910 lines
35 KiB
Python
910 lines
35 KiB
Python
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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文章与图片智能挂靠脚本
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根据文章标签匹配ai_image_tags表中的图片，使用大模型进行处理，
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如果挂靠失败或没有相同标签的图片，则使用Gemini生成图片
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"""
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import json
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import os
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import re
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import requests
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import csv
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import pymysql
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from typing import List, Dict, Tuple, Optional
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from collections import defaultdict
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from database_config import db_manager
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from log_config import setup_logger
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import time
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import random
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from requests.adapters import HTTPAdapter
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from urllib3.util.retry import Retry
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def get_articles_with_tags_from_db() -> List[Dict]:
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"""
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从数据库获取文章及其标签
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Returns:
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包含文章信息的字典列表
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"""
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# 设置日志记录器
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logger = setup_logger('article_matching', 'logs/article_matching.log', 'logs/article_matching_error.log')
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articles = []
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try:
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# 查询审核通过的文章，包含内容和标签
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sql = """
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SELECT id, title, content, coze_tag
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FROM ai_articles
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WHERE status = 'approved'
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ORDER BY id
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"""
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logger.info("开始查询审核通过的文章数据...")
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results = db_manager.execute_query(sql)
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if not results:
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logger.warning("没有找到状态为approved的文章")
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print("没有找到状态为approved的文章")
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return articles
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logger.info(f"查询到 {len(results)} 条审核通过的文章")
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print(f"查询到 {len(results)} 条审核通过的文章")
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for row in results:
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article_id, title, content, coze_tag = row
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# 解析标签
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tags = []
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if coze_tag:
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try:
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# 尝试解析JSON格式的标签
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tags_data = json.loads(coze_tag)
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if isinstance(tags_data, list):
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tags = tags_data
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elif isinstance(tags_data, dict):
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# 如果是字典格式，提取值
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tags = list(tags_data.values()) if isinstance(list(tags_data.values())[0], list) else list(tags_data.values())
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else:
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# 如果是字符串，尝试按逗号分割
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tags = [tag.strip() for tag in str(tags_data).split(',') if tag.strip()]
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except json.JSONDecodeError:
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# 如果不是JSON格式，按逗号分割
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tags = [tag.strip() for tag in str(coze_tag).split(',') if tag.strip()]
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articles.append({
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'id': article_id,
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'title': title,
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'content': content,
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'tags': tags
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})
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except Exception as e:
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logger.error(f"从数据库获取文章数据时发生错误: {e}", exc_info=True)
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print(f"从数据库获取文章数据时发生错误: {e}")
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raise
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return articles
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def get_images_by_tags_from_db(tags: List[str] = [], used_counts: Dict[str, int] = {}) -> List[Dict]:
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"""
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从数据库根据标签获取图片
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Args:
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tags: 标签列表
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used_counts: 已使用次数的字典，key为图片ID，value为使用次数
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Returns:
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包含图片信息的字典列表
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"""
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if not tags:
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return []
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# 设置日志记录器
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logger = setup_logger('article_matching', 'logs/article_matching.log', 'logs/article_matching_error.log')
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images = []
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try:
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# 查询符合条件的图像标签数据
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sql = """
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SELECT id, image_id, image_name, image_url, tag_name, keywords_name, department_name, image_attached_article_count
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FROM ai_image_tags
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WHERE image_attached_article_count < 5
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ORDER BY id
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"""
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logger.info("开始查询符合条件的图像标签数据...")
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results = db_manager.execute_query(sql)
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if not results:
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logger.warning("没有找到符合条件的图像标签数据 (image_attached_article_count < 5)")
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print("没有找到符合条件的图像标签数据 (image_attached_article_count < 5)")
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return images
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logger.info(f"查询到 {len(results)} 条符合条件的图像标签数据")
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print(f"查询到 {len(results)} 条符合条件的图像标签数据")
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for row in results:
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(
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image_id, db_image_id, image_name, image_url, tag_name,
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keywords_name, department_name, base_count
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) = row
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# 检查图片的附加文章数量是否小于5，考虑已使用次数
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used_count = used_counts.get(str(image_id), 0)
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total_count = base_count + used_count
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if total_count >= 5:
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continue
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# 检查标签是否匹配
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if any(tag.lower() in tag_name.lower() for tag in tags):
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images.append({
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'id': str(image_id),
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'image_id': db_image_id,
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'image_name': image_name,
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'image_url': image_url,
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'tag_name': tag_name,
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'keywords_name': keywords_name,
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'department_name': department_name,
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'base_count': base_count
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})
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except Exception as e:
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logger.error(f"从数据库获取图片数据时发生错误: {e}", exc_info=True)
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print(f"从数据库获取图片数据时发生错误: {e}")
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raise
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print(f"从数据库找到 {len(images)} 张符合条件的匹配图片")
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return images
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def call_qwen_model(article: Dict, image_urls: List[str]) -> bool:
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"""
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调用通义千问大模型进行文章与图片挂靠评估
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Args:
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article: 文章信息
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image_urls: 图片URL列表
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Returns:
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挂靠是否成功
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"""
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# 通义千问API配置
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api_key = "sk-e6a38204022a4b538b8954f0584712af"
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api_url = "https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation"
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# 构建请求内容
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content = f"""
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请评估以下文章与图片的匹配度：
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文章标题: {article['title']}
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文章内容: {article['content'][:500]}... # 限制内容长度
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图片URLs: {', '.join(image_urls)}
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请判断这些图片是否适合用于这篇文章。如果匹配度高，请回复"匹配成功"；如果匹配度低，请回复"匹配失败"。
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"""
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headers = {
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'Authorization': f'Bearer {api_key}',
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'Content-Type': 'application/json'
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}
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payload = {
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"model": "qwen-max", # 或其他合适的模型
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"input": {
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"messages": [
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{
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"role": "user",
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"content": content
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}
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]
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},
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"parameters": {
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"temperature": 0.7
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}
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}
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try:
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response = requests.post(api_url, headers=headers, json=payload)
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if response.status_code == 200:
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result = response.json()
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# 解析响应，判断匹配结果
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if 'output' in result and 'text' in result['output']:
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response_text = result['output']['text'].lower()
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# 根据响应内容判断是否匹配
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if '匹配成功' in response_text or '是的' in response_text or '合适' in response_text:
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print(f"通义千问评估结果: 匹配成功 - 文章 '{article['title']}'")
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return True
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else:
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print(f"通义千问评估结果: 匹配失败 - 文章 '{article['title']}'")
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return False
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else:
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print(f"通义千问API响应格式异常: {result}")
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return False
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else:
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print(f"通义千问API调用失败: {response.status_code} - {response.text}")
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# API调用失败时，仍然尝试匹配，这里返回False触发图片生成
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return False
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except Exception as e:
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print(f"调用通义千问API时发生错误: {e}")
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# 发生错误时，返回False以触发图片生成
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return False
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def insert_generated_image_to_db(image_name: str, image_url: str, article_tags: List[str]) -> Optional[Dict]:
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"""
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将Gemini生成的图片信息插入数据库
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Args:
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image_name: 图片文件名，如 "1755310671174988.png"
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image_url: 图片URL路径，如 "20250816/1755310671174988.png"
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article_tags: 文章标签列表，用于查询department和keywords
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Returns:
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包含插入信息的字典：{
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'tag_image_id': tag_image_id,
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'image_id': image_id,
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'image_url': image_url,
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'image_thumb_url': image_thumb_url,
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'keywords_id': keywords_id,
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'keywords_name': keywords_name,
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'department_id': department_id,
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'department_name': department_name
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}
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"""
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connection = db_manager.get_connection()
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if connection is None:
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print("无法连接到数据库")
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return None
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try:
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with connection.cursor(pymysql.cursors.DictCursor) as cursor:
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# 1. 根据文章标签查询ai_image_tags表，获取department和keywords信息
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if article_tags:
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# 使用第一个标签查询
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query = """
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SELECT department_name, keywords_name, department_id, keywords_id, tag_id
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FROM ai_image_tags
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WHERE tag_name = %s
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LIMIT 1
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"""
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cursor.execute(query, (article_tags[0],))
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tag_info = cursor.fetchone()
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if tag_info:
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department = tag_info['department_name']
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keywords = tag_info['keywords_name']
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department_id = tag_info['department_id']
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keywords_id = tag_info['keywords_id']
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tag_id = tag_info['tag_id']
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tag_name = article_tags[0]
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else:
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# 如果没有找到，使用默认值
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department = "AI生成"
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keywords = "AI图片"
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department_id = 1
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keywords_id = 1
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tag_id = 1
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tag_name = article_tags[0] if article_tags else "AI生成"
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else:
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# 没有标签，使用默认值
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department = "AI生成"
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keywords = "AI图片"
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department_id = 1
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keywords_id = 1
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tag_id = 1
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tag_name = "AI生成"
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# 2. 插入ai_images表
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insert_image_query = """
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INSERT INTO ai_images
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(image_name, image_url, image_thumb_url, department, keywords, image_type, upload_user_id, status)
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VALUES (%s, %s, %s, %s, %s, %s, %s, %s)
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"""
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cursor.execute(insert_image_query, (
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image_name,
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image_url,
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'', # image_thumb_url
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department,
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keywords,
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'medical', # image_type
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1, # upload_user_id（默认用户ID）
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'active' # status
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))
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image_id = cursor.lastrowid
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print(f"图片信息已插入ai_images表，image_id: {image_id}")
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# 3. 插入ai_image_tags表
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insert_tag_query = """
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INSERT INTO ai_image_tags
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(image_id, image_name, image_url, image_thumb_url, tag_id, tag_name,
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keywords_id, keywords_name, department_id, department_name,
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image_source, created_user_id, image_attached_article_count)
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VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
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"""
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cursor.execute(insert_tag_query, (
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image_id,
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image_name,
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image_url,
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|
|
'', # image_thumb_url
|
|||
|
|
tag_id,
|
|||
|
|
tag_name,
|
|||
|
|
keywords_id,
|
|||
|
|
keywords,
|
|||
|
|
department_id,
|
|||
|
|
department,
|
|||
|
|
3, # image_source: 3表示AI生成
|
|||
|
|
1, # created_user_id
|
|||
|
|
0 # image_attached_article_count
|
|||
|
|
))
|
|||
|
|
tag_image_id = cursor.lastrowid
|
|||
|
|
print(f"图片标签信息已插入ai_image_tags表，tag_image_id: {tag_image_id}")
|
|||
|
|
|
|||
|
|
# 提交事务
|
|||
|
|
connection.commit()
|
|||
|
|
|
|||
|
|
# 返回包含所有需要信息的字典
|
|||
|
|
return {
|
|||
|
|
'tag_image_id': tag_image_id,
|
|||
|
|
'image_id': image_id,
|
|||
|
|
'image_url': image_url,
|
|||
|
|
'image_thumb_url': '',
|
|||
|
|
'keywords_id': keywords_id,
|
|||
|
|
'keywords_name': keywords,
|
|||
|
|
'department_id': department_id,
|
|||
|
|
'department_name': department
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"插入图片信息到数据库失败: {e}")
|
|||
|
|
connection.rollback()
|
|||
|
|
return None
|
|||
|
|
finally:
|
|||
|
|
connection.close()
|
|||
|
|
|
|||
|
|
|
|||
|
|
def insert_article_image_relation(article_id: int, image_id: int, image_url: str, image_thumb_url: str,
|
|||
|
|
tag_image_id: int, keywords_id: int, keywords_name: str,
|
|||
|
|
department_id: int, department_name: str, image_source: int = 0) -> Optional[int]:
|
|||
|
|
"""
|
|||
|
|
将文章与图片的关联信息插入ai_article_images表
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
article_id: 文章ID
|
|||
|
|
image_id: 图片ID（ai_images表的id）
|
|||
|
|
image_url: 图片URL
|
|||
|
|
image_thumb_url: 缩略图URL
|
|||
|
|
tag_image_id: 图片标签ID（ai_image_tags表的id）
|
|||
|
|
keywords_id: 关键词ID
|
|||
|
|
keywords_name: 关键词名称
|
|||
|
|
department_id: 部门ID
|
|||
|
|
department_name: 部门名称
|
|||
|
|
image_source: 图片来源（0表示默认）
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
插入的ai_article_images表的ID
|
|||
|
|
"""
|
|||
|
|
connection = db_manager.get_connection()
|
|||
|
|
if connection is None:
|
|||
|
|
print("无法连接到数据库")
|
|||
|
|
return None
|
|||
|
|
|
|||
|
|
try:
|
|||
|
|
with connection.cursor(pymysql.cursors.DictCursor) as cursor:
|
|||
|
|
# 1. 查询当前文章下已有图片的最大sort_order
|
|||
|
|
query_max_sort = """
|
|||
|
|
SELECT COALESCE(MAX(sort_order), 0) as max_sort_order
|
|||
|
|
FROM ai_article_images
|
|||
|
|
WHERE article_id = %s
|
|||
|
|
"""
|
|||
|
|
cursor.execute(query_max_sort, (article_id,))
|
|||
|
|
result = cursor.fetchone()
|
|||
|
|
max_sort_order = result['max_sort_order'] if result else 0
|
|||
|
|
new_sort_order = max_sort_order + 1
|
|||
|
|
|
|||
|
|
print(f"文章 {article_id} 当前最大sort_order: {max_sort_order}, 新图片sort_order: {new_sort_order}")
|
|||
|
|
|
|||
|
|
# 2. 插入ai_article_images表
|
|||
|
|
insert_query = """
|
|||
|
|
INSERT INTO ai_article_images
|
|||
|
|
(article_id, image_id, image_url, image_thumb_url, image_tag_id, sort_order,
|
|||
|
|
keywords_id, keywords_name, department_id, department_name, image_source)
|
|||
|
|
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
|
|||
|
|
"""
|
|||
|
|
cursor.execute(insert_query, (
|
|||
|
|
article_id,
|
|||
|
|
image_id,
|
|||
|
|
image_url,
|
|||
|
|
image_thumb_url,
|
|||
|
|
tag_image_id,
|
|||
|
|
new_sort_order,
|
|||
|
|
keywords_id,
|
|||
|
|
keywords_name,
|
|||
|
|
department_id,
|
|||
|
|
department_name,
|
|||
|
|
image_source
|
|||
|
|
))
|
|||
|
|
article_image_id = cursor.lastrowid
|
|||
|
|
print(f"文章图片关联信息已插入ai_article_images表，id: {article_image_id}")
|
|||
|
|
|
|||
|
|
# 提交事务
|
|||
|
|
connection.commit()
|
|||
|
|
|
|||
|
|
return article_image_id
|
|||
|
|
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"插入文章图片关联信息失败: {e}")
|
|||
|
|
connection.rollback()
|
|||
|
|
return None
|
|||
|
|
finally:
|
|||
|
|
connection.close()
|
|||
|
|
|
|||
|
|
|
|||
|
|
def generate_image_with_gemini(prompt: str, article_tags: List[str], article_id: int) -> str:
|
|||
|
|
"""
|
|||
|
|
使用Gemini生成图片并上传到服务器
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
prompt: 图片生成提示词
|
|||
|
|
article_tags: 文章标签列表，用于查询department和keywords
|
|||
|
|
article_id: 文章ID，用于关联图片
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
上传后的图片URL
|
|||
|
|
"""
|
|||
|
|
# 导入必要的库
|
|||
|
|
try:
|
|||
|
|
from google import genai
|
|||
|
|
from google.genai import types
|
|||
|
|
from google.genai.client import HttpOptions
|
|||
|
|
|
|||
|
|
except ImportError:
|
|||
|
|
print("错误：未安装google-genai库，请运行 'pip install google-genai' 进行安装")
|
|||
|
|
raise
|
|||
|
|
|
|||
|
|
client = genai.Client(http_options=HttpOptions(base_url="https://work.poloapi.com"),
|
|||
|
|
api_key="sk-V4tPnDgzFPa7nxWrvKnNJsW8ZcBXXPuGmjfgvPVRnwpHoeob")
|
|||
|
|
|
|||
|
|
print(f"正在调用Gemini API生成图片，提示词: {prompt[:50]}...")
|
|||
|
|
|
|||
|
|
# 生成内容
|
|||
|
|
response = client.models.generate_content(
|
|||
|
|
model="gemini-3-pro-image-preview",
|
|||
|
|
contents=[prompt],
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# 检查是否有候选答案
|
|||
|
|
if not response.candidates:
|
|||
|
|
raise Exception("Gemini API未返回任何候选答案")
|
|||
|
|
|
|||
|
|
# 处理响应 - 遍历第一个候选答案的内容部分
|
|||
|
|
candidate = response.candidates[0]
|
|||
|
|
if not candidate.content or not candidate.content.parts:
|
|||
|
|
raise Exception("Gemini API返回的候选答案中没有内容部分")
|
|||
|
|
|
|||
|
|
for part in candidate.content.parts:
|
|||
|
|
if hasattr(part, 'text') and part.text is not None:
|
|||
|
|
print(f"Gemini响应文本: {part.text}")
|
|||
|
|
elif hasattr(part, 'inline_data') and part.inline_data is not None:
|
|||
|
|
image_data = part.inline_data
|
|||
|
|
if image_data.data is not None:
|
|||
|
|
# 生成唯一的文件名（基于时间戳）
|
|||
|
|
import time
|
|||
|
|
import os
|
|||
|
|
from datetime import datetime
|
|||
|
|
|
|||
|
|
timestamp_ms = int(time.time() * 1000) # 毫秒级时间戳
|
|||
|
|
image_filename = f"{timestamp_ms}.png"
|
|||
|
|
today_date = datetime.now().strftime("%Y%m%d")
|
|||
|
|
image_url_path = f"{today_date}/{image_filename}"
|
|||
|
|
|
|||
|
|
temp_filename = f"temp_generated_image_{timestamp_ms}.png"
|
|||
|
|
# 保存图片数据到临时文件
|
|||
|
|
with open(temp_filename, 'wb') as f:
|
|||
|
|
f.write(image_data.data)
|
|||
|
|
print(f"Gemini生成图片成功: {temp_filename}")
|
|||
|
|
|
|||
|
|
# 先将图片信息插入数据库，获取相关信息
|
|||
|
|
image_info = insert_generated_image_to_db(image_filename, image_url_path, article_tags)
|
|||
|
|
|
|||
|
|
if not image_info:
|
|||
|
|
raise Exception("插入图片信息到数据库失败")
|
|||
|
|
|
|||
|
|
print(f"图片信息已插入数据库，tag_image_id: {image_info['tag_image_id']}, image_id: {image_info['image_id']}")
|
|||
|
|
|
|||
|
|
# 使用tag_image_id上传图片到服务器
|
|||
|
|
uploaded_url = upload_image_to_server(temp_filename, image_info['tag_image_id'])
|
|||
|
|
|
|||
|
|
# 将文章与图片的关联信息插入ai_article_images表
|
|||
|
|
article_image_id = insert_article_image_relation(
|
|||
|
|
article_id=article_id,
|
|||
|
|
image_id=image_info['image_id'],
|
|||
|
|
image_url=image_info['image_url'],
|
|||
|
|
image_thumb_url=image_info['image_thumb_url'],
|
|||
|
|
tag_image_id=image_info['tag_image_id'],
|
|||
|
|
keywords_id=image_info['keywords_id'],
|
|||
|
|
keywords_name=image_info['keywords_name'],
|
|||
|
|
department_id=image_info['department_id'],
|
|||
|
|
department_name=image_info['department_name'],
|
|||
|
|
image_source=0 # 默认值
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
if article_image_id:
|
|||
|
|
print(f"文章图片关联信息已创建，ai_article_images.id: {article_image_id}")
|
|||
|
|
|
|||
|
|
# 删除临时文件
|
|||
|
|
os.remove(temp_filename)
|
|||
|
|
|
|||
|
|
print(f"图片已上传到服务器: {uploaded_url}")
|
|||
|
|
# 返回上传后的图片URL
|
|||
|
|
return uploaded_url
|
|||
|
|
|
|||
|
|
# 如果没有返回图片数据，抛出异常
|
|||
|
|
raise Exception("Gemini API未返回有效的图片数据")
|
|||
|
|
|
|||
|
|
|
|||
|
|
def upload_image_to_server(image_path: str, tag_image_id: int) -> str:
|
|||
|
|
"""
|
|||
|
|
上传图片到服务器
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
image_path: 本地图片路径
|
|||
|
|
tag_image_id: 图片标签ID
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
服务器上的图片URL
|
|||
|
|
"""
|
|||
|
|
import requests
|
|||
|
|
import json
|
|||
|
|
|
|||
|
|
# 登录获取JWT token
|
|||
|
|
base_url = "http://47.99.184.230:8324" # 使用外网API地址
|
|||
|
|
jwt_token = login_and_get_jwt_token(base_url)
|
|||
|
|
|
|||
|
|
if not jwt_token:
|
|||
|
|
raise Exception("获取JWT token失败，无法上传图片")
|
|||
|
|
|
|||
|
|
# 准备上传请求
|
|||
|
|
upload_url = f"{base_url}/api/images/upload"
|
|||
|
|
headers = {
|
|||
|
|
'Authorization': f'Bearer {jwt_token}',
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
# 读取图片文件
|
|||
|
|
with open(image_path, 'rb') as image_file:
|
|||
|
|
files = {'file': image_file}
|
|||
|
|
data = {'tag_image_id': tag_image_id} # 添加必传参数
|
|||
|
|
|
|||
|
|
response = requests.post(upload_url, headers=headers, files=files, data=data)
|
|||
|
|
|
|||
|
|
print(f"图片上传响应状态码: {response.status_code}")
|
|||
|
|
print(f"图片上传响应内容: {response.text}")
|
|||
|
|
|
|||
|
|
if response.status_code == 200:
|
|||
|
|
result = response.json()
|
|||
|
|
if result.get('code') == 200:
|
|||
|
|
# 返回服务器上的图片URL
|
|||
|
|
return result['data']['http_image_url']
|
|||
|
|
else:
|
|||
|
|
raise Exception(f"图片上传失败: {result.get('message', '未知错误')}")
|
|||
|
|
else:
|
|||
|
|
raise Exception(f"图片上传请求失败，状态码: {response.status_code}, 响应: {response.text}")
|
|||
|
|
|
|||
|
|
|
|||
|
|
def login_and_get_jwt_token(base_url: str) -> Optional[str]:
|
|||
|
|
"""
|
|||
|
|
登录获取JWT token
|
|||
|
|
"""
|
|||
|
|
login_url = f"{base_url}/api/auth/login"
|
|||
|
|
login_data = {
|
|||
|
|
"username": "user010", # 使用固定的账号
|
|||
|
|
"password": "@5^2W6R7"
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
print(f"尝试登录: {login_data['username']}")
|
|||
|
|
print(f"登录URL: {login_url}")
|
|||
|
|
|
|||
|
|
try:
|
|||
|
|
response = requests.post(login_url, json=login_data, headers={'Content-Type': 'application/json'})
|
|||
|
|
print(f"响应状态码: {response.status_code}")
|
|||
|
|
|
|||
|
|
if response.status_code == 200:
|
|||
|
|
result = response.json()
|
|||
|
|
if result.get('code') == 200:
|
|||
|
|
jwt_token = result['data']['token']
|
|||
|
|
print("JWT token获取成功")
|
|||
|
|
return jwt_token
|
|||
|
|
else:
|
|||
|
|
print(f"登录失败: {result.get('message', '未知错误')}")
|
|||
|
|
return None
|
|||
|
|
else:
|
|||
|
|
print(f"登录请求失败: {response.status_code}")
|
|||
|
|
return None
|
|||
|
|
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"登录异常: {e}")
|
|||
|
|
return None
|
|||
|
|
|
|||
|
|
|
|||
|
|
def batch_publish_articles(base_url: str, jwt_token: str, article_ids: List[int]) -> bool:
|
|||
|
|
"""
|
|||
|
|
批量提交文章到/api/articles/batch-publish-auto接口
|
|||
|
|
"""
|
|||
|
|
try:
|
|||
|
|
print(f"开始批量提交 {len(article_ids)} 篇文章到batch-publish-auto接口")
|
|||
|
|
|
|||
|
|
# 构建批量发布数据
|
|||
|
|
publish_data = {
|
|||
|
|
"article_ids": article_ids
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
print(f"准备批量提交的数据: {json.dumps(publish_data, ensure_ascii=False)}")
|
|||
|
|
|
|||
|
|
# 发送请求
|
|||
|
|
upload_url = f"{base_url}/api/articles/batch-publish-auto"
|
|||
|
|
headers = {
|
|||
|
|
'Authorization': f'Bearer {jwt_token}',
|
|||
|
|
'Content-Type': 'application/json',
|
|||
|
|
'Accept': 'application/json'
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
response = requests.post(upload_url, json=publish_data, headers=headers)
|
|||
|
|
|
|||
|
|
print(f"批量提交响应状态码: {response.status_code}")
|
|||
|
|
|
|||
|
|
if response.status_code == 200:
|
|||
|
|
try:
|
|||
|
|
result = response.json()
|
|||
|
|
print(f"批量提交响应内容: {result}")
|
|||
|
|
|
|||
|
|
# 根据接口实际返回格式判断成功
|
|||
|
|
if result.get('code') == 200:
|
|||
|
|
data = result.get('data', {})
|
|||
|
|
published_count = data.get('published_count', 0)
|
|||
|
|
failed_count = data.get('failed_count', 0)
|
|||
|
|
|
|||
|
|
success_msg = f"批量提交成功，发布: {published_count}篇，失败: {failed_count}篇"
|
|||
|
|
print(success_msg)
|
|||
|
|
return True
|
|||
|
|
else:
|
|||
|
|
print(f"批量提交失败: {result.get('message', '未知错误')}")
|
|||
|
|
return False
|
|||
|
|
except json.JSONDecodeError as e:
|
|||
|
|
print(f"解析批量提交响应失败: {e}")
|
|||
|
|
return False
|
|||
|
|
elif response.status_code == 401:
|
|||
|
|
# Token过期
|
|||
|
|
print("收到401错误，JWT token可能已过期")
|
|||
|
|
return False
|
|||
|
|
else:
|
|||
|
|
print(f"批量提交请求失败，状态码: {response.status_code}")
|
|||
|
|
return False
|
|||
|
|
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"批量提交异常: {e}")
|
|||
|
|
return False
|
|||
|
|
|
|||
|
|
|
|||
|
|
def process_single_article(article, used_image_counts, match_results):
|
|||
|
|
"""
|
|||
|
|
处理单个文章与图片的匹配和挂靠
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
article: 单个文章数据
|
|||
|
|
used_image_counts: 图片使用计数
|
|||
|
|
match_results: 匹配结果列表
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
是否处理成功
|
|||
|
|
"""
|
|||
|
|
print(f"\n处理文章: {article['title']} (ID: {article['id']})")
|
|||
|
|
|
|||
|
|
# 根据文章标签获取匹配的图片（考虑已使用次数）
|
|||
|
|
matched_images = get_images_by_tags_from_db(article['tags'], used_image_counts)
|
|||
|
|
|
|||
|
|
if matched_images:
|
|||
|
|
print(f"找到 {len(matched_images)} 张符合条件的匹配图片")
|
|||
|
|
|
|||
|
|
# 按基础使用次数排序，优先使用基础计数较低的图片
|
|||
|
|
matched_images.sort(key=lambda x: x['base_count'])
|
|||
|
|
|
|||
|
|
matched = False
|
|||
|
|
for img in matched_images:
|
|||
|
|
# 提取图片URL并添加前缀
|
|||
|
|
image_url = "http://images11.bxmkb.cn/Images/" + img['image_url']
|
|||
|
|
|
|||
|
|
if image_url: # 确保图片URL存在
|
|||
|
|
# 调用通义千问大模型进行挂靠评估
|
|||
|
|
match_success = call_qwen_model(article, [image_url])
|
|||
|
|
|
|||
|
|
if match_success:
|
|||
|
|
print(f"文章与图片挂靠成功: {article['title']}")
|
|||
|
|
|
|||
|
|
# 更新图片使用次数
|
|||
|
|
used_image_counts[img['id']] += 1
|
|||
|
|
|
|||
|
|
# 记录匹配结果
|
|||
|
|
match_results.append({
|
|||
|
|
'文章ID': article['id'],
|
|||
|
|
'文章标题': article['title'],
|
|||
|
|
'文章内容': article['content'][:100] + '...' if len(article['content']) > 100 else article['content'], # 限制内容长度
|
|||
|
|
'标签': ', '.join(article['tags']),
|
|||
|
|
'匹配的图片URL': image_url,
|
|||
|
|
'图片ID': img['id'],
|
|||
|
|
'图片名称': img['image_name'],
|
|||
|
|
'图片标签': img['tag_name'],
|
|||
|
|
'图片关键词': img['keywords_name'],
|
|||
|
|
'图片部门': img['department_name'],
|
|||
|
|
'匹配状态': '成功'
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
return True
|
|||
|
|
|
|||
|
|
if not matched:
|
|||
|
|
print(f"文章未能与任何图片成功匹配，使用Gemini生成图片: {article['title']}")
|
|||
|
|
|
|||
|
|
# 使用文章标题和标签生成提示词
|
|||
|
|
prompt = f"与'{article['title']}'相关的插图，标签: {', '.join(article['tags'])}"
|
|||
|
|
generated_image_url = generate_image_with_gemini(prompt, article['tags'], article['id'])
|
|||
|
|
print(f"生成的图片URL: {generated_image_url}")
|
|||
|
|
|
|||
|
|
# 记录生成图片的结果
|
|||
|
|
match_results.append({
|
|||
|
|
'文章ID': article['id'],
|
|||
|
|
'文章标题': article['title'],
|
|||
|
|
'文章内容': article['content'][:100] + '...' if len(article['content']) > 100 else article['content'],
|
|||
|
|
'标签': ', '.join(article['tags']),
|
|||
|
|
'匹配的图片URL': generated_image_url,
|
|||
|
|
'图片ID': 'N/A',
|
|||
|
|
'图片名称': 'Generated',
|
|||
|
|
'图片标签': 'N/A',
|
|||
|
|
'图片关键词': 'N/A',
|
|||
|
|
'图片部门': 'N/A',
|
|||
|
|
'匹配状态': '生成图片'
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
return True
|
|||
|
|
else:
|
|||
|
|
print(f"没有找到符合条件的匹配图片，使用Gemini生成图片: {article['title']}")
|
|||
|
|
|
|||
|
|
# 使用文章标题和标签生成提示词
|
|||
|
|
prompt = f"与'{article['title']}'相关的插图，标签: {', '.join(article['tags'])}"
|
|||
|
|
generated_image_url = generate_image_with_gemini(prompt, article['tags'], article['id'])
|
|||
|
|
print(f"生成的图片URL: {generated_image_url}")
|
|||
|
|
|
|||
|
|
# 记录生成图片的结果
|
|||
|
|
match_results.append({
|
|||
|
|
'文章ID': article['id'],
|
|||
|
|
'文章标题': article['title'],
|
|||
|
|
'文章内容': article['content'][:100] + '...' if len(article['content']) > 100 else article['content'],
|
|||
|
|
'标签': ', '.join(article['tags']),
|
|||
|
|
'匹配的图片URL': generated_image_url,
|
|||
|
|
'图片ID': 'N/A',
|
|||
|
|
'图片名称': 'Generated',
|
|||
|
|
'图片标签': 'N/A',
|
|||
|
|
'图片关键词': 'N/A',
|
|||
|
|
'图片部门': 'N/A',
|
|||
|
|
'匹配状态': '生成图片'
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
return True
|
|||
|
|
|
|||
|
|
|
|||
|
|
def process_article_image_matching(test_mode=False, test_count=None):
|
|||
|
|
"""
|
|||
|
|
处理文章与图片的匹配和挂靠
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
test_mode: 是否为测试模式
|
|||
|
|
test_count: 测试文章数量（仅在测试模式下使用）
|
|||
|
|
"""
|
|||
|
|
# 用于跟踪每张图片的使用次数
|
|||
|
|
used_image_counts = defaultdict(int)
|
|||
|
|
# 存储匹配结果
|
|||
|
|
match_results = []
|
|||
|
|
|
|||
|
|
try:
|
|||
|
|
# 根据模式决定获取哪些文章
|
|||
|
|
articles = get_articles_with_tags_from_db()
|
|||
|
|
|
|||
|
|
if not articles:
|
|||
|
|
print("没有找到文章")
|
|||
|
|
return
|
|||
|
|
|
|||
|
|
# 如果是测试模式，只取前test_count条数据
|
|||
|
|
if test_mode:
|
|||
|
|
if test_count is None:
|
|||
|
|
test_count = 3 # 默认测试前3条
|
|||
|
|
articles = articles[:test_count]
|
|||
|
|
print(f"测试模式：处理前 {len(articles)} 篇文章")
|
|||
|
|
|
|||
|
|
success_count = 0
|
|||
|
|
generated_count = 0
|
|||
|
|
|
|||
|
|
# 收集所有处理后的文章ID用于发布
|
|||
|
|
processed_article_ids = []
|
|||
|
|
|
|||
|
|
for article in articles:
|
|||
|
|
if process_single_article(article, used_image_counts, match_results):
|
|||
|
|
success_count += 1
|
|||
|
|
processed_article_ids.append(article['id'])
|
|||
|
|
else:
|
|||
|
|
print(f"处理文章 {article['id']} 失败")
|
|||
|
|
|
|||
|
|
# 将匹配结果写入CSV文件
|
|||
|
|
output_csv = 'article_image_match_results.csv'
|
|||
|
|
with open(output_csv, 'w', newline='', encoding='utf-8-sig') as csvfile:
|
|||
|
|
fieldnames = [
|
|||
|
|
'文章ID', '文章标题', '文章内容', '标签',
|
|||
|
|
'匹配的图片URL', '图片ID', '图片名称',
|
|||
|
|
'图片标签', '图片关键词', '图片部门', '匹配状态'
|
|||
|
|
]
|
|||
|
|
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
|
|||
|
|
|
|||
|
|
writer.writeheader()
|
|||
|
|
for result in match_results:
|
|||
|
|
writer.writerow(result)
|
|||
|
|
|
|||
|
|
if not test_mode:
|
|||
|
|
print(f"\n处理完成! 成功挂靠: {success_count} 篇, 生成图片: {generated_count} 篇")
|
|||
|
|
print(f"匹配结果已保存至: {output_csv}")
|
|||
|
|
|
|||
|
|
# 如果有处理过的文章，将它们提交到发布接口
|
|||
|
|
if processed_article_ids:
|
|||
|
|
print(f"\n开始发布处理过的 {len(processed_article_ids)} 篇文章...")
|
|||
|
|
|
|||
|
|
# 登录获取JWT token
|
|||
|
|
base_url = "http://47.99.184.230:8324" # 使用外网API地址
|
|||
|
|
jwt_token = login_and_get_jwt_token(base_url)
|
|||
|
|
|
|||
|
|
if jwt_token:
|
|||
|
|
# 批量发布文章
|
|||
|
|
if batch_publish_articles(base_url, jwt_token, processed_article_ids):
|
|||
|
|
print(f"成功发布 {len(processed_article_ids)} 篇文章")
|
|||
|
|
else:
|
|||
|
|
print("批量发布失败")
|
|||
|
|
else:
|
|||
|
|
print("获取JWT token失败，无法发布文章")
|
|||
|
|
else:
|
|||
|
|
print("\n没有处理过的文章，跳过发布步骤")
|
|||
|
|
else:
|
|||
|
|
print(f"\n测试模式完成! 处理了 {len(articles)} 篇文章，成功挂靠: {success_count} 篇, 生成图片: {len([r for r in match_results if r['匹配状态'] == '生成图片'])} 篇")
|
|||
|
|
print(f"处理结果已保存至: {output_csv}")
|
|||
|
|
|
|||
|
|
except Exception as e:
|
|||
|
|
print(f"处理文章图片匹配时发生错误: {e}")
|
|||
|
|
raise
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__ == "__main__":
|
|||
|
|
import sys
|
|||
|
|
|
|||
|
|
print("开始处理文章与图片的智能挂靠...")
|
|||
|
|
|
|||
|
|
# 检查命令行参数
|
|||
|
|
if len(sys.argv) > 1:
|
|||
|
|
if sys.argv[1] == "--test" and len(sys.argv) > 2:
|
|||
|
|
# 测试模式：处理前N篇文章
|
|||
|
|
test_count = int(sys.argv[2])
|
|||
|
|
print(f"启动测试模式，处理前 {test_count} 篇文章")
|
|||
|
|
process_article_image_matching(test_mode=True, test_count=test_count)
|
|||
|
|
elif sys.argv[1] == "--test" and len(sys.argv) == 2:
|
|||
|
|
# 提示用户输入要测试的文章数量
|
|||
|
|
test_count_input = input("请输入要测试的文章数量 (默认3): ")
|
|||
|
|
test_count = int(test_count_input) if test_count_input.strip().isdigit() else 3
|
|||
|
|
print(f"启动测试模式，处理前 {test_count} 篇文章")
|
|||
|
|
process_article_image_matching(test_mode=True, test_count=test_count)
|
|||
|
|
else:
|
|||
|
|
print("使用方法:")
|
|||
|
|
print(" 正常模式: python match_article_images.py")
|
|||
|
|
print(" 测试模式: python match_article_images.py --test [文章ID]")
|
|||
|
|
else:
|
|||
|
|
# 正常模式：处理所有文章
|
|||
|
|
process_article_image_matching()
|