topic_id = topic_id
self.topic = self._load_topic()
outline_file = OUTLINE_DIR / TODAY / f"{topic_id}_outline.md"
if not outline_file.exists():
raise FileNotFoundError(f"Outline not found: {outline_file}")
self.outline_content = outline_file.read_text(encoding='utf-8')
self.release_dir = RELEASE_DIR / TODAY
self.release_dir.mkdir(parents=True, exist_ok=True)
# 加载研究笔记(作为 LLM 上下文)
research_file = DATA_DIR / "research" / TODAY / f"{topic_id}_research.md"
self.research_notes = research_file.read_text(encoding='utf-8') if research_file.exists() else ""
def _load_topic(self) -> Dict:
topics = json.loads(TOPICS_FILE.read_text(encoding='utf-8'))
for t in topics:
if t['id'] == self.topic_id:
return t
raise ValueError(f"Topic {self.topic_id} not found")
def _clean_title(self, title: str) -> str:
"""去除标题中的指导性文字(如字数说明、MVP标记等)"""
import re
# 去掉括号中的说明:约200字、约300字、MVP、试行等
title = re.sub(r'[(]约\s*\d+字[)]', '', title)
title = re.sub(r'[(]MVP[)]', '', title)
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)
return title.strip()
def _parse_outline_sections(self) -> List[Dict]:
"""将大纲 Markdown 解析为结构化列表,保留层级和内容"""
sections = []
current = None
for line in self.outline_content.splitlines():
if line.startswith("# "):
if current:
sections.append(current)
current = {"level": 1, "title": line[2:].strip(), "content": ""}
elif line.startswith("## "):
if current:
sections.append(current)
current = {"level": 2, "title": line[3:].strip(), "content": ""}
elif line.startswith("### "):
if current:
sections.append(current)
current = {"level": 3, "title": line[4:].strip(), "content": ""}
else:
if current and line.strip():
current['content'] = current.get('content', '') + line + "\n"
if current:
sections.append(current)
return sections
def _expand_section(self, section: Dict) -> str:
"""将大纲中的简短描述扩展为完整段落"""
content = section.get('content', '').strip()
# 如果有足够内容(>200字),直接返回
if len(content) > 200:
return content
# 如果内容极少,需要 LLM 扩写
if HAVE_LLM and len(content) < 150:
logger.info(f"使用 LLM 扩写章节: {section['title']}")
try:
expanded = expand_content_with_llm(
topic=self.topic,
section_title=section['title'],
section_content=content,
context=self.research_notes
)
if expanded and len(expanded.strip()) > len(content):
return expanded.strip()
else:
logger.warning("LLM 扩写结果为空或过短,使用占位")
raise ValueError("Empty expansion")
except Exception as e:
logger.warning(f"LLM 扩写失败: {e},使用占位内容")
# 返回占位内容,保持流程继续
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)"
# 否则返回原内容
return content
def generate_full_markdown(self) -> str:
"""根据大纲生成完整 Markdown 正文(不用原标题,全部由 LLM 扩写生成)"""
sections = self._parse_outline_sections()
parts = []
# 只保留 LLM 扩写的内容,不添加任何原始标题标记
for sec in sections:
# 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
if sec.get('content'):
expanded = self._expand_section(sec)
parts.append(expanded + "\n\n")
full_md = "\n".join(parts).strip()
# 添加文末声明
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"
full_md += f"<p><em>生成时间:{TODAY}</em></p>\n"
return full_md
def generate_platform_html(self, markdown: str, platform: str) -> str:
"""将 Markdown 转换为平台 HTML(基于模板)"""
title = self.topic['title']
# 加载模板
tpl_path = TEMPLATES_DIR / f"{platform}.html"
if tpl_path.exists():
template = tpl_path.read_text(encoding='utf-8')
else:
template = "<!DOCTYPE html><html><head><meta charset='UTF-8'><title>{{TITLE}}</title></head><body><h1>{{TITLE}}</h1><!-- CONTENT --></body></html>"
# 替换变量
html = template.replace("{{TITLE}}", title).replace("{{DATE}}", TODAY)
# 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)
# 为了快速展示,我们将 markdown 的段落转换为 <p> 标签
# 实际中建议使用 markdown 库(如 python-markdown)转换
html_content = self._markdown_to_html(markdown)
html = html.replace("<!-- CONTENT -->", html_content)
# 平台特定标签补充
if platform == "zhihu":
tags = '<div class="tags">#科技 #职场</div>'
html = html.replace("<!-- TAGS -->", tags)
elif platform == "xiaohongshu":
hashtags = '<div class="hashtags">#AI #可持续 #生活方式</div>'
html = html.replace("<!-- HASHTAGS -->", hashtags)
elif platform == "wechat":
# 微信公众号可能还需要摘要等,模板已处理
pass
if platform == "xiaohongshu":
html = self._fill_image_placeholders(html, platform, title)
return html
def _markdown_to_html(self, md: str) -> str:
"""极简 markdown 转换(仅本场景使用)"""
lines = md.split('\n')
html_parts = []
for line in lines:
if line.startswith('# '):
html_parts.append(f"<h1>{line[2:]}</h1>")
elif line.startswith('## '):
html_parts.append(f"<h2>{line[3:]}</h2>")
elif line.startswith('### '):
html_parts.append(f"<h3>{line[4:]}</h3>")
elif line.strip().startswith('- '):
html_parts.append(f"<li>{line[2:]}</li>")
elif re.match(r'^\d+\. ', line):
content = re.sub(r'^\d+\. ', '', line)
html_parts.append(f"<li>{content}</li>")
elif line.strip():
html_parts.append(f"<p>{line}</p>")
else:
html_parts.append("") # 空行
return "\n".join(html_parts)
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}_{platform}.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):
"""标记选题为「待审查」,同时更新数据库"""
"""标记选题为「待发布」,同时更新数据库"""
# 更新 JSON 文件
with open(TOPICS_FILE, 'r', encoding='utf-8') as f:
topics = json.load(f)
updated = False
for t in topics:
if t.get('id') == self.topic_id:
t['status'] = '待审查'
updated = True
break
if updated:
with open(TOPICS_FILE, 'w', encoding='utf-8') as f:
json.dump(topics, f, ensure_ascii=False, indent=2)
# 更新数据库
db = SessionLocal()
try:
topic_db = db.query(Topic).filter(Topic.id == self.topic_id).first()
if topic_db:
topic_db.status = '待审查'
db.commit()
logger.info(f"选题 {self.topic_id} 状态已更新为 draft(数据库)")
else:
logger.warning(f"数据库中未找到选题 {self.topic_id}")
except Exception as e:
logger.error(f"更新数据库失败: {e}")
db.rollback()
finally:
db.close()
logger.info(f"选题 {self.topic_id} 状态更新为「待审查」(JSON")
"""标记选题为「待发布」"""
with open(TOPICS_FILE, 'r', encoding='utf-8') as f:
topics = json.load(f)
for t in topics:
if t.get('id') == self.topic_id:
t['status'] = '待审查'
# ready_at 留空,待合规审核通过后设置
break
with open(TOPICS_FILE, 'w', encoding='utf-8') as f:
json.dump(topics, f, ensure_ascii=False, indent=2)
logger.info(f"选题 {self.topic_id} 状态更新为「待审查」")
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,状态改为 draft,待合规审核")
return {"ok": True, "files": results}
def _image_to_data_url(self, img_path: Path, fmt: str = None) -> str:
data = img_path.read_bytes()
if fmt is None:
fmt = img_path.suffix.lstrip('.').lower()
b64 = base64.b64encode(data).decode('ascii')
return f"data:image/{fmt};base64,{b64}"
def _generate_and_inline_images(self, platform: str, title: str) -> dict:
from scripts.image_generator import ImageGenerator
gen = ImageGenerator()
files = gen.generate_all_placeholders(title, platform)
mapping = {}
cover = files.get('cover')
if cover and cover.exists():
mapping['main-image-src'] = self._image_to_data_url(cover)
thumbs = []
for k, p in files.items():
if k != 'cover' and p.exists():
thumbs.append(self._image_to_data_url(p))
mapping['thumbnail-srcs'] = thumbs
return mapping
def _fill_image_placeholders(self, html: str, platform: str, title: str) -> str:
if platform != 'xiaohongshu':
return html
mapping = self._generate_and_inline_images(platform, title)
# Replace main image placeholder
main_ph = '<img src="" alt="封面图" class="main-image">'
if 'main-image-src' in mapping:
new_main = f'<img src="{mapping["main-image-src"]}" alt="封面图" class="main-image">'
html = html.replace(main_ph, new_main)
# Replace thumbnail placeholders (6)
thumbs = mapping.get('thumbnail-srcs', [])
for idx, src in enumerate(thumbs[:6], start=1):
ph = f'<img src="" alt="{idx}" class="thumbnail">'
new_thumb = f'<img src="{src}" alt="{idx}" class="thumbnail">'
html = html.replace(ph, new_thumb)
return html
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()