a7242f0c69
- 包含4个产业方向的前端项目：智能开发、智能制造、大健康、财经商贸 - 已清理node_modules、.yoyo等大文件，项目大小从2.6GB优化至631MB - 配置完善的.gitignore文件 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
122 lines
3.9 KiB
Python
122 lines
3.9 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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将智能制造问答内容转换为专家支持中心数据格式
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"""
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import json
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from datetime import datetime
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# 导师头像映射
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mentor_avatars = {
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"顾华": "https://ddcz-1315997005.cos.ap-nanjing.myqcloud.com/static/img/teach_sys_teacher-avatar/recuW7cCvScko4.png",
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"王军": "https://ddcz-1315997005.cos.ap-nanjing.myqcloud.com/static/img/teach_sys_teacher-avatar/recuW7c44HkjqO.png",
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"杨文琦": "https://ddcz-1315997005.cos.ap-nanjing.myqcloud.com/static/img/teach_sys_teacher-avatar/recuW7dxJ5Av9E.png",
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"李奇": "https://ddcz-1315997005.cos.ap-nanjing.myqcloud.com/static/img/teach_sys_teacher-avatar/recuW7dDIg60Tg.png"
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}
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# 读取原始数据
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with open('网页未导入数据/智能制造产业/智能制造问答内容.json', 'r', encoding='utf-8') as f:
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data = json.load(f)
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conversations = []
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for idx, item in enumerate(data, 1):
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# 解析日期获取月份
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first_time = item.get('流程1_时间', '')
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if first_time:
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try:
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date_obj = datetime.strptime(first_time.split(' ')[0], '%Y/%m/%d')
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date_str = f"{date_obj.year}年{date_obj.month}月"
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except:
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date_str = "2024年10月" # 默认日期
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else:
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date_str = "2024年10月"
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# 构建对话消息
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messages = []
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# 流程1和2
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if item.get('问题_流程1'):
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messages.append({
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"type": "user",
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"content": item['问题_流程1'],
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"time": item.get('流程1_时间', '')
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})
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if item.get('回答_流程2'):
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mentor_name = item.get('查询导师名称', '')
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if item['问答类型'] == '智能客服':
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mentor = "多多畅职机器人"
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avatar = "https://ddcz-1315997005.cos.ap-nanjing.myqcloud.com/static/img/teach_sys_icon/recuWmDuekBTlr.png"
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else:
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mentor = f"{mentor_name}老师" if mentor_name else "顾华老师"
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avatar = mentor_avatars.get(mentor_name, mentor_avatars['顾华'])
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messages.append({
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"type": "assistant",
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"content": item['回答_流程2'],
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"mentor": mentor,
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"time": item.get('流程2_时间', ''),
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"mentorAvatar": avatar
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})
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# 流程3和4
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if item.get('问题_流程3'):
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messages.append({
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"type": "user",
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"content": item['问题_流程3'],
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"time": item.get('流程3_时间', '')
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})
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if item.get('回答_流程4'):
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messages.append({
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"type": "assistant",
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"content": item['回答_流程4'],
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"mentor": mentor,
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"time": item.get('流程4_时间', ''),
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"mentorAvatar": avatar
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})
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# 流程5和6
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if item.get('问题_流程5'):
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messages.append({
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"type": "user",
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"content": item['问题_流程5'],
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"time": item.get('流程5_时间', '')
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})
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if item.get('回答_流程6'):
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messages.append({
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"type": "assistant",
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"content": item['回答_流程6'],
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"mentor": mentor,
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"time": item.get('流程6_时间', ''),
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"mentorAvatar": avatar
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})
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# 创建对话对象
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conversation = {
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"id": idx,
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"title": item['问题标题'],
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"status": "finish",
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"date": date_str,
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"type": item['问题类型'],
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"messages": messages
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}
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conversations.append(conversation)
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# 生成最终的JavaScript文件
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js_content = """// 从智能制造问答内容.json转换的专家支持中心数据
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const expertSupportData = {
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"conversations": %s
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};
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export default expertSupportData;
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""" % json.dumps(conversations, ensure_ascii=False, indent=2)
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# 写入文件
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with open('src/data/expertSupportData.js', 'w', encoding='utf-8') as f:
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f.write(js_content)
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print(f"成功转换 {len(conversations)} 个对话") |