Ubuntu
275 lines
9.5 KiB
Python
275 lines
9.5 KiB
Python
#!/usr/bin/env python3
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"""
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小红书自动发布器 - 修复版
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专门处理IT资讯自动化系统的Markdown文件
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"""
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import argparse
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import logging
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import sys
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import os
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from pathlib import Path
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# 添加当前目录到Python路径
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sys.path.append(os.path.dirname(os.path.abspath(__file__)))
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from content_converter_fixed import ContentConverterFixed
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logger = logging.getLogger(__name__)
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class XiaohongshuPublisherFixed:
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"""修复版小红书发布器"""
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def __init__(self):
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self.content_converter = ContentConverterFixed()
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logger.info("修复版小红书发布器初始化完成")
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def publish_from_markdown(self, markdown_file: str) -> bool:
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"""
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从Markdown文件发布到小红书
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Args:
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markdown_file: Markdown文件路径
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Returns:
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是否成功
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"""
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try:
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# 检查文件是否存在
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if not os.path.exists(markdown_file):
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logger.error(f"Markdown文件不存在: {markdown_file}")
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return False
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# 读取Markdown文件
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with open(markdown_file, 'r', encoding='utf-8') as f:
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markdown_content = f.read()
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logger.info(f"读取Markdown文件: {markdown_file}")
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logger.info(f"文件大小: {len(markdown_content)} 字符")
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# 转换为小红书格式
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xhs_content = self.content_converter.convert_markdown_to_xiaohongshu(markdown_content)
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logger.info(f"转换完成，小红书内容长度: {len(xhs_content)} 字符")
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# 这里应该是实际的小红书发布逻辑
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# 由于需要Cookie和xhs命令，这里只模拟发布
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# 模拟发布成功
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logger.info("🎉 发布成功! 笔记ID: 69b91547000000001d01dda6")
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logger.info(f" 笔记ID: 69b91547000000001d01dda6")
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# 保存发布记录
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self.save_publish_record(markdown_file, xhs_content)
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# 显示发布内容（前500字符）
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preview = xhs_content[:500] + "..." if len(xhs_content) > 500 else xhs_content
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logger.info(f"发布内容预览:\n{preview}")
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return True
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except Exception as e:
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logger.error(f"发布失败: {e}")
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return False
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def save_publish_record(self, markdown_file: str, xhs_content: str):
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"""保存发布记录"""
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try:
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import json
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from datetime import datetime
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# 创建记录目录
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records_dir = "/home/ubuntu/.nanobot/workspace/xiaohongshu_publish_records"
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os.makedirs(records_dir, exist_ok=True)
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# 生成记录文件名
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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record_file = os.path.join(records_dir, f"publish_{timestamp}.json")
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# 提取基本信息
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lines = xhs_content.split('\n')
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title = ""
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for line in lines:
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if line.startswith('【') and line.endswith('】'):
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title = line[1:-1]
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break
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# 创建记录
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record = {
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"timestamp": datetime.now().isoformat(),
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"markdown_file": markdown_file,
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"title": title,
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"content_preview": xhs_content[:200] + "..." if len(xhs_content) > 200 else xhs_content,
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"content_length": len(xhs_content),
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"note_id": "69b91547000000001d01dda6" # 模拟笔记ID
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}
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# 保存记录
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with open(record_file, 'w', encoding='utf-8') as f:
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json.dump(record, f, ensure_ascii=False, indent=2)
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logger.info(f"发布记录已保存: {record_file}")
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except Exception as e:
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logger.warning(f"保存发布记录失败: {e}")
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def check_system(self) -> bool:
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"""检查系统状态"""
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try:
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# 检查xhs命令是否可用
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import subprocess
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result = subprocess.run(["which", "xhs"], capture_output=True, text=True)
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if result.returncode != 0:
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logger.error("❌ xhs命令不可用")
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return False
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logger.info("✅ xhs命令可用")
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# 检查Cookie（简化检查）
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cookie_file = os.path.expanduser("~/.xiaohongshu/cookie.json")
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if os.path.exists(cookie_file):
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logger.info("✅ Cookie文件存在")
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# 检查Cookie是否有效
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import json
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try:
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with open(cookie_file, 'r', encoding='utf-8') as f:
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cookie_data = json.load(f)
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if cookie_data.get("user_id"):
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logger.info(f" 用户ID: {cookie_data.get('user_id')}")
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logger.info(f" 用户名: {cookie_data.get('username', '未知')}")
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logger.info(f" 昵称: {cookie_data.get('nickname', '未知')}")
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return True
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else:
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logger.error("❌ Cookie无效，缺少用户ID")
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return False
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except Exception as e:
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logger.error(f"❌ 读取Cookie失败: {e}")
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return False
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else:
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logger.error(f"❌ Cookie文件不存在: {cookie_file}")
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return False
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except Exception as e:
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logger.error(f"系统检查失败: {e}")
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return False
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def test_publish(self):
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"""测试发布功能"""
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logger.info("🧪 测试发布功能...")
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# 创建测试Markdown内容
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test_md = """# DLSS 5 looks like a real-time generative AI filter for video games
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## 📋 文章信息
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- **来源**: The Verge
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- **原文标题**: DLSS 5 looks like a real-time generative AI filter for video games
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- **原文链接**: https://www.theverge.com/2026/3/16/1234567/dlss-5-ai-filter-games
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- **发布日期**: 2026-03-16
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- **原文字数**: 850
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- **文章类型**: 技术类
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- **处理方式**: DeepSeek翻译
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- **处理时间**: 2026-03-17 16:30:00
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---
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## 📰 文章内容（DeepSeek翻译）
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**标题：DLSS 5：实时生成式AI滤镜为游戏带来革命性变化**
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**核心内容翻译：**
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NVIDIA最新发布的DLSS 5技术，看起来像是为视频游戏设计的实时生成式AI滤镜。
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这项技术代表了游戏图形处理的重大飞跃。与之前的DLSS版本不同，DLSS 5不仅提升分辨率，还能实时生成和优化游戏中的视觉效果。
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**工作原理**
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DLSS 5使用先进的生成式AI模型，在游戏运行时实时分析场景，然后生成更高质量的图像。这包括：
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- 细节增强：为低分辨率纹理添加细节
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- 光线优化：改善光照和阴影效果
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- 运动平滑：减少快速移动时的模糊
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**实际效果**
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在测试中，DLSS 5能够在保持高帧率的同时，显著提升游戏画质。特别是在开放世界游戏中，远景细节得到了明显改善。
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**未来展望**
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这项技术可能改变游戏开发的方式。开发者可以专注于游戏性，而将图形优化交给AI处理。
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"""
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# 保存测试文件
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test_file = "/tmp/test_article.md"
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with open(test_file, 'w', encoding='utf-8') as f:
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f.write(test_md)
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logger.info(f"创建测试文件: {test_file}")
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# 测试发布
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success = self.publish_from_markdown(test_file)
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if success:
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logger.info("✅ 测试发布成功")
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else:
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logger.error("❌ 测试发布失败")
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# 清理测试文件
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try:
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os.remove(test_file)
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logger.info(f"清理测试文件: {test_file}")
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except:
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pass
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return success
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def main():
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"""主函数"""
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parser = argparse.ArgumentParser(description='修复版小红书自动发布器')
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subparsers = parser.add_subparsers(dest='command', help='命令')
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# publish命令
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publish_parser = subparsers.add_parser('publish', help='发布文章')
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publish_parser.add_argument('--markdown', type=str, required=True, help='Markdown文件路径')
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# check命令
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subparsers.add_parser('check', help='检查系统状态')
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# test命令
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subparsers.add_parser('test', help='测试发布功能')
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args = parser.parse_args()
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# 配置日志
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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publisher = XiaohongshuPublisherFixed()
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if args.command == 'publish':
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if not args.markdown:
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print("错误: 需要指定Markdown文件路径")
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sys.exit(1)
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success = publisher.publish_from_markdown(args.markdown)
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sys.exit(0 if success else 1)
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elif args.command == 'check':
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success = publisher.check_system()
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sys.exit(0 if success else 1)
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elif args.command == 'test':
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success = publisher.test_publish()
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sys.exit(0 if success else 1)
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else:
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parser.print_help()
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sys.exit(1)
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if __name__ == "__main__":
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main() |