114 lines
4.5 KiB
Python
114 lines
4.5 KiB
Python
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#!/usr/bin/env python3
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"""更新专家支持中心的对话数据为视觉设计问答内容"""
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import json
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import os
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from datetime import datetime
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def load_visual_qa_data():
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"""加载视觉设计问答内容"""
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file_path = '网页未导入数据/视觉设计产业/视觉设计问答内容.json'
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with open(file_path, 'r', encoding='utf-8') as f:
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return json.load(f)
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def get_mentor_avatar(mentor_name):
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"""根据导师名称获取头像URL"""
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mentor_avatars = {
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"光年": "https://ddcz-1315997005.cos.ap-nanjing.myqcloud.com/static/img/teach_sys_teacher-avatar/recuW7dxJ5AdfT.png",
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"唐振华": "https://ddcz-1315997005.cos.ap-nanjing.myqcloud.com/static/img/teach_sys_teacher-avatar/recuW7dxJ5EEac.png",
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"张宏达": "https://ddcz-1315997005.cos.ap-nanjing.myqcloud.com/static/img/teach_sys_teacher-avatar/recuW7dxJ5bHYT.png",
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"李奇": "https://ddcz-1315997005.cos.ap-nanjing.myqcloud.com/static/img/teach_sys_teacher-avatar/recuUpSO4gUtJz.png",
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"多多畅职机器人": "https://ddcz-1315997005.cos.ap-nanjing.myqcloud.com/static/img/teach_sys_icon/recuWmDuekBTlr.png"
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}
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return mentor_avatars.get(mentor_name, mentor_avatars["多多畅职机器人"])
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def transform_qa_to_conversations(qa_data):
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"""转换问答数据为对话列表格式"""
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conversations = []
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for idx, qa in enumerate(qa_data):
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# 构建消息列表
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messages = []
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# 处理最多3轮问答
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for i in range(1, 6, 2): # 1,3,5
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question_key = f"问题_流程{i}"
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question_time_key = f"流程{i}_时间"
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answer_key = f"回答_流程{i+1}"
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answer_time_key = f"流程{i+1}_时间"
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if qa.get(question_key) and qa.get(question_key).strip():
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# 添加用户问题
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messages.append({
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"type": "user",
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"content": qa[question_key],
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"time": qa.get(question_time_key, "2024/12/1 10:00")
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})
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# 添加导师/机器人回答
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if qa.get(answer_key) and qa.get(answer_key).strip():
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mentor_name = qa.get("查询导师名称", "").strip()
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if qa["问答类型"] == "智能客服" or not mentor_name:
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mentor_name = "多多畅职机器人"
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messages.append({
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"type": "assistant",
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"content": qa[answer_key],
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"mentor": mentor_name,
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"time": qa.get(answer_time_key, "2024/12/1 10:01"),
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"mentorAvatar": get_mentor_avatar(mentor_name)
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})
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# 只有有消息的对话才添加
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if messages:
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# 提取日期部分作为显示日期
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first_time = messages[0].get("time", "2024/12/1 10:00")
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date_parts = first_time.split(' ')[0].split('/')
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display_date = f"{date_parts[0]}年{int(date_parts[1])}月"
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conversations.append({
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"id": idx + 1,
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"title": qa.get("问题标题", "视觉设计咨询"),
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"status": "finish",
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"date": display_date,
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"type": qa.get("问题类型", "常规问题"),
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"messages": messages
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})
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return conversations
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def update_expert_support_file():
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"""更新expertSupportData.js文件"""
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# 加载视觉设计问答数据
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qa_data = load_visual_qa_data()
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# 转换为对话格式
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conversations = transform_qa_to_conversations(qa_data)
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# 构建完整的数据结构
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expert_support_data = {
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"conversations": conversations
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}
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# 生成JavaScript内容
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js_content = f"""// 专家支持中心数据 - 视觉设计产业
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const expertSupportData = {json.dumps(expert_support_data, ensure_ascii=False, indent=2)};
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export default expertSupportData;"""
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# 备份原文件
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backup_time = datetime.now().strftime('%Y%m%d_%H%M%S')
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os.system(f'cp src/data/expertSupportData.js src/data/expertSupportData.js.backup_{backup_time}')
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# 写入新文件
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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"✅ 已更新 expertSupportData.js")
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print(f" - 对话数量: {len(conversations)}")
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print(f" - 备份文件: expertSupportData.js.backup_{backup_time}")
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if __name__ == "__main__":
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print("🚀 开始更新专家支持中心数据...")
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update_expert_support_file()
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print("🎉 更新完成！")
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