Ubuntu
516 lines
18 KiB
Python
516 lines
18 KiB
Python
#!/usr/bin/env python3
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"""
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CMS 改写 → 公众号发布 一键流水线
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两种模式:
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1. 自动改写模式 (默认): python pipeline.py <article_id>
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自己调 DeepSeek LLM 改写，然后发布
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2. 传入改写内容模式: python pipeline.py <article_id> --content-file <markdown文件>
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用预改写好的内容直接发布（志玲模式）
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环境:
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依赖 openai 库: pip install openai
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API Key 从 ~/.nanobot/config.json 读取 DeepSeek 配置
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"""
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import sys
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import os
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import json
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import time
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import argparse
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from typing import Optional, Dict, Any
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# 确保脚本在 rewrite/ 目录执行也能正常工作
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SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
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PROJECT_ROOT = os.path.dirname(SCRIPT_DIR)
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BAOYU_DIR = "/home/ubuntu/.nanobot/workspace/skills/baoyu-post-to-wechat"
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sys.path.insert(0, BAOYU_DIR)
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sys.path.insert(0, SCRIPT_DIR)
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from cms_ops import get_article_full, update_article_content
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from baoyu_optimized import WeChatPublisher
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# ---------- 配置 ----------
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MODEL = "deepseek-v4-flash"
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NANOBOT_CONFIG = os.path.expanduser("~/.nanobot/config.json")
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SYSTEM_PROMPT = """你是一个专业的医疗健康内容编辑。请将下面的文章改写为适合微信公众号发布的版本。
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改写要求:
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1. **保持医疗准确性**: 不得改变任何医学事实、数据、诊断标准、用药建议
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2. **风格口语化**: 用亲近自然的语气，像医生朋友在聊天。适当使用emoji（每段不超过1个）
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3. **保留结构**: 保留原文的章节标题和信息层次，但标题可以重新提炼得更吸引人
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4. **精简优化**: 去除重复表述，合并相近内容，让文章更紧凑
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5. **适合手机阅读**: 段落短小（2-3句一段），善用小标题和列表
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6. **只输出改写后的 markdown**，不要加任何解释说明
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输出格式: 纯 markdown 文本，可以包含:
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- **粗体** 强调关键信息
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- 无序列表 -
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- ## 二级标题（如果需要）
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- 段间空行分隔"""
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# ---------- 图片生成配置 ----------
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IMAGE_GEN_ENABLED = True # 是否启用配图生成
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IMAGE_GEN_COUNT = 2 # 目标生成图片数量（1-3）
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IMAGE_STORAGE_DIR = os.path.join(PROJECT_ROOT, "storage", "admin", "images")
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IMAGE_PROMPT_SYSTEM = """你是一个图片提示词专家。根据下面给出的文章片段，生成一个适合配图的简短中文描述（用于AI生图），要求：
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1. 描述要简洁具体，20-40个字
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2. 风格要求：温暖、专业、治愈的视觉风格
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3. 内容要与上下文紧密相关
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4. 只输出描述文字，不要加任何解释或前缀"""
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# 混元生图
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HUNYUAN_SECRET_ID = "AKIDlmaJo4Qeq23xmdR7jIn59yd4RZUYUHtP"
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HUNYUAN_SECRET_KEY = "QnCSUP6d3Untw6B8tUt4xqkTCoEh1A9u"
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HUNYUAN_REGION = "ap-guangzhou"
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# Seedream（降级）
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SEEDREAM_API_KEY = "83a29955-c633-449f-97f1-f49b62295cf6"
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SEEDREAM_BASE_URL = "https://ark.cn-beijing.volces.com/api/v3"
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SEEDREAM_MODEL = "doubao-seedream-4-5-251128"
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# ---------- 工具函数 ----------
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def load_deepseek_key() -> str:
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"""从 nanobot 配置读取 DeepSeek API Key"""
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try:
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with open(NANOBOT_CONFIG) as f:
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config = json.load(f)
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key = config["providers"]["deepseek"]["apiKey"]
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if not key:
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raise ValueError("DeepSeek API key 为空")
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return key
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except Exception as e:
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print(f"❌ 读取 DeepSeek API Key 失败: {e}", file=sys.stderr)
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print(f" 配置文件: {NANOBOT_CONFIG}", file=sys.stderr)
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sys.exit(1)
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def call_deepseek(markdown: str, api_key: str) -> str:
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"""调用 DeepSeek API 改写文章"""
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try:
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from openai import OpenAI
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except ImportError:
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print("❌ 需要安装 openai: pip install openai", file=sys.stderr)
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sys.exit(1)
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client = OpenAI(api_key=api_key, base_url="https://api.deepseek.com")
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print("⏳ 调 DeepSeek 改写中...", file=sys.stderr)
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response = client.chat.completions.create(
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model=MODEL,
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messages=[
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": markdown},
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],
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temperature=0.7,
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max_tokens=4096,
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)
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result = response.choices[0].message.content.strip()
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tokens = response.usage.total_tokens if response.usage else "?"
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print(f" 消耗 {tokens} tokens", file=sys.stderr)
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return result
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def resolve_cover_path(cover_url: Optional[str]) -> Optional[str]:
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"""将 CMS 封面 URL 转为本地绝对路径"""
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if not cover_url:
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return None
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# cover_url 格式: /storage/admin/images/202605/xxx.jpg
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path = os.path.join(PROJECT_ROOT, "..", cover_url.lstrip("/"))
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# 某些情况下 cover_url 可能不带前导 /
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alt_path = os.path.join(PROJECT_ROOT, "storage", "admin", "images",
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*cover_url.split("/")[-2:])
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for p in [path, alt_path]:
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if os.path.exists(p):
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return os.path.normpath(p)
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print(f"⚠️ 封面图未找到: {cover_url}", file=sys.stderr)
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return None
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# ---------- 图片生成 ----------
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def _make_image_prompt(context_text: str, api_key: str) -> str:
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"""用 LLM 从文章片段生成图片 prompt"""
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try:
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from openai import OpenAI
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except ImportError:
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print("⚠️ openai 未安装，使用默认图片 prompt", file=sys.stderr)
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return "温暖的医疗健康主题插图，简洁专业的扁平设计风格"
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client = OpenAI(api_key=api_key, base_url="https://api.deepseek.com")
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try:
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response = client.chat.completions.create(
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model=MODEL,
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messages=[
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{"role": "system", "content": IMAGE_PROMPT_SYSTEM},
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{"role": "user", "content": context_text},
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],
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temperature=0.3,
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max_tokens=200,
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)
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prompt = response.choices[0].message.content.strip()
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print(f" 🎨 图片 prompt: {prompt}", file=sys.stderr)
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return prompt
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except Exception as e:
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print(f" ⚠️ 图片 prompt 生成失败: {e}", file=sys.stderr)
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return "温暖的医疗健康主题插图，简洁专业的扁平设计风格"
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def _generate_image(prompt: str, output_path: str,
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resolution: str = "1024:1024") -> bool:
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"""调用混元生图生成图片，失败则降级到 Seedream"""
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success = _hunyuan_image(prompt, output_path, resolution)
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if success:
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return True
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print(" ↪ 降级到 Seedream...", file=sys.stderr)
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return _seedream_image(prompt, output_path, resolution)
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def _hunyuan_image(prompt: str, output_path: str,
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resolution: str = "1024:1024") -> bool:
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"""腾讯混元生图 TextToImageLite"""
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try:
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from tencentcloud.common import credential
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from tencentcloud.common.exception.tencent_cloud_sdk_exception import TencentCloudSDKException
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from tencentcloud.aiart.v20221229 import aiart_client, models
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except ImportError:
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return False
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try:
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cred = credential.Credential(HUNYUAN_SECRET_ID, HUNYUAN_SECRET_KEY)
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client = aiart_client.AiartClient(cred, HUNYUAN_REGION)
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req = models.TextToImageLiteRequest()
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req.Prompt = prompt
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req.Resolution = resolution
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req.LogoAdd = 0
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req.RspImgType = "url"
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resp = client.TextToImageLite(req)
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result_url = resp.ResultImage
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if not result_url:
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return False
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import requests
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r = requests.get(result_url, timeout=60)
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r.raise_for_status()
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os.makedirs(os.path.dirname(output_path) or ".", exist_ok=True)
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with open(output_path, "wb") as f:
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f.write(r.content)
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print(f" ✅ 混元生图: {os.path.basename(output_path)} ({len(r.content)} bytes)", file=sys.stderr)
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return True
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except TencentCloudSDKException as e:
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print(f" ⚠️ 混元: {e}", file=sys.stderr)
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return False
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except Exception as e:
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print(f" ⚠️ 混元异常: {e}", file=sys.stderr)
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return False
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def _seedream_image(prompt: str, output_path: str,
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resolution: str = "1024:1024") -> bool:
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"""Seedream (火山引擎 ARK)"""
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import requests
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# Seedream 要求 ≥ 3,686,400 像素，自动放大
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parts = resolution.replace(":", "x").split("x")
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if len(parts) == 2:
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try:
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w, h = int(parts[0]), int(parts[1])
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if w * h < 3686400:
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scale = (3686400 / (w * h)) ** 0.5
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w2, h2 = (int(w * scale) // 16) * 16, (int(h * scale) // 16) * 16
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resolution = f"{w2}x{h2}"
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except ValueError:
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pass
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try:
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url = f"{SEEDREAM_BASE_URL}/images/generations"
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resp = requests.post(
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url,
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headers={
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"Authorization": f"Bearer {SEEDREAM_API_KEY}",
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"Content-Type": "application/json",
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},
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json={
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"model": SEEDREAM_MODEL,
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"prompt": prompt,
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"size": resolution,
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"n": 1,
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},
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timeout=120,
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)
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resp.raise_for_status()
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data = resp.json()
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image_url = data["data"][0].get("url")
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if not image_url:
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return False
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r = requests.get(image_url, timeout=60)
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r.raise_for_status()
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os.makedirs(os.path.dirname(output_path) or ".", exist_ok=True)
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with open(output_path, "wb") as f:
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f.write(r.content)
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print(f" ✅ Seedream: {os.path.basename(output_path)} ({len(r.content)} bytes)", file=sys.stderr)
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return True
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except Exception as e:
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print(f" ⚠️ Seedream: {e}", file=sys.stderr)
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return False
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def _find_image_spots(markdown: str, count: int = 2) -> list:
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"""在 markdown 中找适合插入图片的位置，返回 [(插入点索引, 上下文), ...]"""
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lines = markdown.split("\n")
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candidates = []
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# 找段落间的空行位置作为候选
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for i in range(1, len(lines) - 1):
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# 空行 + 下一行有内容 → 段落边界
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if lines[i].strip() == "" and i + 1 < len(lines) and lines[i + 1].strip():
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# 确保不在标题前插入（标题通常以 ## 开头）
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if not lines[i + 1].strip().startswith("#"):
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# 取前后各 3 行作为上下文
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ctx_start = max(0, i - 3)
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ctx_end = min(len(lines), i + 4)
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context = "\n".join(lines[ctx_start:ctx_end])
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candidates.append((i + 1, context)) # 在空行后插入
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# 均匀选取 count 个点
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if not candidates:
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return []
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if len(candidates) <= count:
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return candidates
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# 均匀分布：从中间选取
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step = len(candidates) // count
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selected = [candidates[step * j] for j in range(count)]
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return selected
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def _insert_images(markdown: str, api_key: str) -> str:
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"""分析 markdown、生成配图并插入，返回新 markdown"""
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if not IMAGE_GEN_ENABLED:
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return markdown
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spots = _find_image_spots(markdown, IMAGE_GEN_COUNT)
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if not spots:
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print(" ℹ️ 未找到合适的图片插入位置", file=sys.stderr)
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return markdown
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print(f" 🖼️ 计划插入 {len(spots)} 张配图", file=sys.stderr)
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lines = markdown.split("\n")
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generated: list[tuple[int, str]] = [] # (line_index, image_path)
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for idx, (line_idx, context) in enumerate(spots):
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# 生成图片 prompt
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img_prompt = _make_image_prompt(context, api_key)
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# 输出路径：storage/admin/images/YYYYMM/
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month_dir = time.strftime("%Y%m")
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filename = f"ai_gen_{int(time.time())}_{idx}.jpg"
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output_dir = os.path.join(IMAGE_STORAGE_DIR, month_dir)
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output_path = os.path.join(output_dir, filename)
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ok = _generate_image(img_prompt, output_path)
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if ok:
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# 相对路径用于 markdown
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web_path = f"/storage/admin/images/{month_dir}/{filename}"
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generated.append((line_idx, web_path))
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else:
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print(f" ❌ 图片 {idx+1} 生成失败，跳过", file=sys.stderr)
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if not generated:
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return markdown
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# 从后往前插入（避免索引偏移）
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for line_idx, web_path in sorted(generated, key=lambda x: x[0], reverse=True):
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img_md = f"\n\n"
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lines.insert(line_idx, img_md)
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return "\n".join(lines)
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# ---------- 微信图片上传 ----------
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def _upload_html_images(html: str, publisher) -> str:
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"""找出 HTML 中以 /storage/ 开头的本地图片，上传到微信并替换为 CDN 地址"""
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import re
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from html import unescape
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# 匹配 <img src="..." ...> 标签，提取 src 和整个标签
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img_pattern = re.compile(r'<img[^>]+src=["\']([^"\']+)["\'][^>]*>', re.IGNORECASE)
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storage_base = PROJECT_ROOT
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def upload_and_replace(match):
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full_tag = match.group(0)
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src = match.group(1)
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# 只处理以 /storage/ 开头的本地路径
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if not src.startswith("/storage/"):
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return full_tag
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local_path = os.path.normpath(os.path.join(storage_base, src.lstrip("/")))
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if not os.path.exists(local_path):
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print(f" ⚠️ 图片不存在: {local_path}", file=sys.stderr)
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return full_tag
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try:
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resp = publisher.upload_image(local_path, upload_type="body")
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wechat_url = resp.get("url", "")
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if wechat_url:
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print(f" ✅ 已上传到微信: {os.path.basename(src)}", file=sys.stderr)
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# 替换整个 img 标签，只改 src
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return full_tag.replace(src, wechat_url)
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else:
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print(f" ❌ 微信未返回URL: {os.path.basename(src)}", file=sys.stderr)
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return full_tag
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except Exception as e:
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print(f" ❌ 上传失败 {os.path.basename(src)}: {e}", file=sys.stderr)
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return full_tag
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result = img_pattern.sub(upload_and_replace, html)
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return result
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# ---------- 核心流程 ----------
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def run_pipeline(
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article_id: int,
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rewritten_content: Optional[str] = None,
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) -> Dict[str, Any]:
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"""
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一键改写→发布流程。
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Args:
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article_id: CMS 文章 ID
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rewritten_content: 预改写好的 markdown。None 则自动调 LLM
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Returns:
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{"article_id": ..., "title": ..., "media_id": ..., "db_updated": bool}
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"""
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# 1. 查文章
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article = get_article_full(article_id)
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if not article:
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raise ValueError(f"文章 ID={article_id} 不存在")
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title = article["title"]
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author = article.get("author") or "尿大夫"
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digest = article.get("summary") or ""
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cover_url = article.get("cover_image") or ""
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original = article["content"]
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print(f"📄 文章: ID={article_id} | {title}", file=sys.stderr)
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print(f" 原文字数: {len(original)} chars", file=sys.stderr)
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# 2. 改写（两种模式）
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if rewritten_content:
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print(" 模式: 手动改写（跳过 LLM）", file=sys.stderr)
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new_content = rewritten_content
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else:
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print(" 模式: 自动改写（DeepSeek）", file=sys.stderr)
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api_key = load_deepseek_key()
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new_content = call_deepseek(original, api_key)
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print(f" 改写后字数: {len(new_content)} chars", file=sys.stderr)
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|
||
# 2.5 生成配图并插入
|
||
print("🎨 生成配图...", file=sys.stderr)
|
||
if rewritten_content:
|
||
new_content = _insert_images(new_content, load_deepseek_key())
|
||
else:
|
||
new_content = _insert_images(new_content, api_key)
|
||
|
||
# 3. 转 HTML
|
||
publisher = WeChatPublisher()
|
||
html_content = publisher.markdown_to_html(new_content)
|
||
|
||
# 3.5 上传 HTML 中的本地图片到微信（将 /storage/... 转为微信 CDN 地址）
|
||
html_content = _upload_html_images(html_content, publisher)
|
||
|
||
# 4. 解析封面图路径
|
||
cover_path = resolve_cover_path(cover_url)
|
||
|
||
# 5. 发布公众号草稿
|
||
print("🚀 发布公众号草稿...", file=sys.stderr)
|
||
media_id = publisher.create_draft(
|
||
title=title,
|
||
content=html_content,
|
||
author=author,
|
||
digest=digest,
|
||
cover_image_path=cover_path,
|
||
)
|
||
print(f" media_id: {media_id}", file=sys.stderr)
|
||
|
||
# 6. 更新数据库
|
||
ok = update_article_content(article_id, new_content, status="published")
|
||
print(f"{'✅' if ok else '❌'} 数据库更新: {'成功' if ok else '失败'}", file=sys.stderr)
|
||
|
||
return {
|
||
"article_id": article_id,
|
||
"title": title,
|
||
"media_id": media_id,
|
||
"db_updated": ok,
|
||
}
|
||
|
||
|
||
# ---------- CLI ----------
|
||
|
||
def main():
|
||
parser = argparse.ArgumentParser(
|
||
description="CMS 草稿改写 → 公众号发布 一键流水线"
|
||
)
|
||
parser.add_argument("article_id", type=int, help="CMS 文章 ID")
|
||
parser.add_argument(
|
||
"--content-file", "-f",
|
||
help="改写好的 markdown 文件路径。不传则自动调 DeepSeek 改写"
|
||
)
|
||
parser.add_argument(
|
||
"--dry-run", action="store_true",
|
||
help="只改写不发布，输出改写结果到 stdout"
|
||
)
|
||
args = parser.parse_args()
|
||
|
||
# 读取改写内容
|
||
rewritten = None
|
||
if args.content_file:
|
||
with open(args.content_file) as f:
|
||
rewritten = f.read()
|
||
|
||
# 干跑模式：只改写输出
|
||
if args.dry_run:
|
||
article = get_article_full(args.article_id)
|
||
if not article:
|
||
print(f"❌ 文章 ID={args.article_id} 不存在", file=sys.stderr)
|
||
sys.exit(1)
|
||
api_key = load_deepseek_key()
|
||
result = call_deepseek(article["content"], api_key)
|
||
print(result)
|
||
return
|
||
|
||
# 正式执行
|
||
try:
|
||
result = run_pipeline(args.article_id, rewritten)
|
||
print(json.dumps(result, ensure_ascii=False, indent=2), flush=True)
|
||
except Exception as e:
|
||
print(f"❌ 流水线失败: {e}", file=sys.stderr)
|
||
sys.exit(1)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|