#!/usr/bin/env python3
"""更新专家支持中心的对话数据为视觉设计问答内容"""

import json
import os
from datetime import datetime

def load_visual_qa_data():
    """加载视觉设计问答内容"""
    file_path = '网页未导入数据/视觉设计产业/视觉设计问答内容.json'
    with open(file_path, 'r', encoding='utf-8') as f:
        return json.load(f)

def get_mentor_avatar(mentor_name):
    """根据导师名称获取头像URL"""
    mentor_avatars = {
        "光年": "https://ddcz-1315997005.cos.ap-nanjing.myqcloud.com/static/img/teach_sys_teacher-avatar/recuW7dxJ5AdfT.png",
        "唐振华": "https://ddcz-1315997005.cos.ap-nanjing.myqcloud.com/static/img/teach_sys_teacher-avatar/recuW7dxJ5EEac.png",
        "张宏达": "https://ddcz-1315997005.cos.ap-nanjing.myqcloud.com/static/img/teach_sys_teacher-avatar/recuW7dxJ5bHYT.png",
        "李奇": "https://ddcz-1315997005.cos.ap-nanjing.myqcloud.com/static/img/teach_sys_teacher-avatar/recuUpSO4gUtJz.png",
        "多多畅职机器人": "https://ddcz-1315997005.cos.ap-nanjing.myqcloud.com/static/img/teach_sys_icon/recuWmDuekBTlr.png"
    }
    return mentor_avatars.get(mentor_name, mentor_avatars["多多畅职机器人"])

def transform_qa_to_conversations(qa_data):
    """转换问答数据为对话列表格式"""
    conversations = []

    for idx, qa in enumerate(qa_data):
        # 构建消息列表
        messages = []

        # 处理最多3轮问答
        for i in range(1, 6, 2):  # 1,3,5
            question_key = f"问题_流程{i}"
            question_time_key = f"流程{i}_时间"
            answer_key = f"回答_流程{i+1}"
            answer_time_key = f"流程{i+1}_时间"

            if qa.get(question_key) and qa.get(question_key).strip():
                # 添加用户问题
                messages.append({
                    "type": "user",
                    "content": qa[question_key],
                    "time": qa.get(question_time_key, "2024/12/1 10:00")
                })

                # 添加导师/机器人回答
                if qa.get(answer_key) and qa.get(answer_key).strip():
                    mentor_name = qa.get("查询导师名称", "").strip()
                    if qa["问答类型"] == "智能客服" or not mentor_name:
                        mentor_name = "多多畅职机器人"

                    messages.append({
                        "type": "assistant",
                        "content": qa[answer_key],
                        "mentor": mentor_name,
                        "time": qa.get(answer_time_key, "2024/12/1 10:01"),
                        "mentorAvatar": get_mentor_avatar(mentor_name)
                    })

        # 只有有消息的对话才添加
        if messages:
            # 提取日期部分作为显示日期
            first_time = messages[0].get("time", "2024/12/1 10:00")
            date_parts = first_time.split(' ')[0].split('/')
            display_date = f"{date_parts[0]}年{int(date_parts[1])}月"

            conversations.append({
                "id": idx + 1,
                "title": qa.get("问题标题", "视觉设计咨询"),
                "status": "finish",
                "date": display_date,
                "type": qa.get("问题类型", "常规问题"),
                "messages": messages
            })

    return conversations

def update_expert_support_file():
    """更新expertSupportData.js文件"""
    # 加载视觉设计问答数据
    qa_data = load_visual_qa_data()

    # 转换为对话格式
    conversations = transform_qa_to_conversations(qa_data)

    # 构建完整的数据结构
    expert_support_data = {
        "conversations": conversations
    }

    # 生成JavaScript内容
    js_content = f"""// 专家支持中心数据 - 视觉设计产业
const expertSupportData = {json.dumps(expert_support_data, ensure_ascii=False, indent=2)};

export default expertSupportData;"""

    # 备份原文件
    backup_time = datetime.now().strftime('%Y%m%d_%H%M%S')
    os.system(f'cp src/data/expertSupportData.js src/data/expertSupportData.js.backup_{backup_time}')

    # 写入新文件
    with open('src/data/expertSupportData.js', 'w', encoding='utf-8') as f:
        f.write(js_content)

    print(f"✅ 已更新 expertSupportData.js")
    print(f"   - 对话数量: {len(conversations)}")
    print(f"   - 备份文件: expertSupportData.js.backup_{backup_time}")

if __name__ == "__main__":
    print("🚀 开始更新专家支持中心数据...")
    update_expert_support_file()
    print("🎉 更新完成！")