AiOpsWechatiPad/resources/messages_resource.py

from flask_restful import Resource, reqparse
from flask import jsonify,request
import requests,json,re
from wechat import gewe_chat
from voice.ali.ali_voice import AliVoice
from common import utils,redis_helper,memory,kafka_helper
from common.log import logger
import xml.etree.ElementTree as ET
import threading,time


import os

from voice import audio_convert


timeout_duration = 4.0 

class MessagesResource(Resource):
    def __init__(self):
        self.parser = reqparse.RequestParser()


    def post(self):
        msg = request.get_json()
        logger.info(f"收到微信回调消息: {msg}")
        type_name =msg.get("TypeName")
        app_id = msg.get("Appid")
        # token_id = "f828cb3c-1039-489f-b9ae-7494d1778a15"
        token_id=get_token_id_by_app_id(app_id)

        if token_id=="":
            logger.warning('找不到登录信息，不处理')
            return jsonify({"message": "收到微信回调消息"})
        
        wx_config = gewe_chat.wxchat.get_wxchat_config_from_cache(wxid)
        if not bool(wx_config.get("agentEnabled",False)):
            logger.info('智能体未启用，不处理')
            return jsonify({"message": "收到微信回调消息"})
        
        if type_name=='AddMsg': 
            wxid = msg.get("Wxid")
            msg_data = msg.get("Data")
            msg_type = msg_data.get("MsgType",None)
            from_wxid = msg_data["FromUserName"]["string"]
            to_wxid = msg_data["ToUserName"]["string"]
            msg_push_content=msg_data.get("PushContent") #群发

            #wx_config = gewe_chat.wxchat.get_wxchat_config_from_cache(wxid)
            
                
            handlers = {
                    1:  handle_text,
                    3:  handle_image,
                    34: handle_voice,
                    49: handle_xml, 
                    37: handle_add_friend_notice,
                    10002: handle_10002_msg
                }
            # (扫码进群情况)判断受否是群聊,并添加到通信录
            if check_chatroom(from_wxid) or check_chatroom(to_wxid):
                logger.info('群信息')
                chatroom_id=from_wxid
                ret,msg,data=gewe_chat.wxchat.save_contract_list(token_id,app_id,chatroom_id,3)
                logger.info(f'保存到通讯录 chatroom_id {chatroom_id}  {msg}')
                gewe_chat.wxchat.update_groups_info_to_cache(token_id,app_id,wxid,chatroom_id)
                handlers[1]=handle_text_group
                handlers[3]=handle_image_group
                handlers[34]=handle_voice_group

            handler = handlers.get(msg_type)
            if handler:
                return handler(token_id,app_id, wxid,msg_data,from_wxid, to_wxid)
            else:
                logger.warning(f"微信回调消息类型 {msg_type} 未处理")
        elif type_name=='ModContacts':
            '''
            好友通过验证及好友资料变更的通知消息
            '''
            wxid = msg.get("Wxid")
            msg_data = msg.get("Data")
            handle_mod_contacts(token_id,app_id,wxid,msg_data)
        elif type_name=="DelContacts":
            '''
            删除好友通知/退出群聊
            '''
            msg_data = msg.get("Data")
            username=msg_data["UserName"]["string"]
            if check_chatroom(username):
                logger.info('退出群聊')
                wxid = msg.get("Wxid")
                chatroom_id=username
                redis_helper.redis_helper.delete_hash_field(f'__AI_OPS_WX__:GROUPS_INFO:{wxid}',chatroom_id)
                logger.info(f'清除 chatroom_id{chatroom_id} 数据')
            else:
                logger.info('删除好友通知')
        elif type_name=="Offline":
            '''
            已经离线
            '''
            wxid = msg.get("Wxid")
            logger.warning(f'微信ID {wxid}在设备{app_id}已经离线')
            k,r=get_login_info_by_app_id(app_id)
            print(k)
            redis_helper.redis_helper.update_hash_field(k,'status',0)
            redis_helper.redis_helper.update_hash_field(k,'modify_at',int(time.time())) 
        else:
            logger.warning(f"未知消息类型")

        return jsonify({"message": "收到微信回调消息"})
    

def handle_text(token_id,app_id, wxid,msg_data,from_wxid, to_wxid):
    '''
    私聊文本消息
    '''
    msg_content=msg_data["Content"]["string"]
    if wxid == from_wxid:  #手动发送消息
        logger.info("Active message sending detected")
        
        gewe_chat.wxchat.save_contacts_brief_to_cache(token_id,app_id,wxid,[to_wxid])
        callback_to_user=msg_data["ToUserName"]["string"]

        input_wx_content_dialogue_message=[{"type": "text", "text": msg_content}]
        input_message=utils.dialogue_message(from_wxid,to_wxid,input_wx_content_dialogue_message)
        kafka_helper.kafka_client.produce_message(input_message)
        logger.info("发送对话 %s",input_message)
    else:
        callback_to_user=msg_data["FromUserName"]["string"]
        # hash_key = f'__AI_OPS_WX__:MESSAGES:{wxid}:{callback_to_user}'

        # prompt={"role": "user", "content": [{
        #     "type": "text",
        #     "text": msg_content
        # }]}
        # messages_to_send=gewe_chat.wxchat.save_session_messages_to_cache(hash_key, prompt)
        # # 收到的对话
        # input_wx_content_dialogue_message=[{"type": "text", "text": msg_content}]
        # input_message=utils.dialogue_message(callback_to_user,wxid,input_wx_content_dialogue_message)
        # kafka_helper.kafka_client.produce_message(input_message)
        # logger.info("发送对话 %s",input_message)
        
        # cache_data = memory.USER_INTERACTIVE_CACHE.get(wxid)
        # if cache_data and cache_data.get('interactive') :
        #     o=get_first_char_if_digit(msg_content)
        #     if o is not None:
        #         userSelectOptions=cache_data.get('options')  
        #         if o < len(userSelectOptions):
        #             o=o-1
        #             msg_content=userSelectOptions[o].get("value")
        #             messages_to_send=[{"role": "user", "content": msg_content}]
        #         else:    
        #             messages_to_send=[{"role": "user", "content": msg_content}] 
        #     else:    
        #         messages_to_send=[{"role": "user", "content": msg_content}]
        
        # res=fast_gpt_api(messages_to_send,wxid,callback_to_user)
        # reply_content=res["choices"][0]["message"]["content"]
    
        # description = ''
        # userSelectOptions = []

        # if isinstance(reply_content, list) and any(item.get("type") == "interactive" for item in reply_content):
        #     for item in reply_content:
        #         if item["type"] == "interactive" and item["interactive"]["type"] == "userSelect":
        #             params = item["interactive"]["params"]
        #             description = params.get("description")
        #             userSelectOptions = params.get("userSelectOptions", [])
        #     values_string = "\n".join(option["value"] for option in userSelectOptions)
            
        #     if description is not None:
        #         memory.USER_INTERACTIVE_CACHE[wxid] = {
        #         "interactive":True,
        #         "options": userSelectOptions,
        #         }          
        #         reply_content=description + '------------------------------\n'+values_string
            
        # elif isinstance(reply_content, list) and any(item.get("type") == "text" for item in reply_content):
        #     memory.USER_INTERACTIVE_CACHE[wxid] = {
        #         "interactive":False
        #     }
        #     text=''                        
        #     for item in reply_content:
        #         if item["type"] == "text":
        #             text=item["text"]["content"]
        #     if text=='':
        #         # 去除上次上一轮对话再次请求
        #         cache_messages_str=redis_helper.redis_helper.get_hash_field(hash_key,"data")
        #         cache_messages = json.loads(cache_messages_str) if cache_messages_str else []
                
        #         if len(cache_messages) >= 3:
        #             cache_messages = cache_messages[:-3]  

        #         redis_helper.redis_helper.update_hash_field(hash_key,"data",json.dumps(cache_messages,ensure_ascii=False))
        #         messages_to_send=gewe_chat.wxchat.save_session_messages_to_cache(hash_key, prompt)
        #         res=fast_gpt_api(messages_to_send,wxid,callback_to_user)
        #         reply_content=res["choices"][0]["message"]["content"]


        #         if isinstance(reply_content, list) :
        #             reply_content=reply_content[0].get('text').get("content")

        #     else:
        #         reply_content=text
        # else:
        #     memory.USER_INTERACTIVE_CACHE[wxid] = {
        #         "interactive":False
        #     }     
        #     reply_content=res["choices"][0]["message"]["content"]

        # gewe_chat.wxchat.post_text(token_id,app_id,callback_to_user,reply_content)
        # gewe_chat.wxchat.save_session_messages_to_cache(hash_key, {"role": "assistant", "content": reply_content})
        # # 回复的对话
        # input_wx_content_dialogue_message=[{"type": "text", "text": reply_content}]
        # input_message=utils.dialogue_message(wxid,callback_to_user,input_wx_content_dialogue_message,True)
        # kafka_helper.kafka_client.produce_message(input_message)
        # logger.info("发送对话 %s",input_message)
       
        # 创建并启动任务线程，将参数传递给 ai_chat_text 函数
        task_thread = threading.Thread(
            target=ai_chat_text,
            args=(token_id,app_id,wxid,msg_data,msg_content)
        )
        task_thread.start()

        # 设置定时器，1秒后检查任务是否超时。这里需要使用 lambda 来传递参数
        timeout_timer = threading.Timer(
            timeout_duration,
            lambda:check_timeout(task_thread, token_id, wxid,app_id, callback_to_user)
        )
        timeout_timer.start()

        # 等待任务线程完成
        task_thread.join()
        # 取消定时器
        timeout_timer.cancel()

def check_timeout( task_thread:threading.Thread, token_id,wxid, app_id, callback_to_user):
    if task_thread.is_alive():
        print(f"任务运行时间超过{timeout_duration}秒，token_id={token_id}, app_id={app_id}, callback_to_user={callback_to_user}")
        wx_config = gewe_chat.wxchat.get_wxchat_config_from_cache(wxid)
        if bool(wx_config.get("chatWaitingMsgEnabled",True)):
            gewe_chat.wxchat.post_text(token_id,app_id,callback_to_user,"亲，我正在组织回复的信息，请稍等一会")

def ai_chat_text(token_id,app_id,wxid,msg_data,msg_content):
    start_time = time.time()  # 记录任务开始时间

    callback_to_user=msg_data["FromUserName"]["string"]
    hash_key = f'__AI_OPS_WX__:MESSAGES:{wxid}:{callback_to_user}'

    prompt={"role": "user", "content": [{
        "type": "text",
        "text": msg_content
    }]}
    messages_to_send=gewe_chat.wxchat.save_session_messages_to_cache(hash_key, prompt)
    # 收到的对话
    input_wx_content_dialogue_message=[{"type": "text", "text": msg_content}]
    input_message=utils.dialogue_message(callback_to_user,wxid,input_wx_content_dialogue_message)
    kafka_helper.kafka_client.produce_message(input_message)
    logger.info("发送对话 %s",input_message)
    
    cache_data = memory.USER_INTERACTIVE_CACHE.get(wxid)
    if cache_data and cache_data.get('interactive') :
        o=get_first_char_if_digit(msg_content)
        if o is not None:
            userSelectOptions=cache_data.get('options')  
            if o < len(userSelectOptions):
                o=o-1
                msg_content=userSelectOptions[o].get("value")
                messages_to_send=[{"role": "user", "content": msg_content}]
            else:    
                messages_to_send=[{"role": "user", "content": msg_content}] 
        else:    
            messages_to_send=[{"role": "user", "content": msg_content}]
    res=fast_gpt_api(messages_to_send,wxid,callback_to_user)

    reply_content=res["choices"][0]["message"]["content"]

    description = ''
    userSelectOptions = []

    if isinstance(reply_content, list) and any(item.get("type") == "interactive" for item in reply_content):
        for item in reply_content:
            if item["type"] == "interactive" and item["interactive"]["type"] == "userSelect":
                params = item["interactive"]["params"]
                description = params.get("description")
                userSelectOptions = params.get("userSelectOptions", [])
        values_string = "\n".join(option["value"] for option in userSelectOptions)
        
        if description is not None:
            memory.USER_INTERACTIVE_CACHE[wxid] = {
            "interactive":True,
            "options": userSelectOptions,
            }          
            reply_content=description + '------------------------------\n'+values_string
        
    elif isinstance(reply_content, list) and any(item.get("type") == "text" for item in reply_content):
        memory.USER_INTERACTIVE_CACHE[wxid] = {
            "interactive":False
        }
        text=''                        
        for item in reply_content:
            if item["type"] == "text":
                text=item["text"]["content"]
        if text=='':
            # 去除上次上一轮对话再次请求
            cache_messages_str=redis_helper.redis_helper.get_hash_field(hash_key,"data")
            cache_messages = json.loads(cache_messages_str) if cache_messages_str else []
            
            if len(cache_messages) >= 3:
                cache_messages = cache_messages[:-3]  

            redis_helper.redis_helper.update_hash_field(hash_key,"data",json.dumps(cache_messages,ensure_ascii=False))
            messages_to_send=gewe_chat.wxchat.save_session_messages_to_cache(hash_key, prompt)
            res=fast_gpt_api(messages_to_send,wxid,callback_to_user)
            reply_content=res["choices"][0]["message"]["content"]


            if isinstance(reply_content, list) :
                reply_content=reply_content[0].get('text').get("content")

        else:
            reply_content=text
    else:
        memory.USER_INTERACTIVE_CACHE[wxid] = {
            "interactive":False
        }     
        reply_content=res["choices"][0]["message"]["content"]

    gewe_chat.wxchat.post_text(token_id,app_id,callback_to_user,reply_content)
    gewe_chat.wxchat.save_session_messages_to_cache(hash_key, {"role": "assistant", "content": reply_content})
    # 回复的对话
    input_wx_content_dialogue_message=[{"type": "text", "text": reply_content}]
    input_message=utils.dialogue_message(wxid,callback_to_user,input_wx_content_dialogue_message,True)
    kafka_helper.kafka_client.produce_message(input_message)
    logger.info("发送对话 %s",input_message)
    
    end_time = time.time()    # 记录任务结束时间
    execution_time = end_time - start_time  # 计算执行时间
    logger.info(f"AI回答任务完成，耗时 {execution_time:.2f} 秒")


def handle_text_group(token_id,app_id, wxid,msg_data,from_wxid, to_wxid):
    '''
    群聊文本消息
    '''
    msg_content=msg_data["Content"]["string"]
    msg_push_content=msg_data.get("PushContent")
    k,login_info=get_login_info_by_app_id(app_id)
    nickname=login_info.get("nickName")

    if wxid == from_wxid:  #手动发送消息
        logger.info("Active message sending detected")
        
        gewe_chat.wxchat.save_contacts_brief_to_cache(token_id,app_id,wxid,[to_wxid])
        callback_to_user=msg_data["ToUserName"]["string"]

        input_wx_content_dialogue_message=[{"type": "text", "text": msg_content}]
        input_message=utils.dialogue_message(from_wxid,to_wxid,input_wx_content_dialogue_message)
        kafka_helper.kafka_client.produce_message(input_message)
        logger.info("发送对话 %s",input_message)
    else:
        c = gewe_chat.wxchat.get_wxchat_config_from_cache(wxid)
        chatroom_id_white_list = c.get("chatroomIdWhiteList", [])

        if not chatroom_id_white_list:
            logger.info('白名单为空或未定义,不处理')
            return

        if from_wxid not in chatroom_id_white_list:
            logger.info('群ID不在白名单中,不处理')
            return
        
        if '在群聊中@了你' in msg_push_content or '@'+nickname  in msg_push_content:

            callback_to_user=msg_data["FromUserName"]["string"]
            hash_key = f'__AI_OPS_WX__:MESSAGES:{wxid}:{callback_to_user}'

            prompt={"role": "user", "content": [{
                "type": "text",
                "text": msg_content
            }]}
            messages_to_send=gewe_chat.wxchat.save_session_messages_to_cache(hash_key, prompt)
            # 收到的对话
            input_wx_content_dialogue_message=[{"type": "text", "text": msg_content}]
            input_message=utils.dialogue_message(callback_to_user,wxid,input_wx_content_dialogue_message)
            kafka_helper.kafka_client.produce_message(input_message)
            logger.info("发送对话 %s",input_message)
            
            cache_data = memory.USER_INTERACTIVE_CACHE.get(wxid)
            if cache_data and cache_data.get('interactive') :
                o=get_first_char_if_digit(msg_content)
                if o is not None:
                    userSelectOptions=cache_data.get('options')  
                    if o < len(userSelectOptions):
                        o=o-1
                        msg_content=userSelectOptions[o].get("value")
                        messages_to_send=[{"role": "user", "content": msg_content}]
                    else:    
                        messages_to_send=[{"role": "user", "content": msg_content}] 
                else:    
                    messages_to_send=[{"role": "user", "content": msg_content}]
            
            res=fast_gpt_api(messages_to_send,wxid,callback_to_user)
            reply_content=res["choices"][0]["message"]["content"]

            description = ''
            userSelectOptions = []

            if isinstance(reply_content, list) and any(item.get("type") == "interactive" for item in reply_content):
                for item in reply_content:
                    if item["type"] == "interactive" and item["interactive"]["type"] == "userSelect":
                        params = item["interactive"]["params"]
                        description = params.get("description")
                        userSelectOptions = params.get("userSelectOptions", [])
                values_string = "\n".join(option["value"] for option in userSelectOptions)
                
                if description is not None:
                    memory.USER_INTERACTIVE_CACHE[wxid] = {
                    "interactive":True,
                    "options": userSelectOptions,
                    }          
                    reply_content=description + '------------------------------\n'+values_string
                
            elif isinstance(reply_content, list) and any(item.get("type") == "text" for item in reply_content):
                memory.USER_INTERACTIVE_CACHE[wxid] = {
                    "interactive":False
                }
                text=''
                for item in reply_content:
                    if item["type"] == "text":
                        text=item["text"]["content"]
                if text=='':
                    # 去除上次上一轮对话再次请求
                    cache_messages_str=redis_helper.redis_helper.get_hash_field(hash_key,"data")
                    cache_messages = json.loads(cache_messages_str) if cache_messages_str else []
                    
                    if len(cache_messages) >= 3:
                        cache_messages = cache_messages[:-3]  

                    redis_helper.redis_helper.update_hash_field(hash_key,"data",json.dumps(cache_messages,ensure_ascii=False))
                    messages_to_send=gewe_chat.wxchat.save_session_messages_to_cache(hash_key, prompt)
                    res=fast_gpt_api(messages_to_send,wxid,callback_to_user)
                    reply_content=res["choices"][0]["message"]["content"]
                else:
                    reply_content=text
            else:
                memory.USER_INTERACTIVE_CACHE[wxid] = {
                    "interactive":False
                }     
                reply_content=res["choices"][0]["message"]["content"]

        
            reply_content='@'+extract_nickname(msg_push_content) + reply_content

            gewe_chat.wxchat.post_text(token_id,app_id,callback_to_user,reply_content)
            gewe_chat.wxchat.save_session_messages_to_cache(hash_key, {"role": "assistant", "content": reply_content})
            # 回复的对话
            input_wx_content_dialogue_message=[{"type": "text", "text": reply_content}]
            input_message=utils.dialogue_message(wxid,callback_to_user,input_wx_content_dialogue_message,True)
            kafka_helper.kafka_client.produce_message(input_message)
            logger.info("发送对话 %s",input_message)
        else:
            logger.info('群聊公开消息')
            callback_to_user=msg_data["FromUserName"]["string"]
            dialogue_message=[{"type": "text", "text": msg_content}]
            input_message=utils.dialogue_message(callback_to_user,wxid,dialogue_message)
            kafka_helper.kafka_client.produce_message(input_message)
            logger.info("发送对话 %s",input_message)
            return

def handle_image(token_id,app_id, wxid,msg_data,from_wxid, to_wxid):
        '''
        图片消息
        '''
        msg_content=msg_data["Content"]["string"]

        callback_to_user=from_wxid
        hash_key = f'__AI_OPS_WX__:MESSAGES:{wxid}:{callback_to_user}'
        wx_img_url=gewe_chat.wxchat.download_image_msg(token_id,app_id,msg_content)

        oss_access_key_id="LTAI5tRTG6pLhTpKACJYoPR5"
        oss_access_key_secret="E7dMzeeMxq4VQvLg7Tq7uKf3XWpYfN"
        oss_endpoint="http://oss-cn-shanghai.aliyuncs.com"
        oss_bucket_name="cow-agent"
        oss_prefix="cow"

        img_url=utils.upload_oss(oss_access_key_id, oss_access_key_secret, oss_endpoint, oss_bucket_name, wx_img_url, oss_prefix)

        prompt={
            "role": "user", 
            "content": [{
                "type": "image_url",
                "image_url": {"url": img_url}
                }]
        }

        gewe_chat.wxchat.save_session_messages_to_cache(hash_key, prompt)
        gewe_chat.wxchat.post_text(token_id,app_id,callback_to_user,'已经上传了图片，有什么可以为您服务')
        logger.info(f"上传图片 URL: {img_url}")

        wx_content_dialogue_message=[{"type": "image_url", "image_url": {"url": img_url}}]
        input_message=utils.dialogue_message(wxid,callback_to_user,wx_content_dialogue_message)
        kafka_helper.kafka_client.produce_message(input_message)
        logger.info("发送对话 %s",input_message)

def handle_image_group(token_id,app_id, wxid,msg_data,from_wxid, to_wxid):
    logger.info('群聊图片消息')

def handle_voice(token_id,app_id, wxid,msg_data,from_wxid, to_wxid):
    '''
    语音消息
    '''
    callback_to_user=from_wxid
    msg_content=msg_data["Content"]["string"]
    msg_id=msg_data["MsgId"]


    file_url=gewe_chat.wxchat.download_audio_msg(token_id,app_id,msg_id,msg_content)
    react_silk_path=utils.save_to_local_from_url(file_url)
    react_wav_path = os.path.splitext(react_silk_path)[0] + ".wav"
    audio_convert.any_to_wav(react_silk_path,react_wav_path)
    react_voice_text=AliVoice().voiceToText(react_wav_path)

    os.remove(react_silk_path)
    os.remove(react_wav_path)

    hash_key = f'__AI_OPS_WX__:MESSAGES:{wxid}:{callback_to_user}'
    messages=gewe_chat.wxchat.save_session_messages_to_cache(hash_key, {"role": "user", "content": react_voice_text})
    ai_res=fast_gpt_api(messages,wxid,callback_to_user)
    ai_res_content=ai_res["choices"][0]["message"]["content"]
    has_url=contains_url(ai_res_content)
    if not has_url:    
        voice_during,voice_url=utils.wx_voice(ai_res_content)
        
        if voice_during < 60 * 1000:
            ret,ret_msg,res=gewe_chat.wxchat.post_voice(token_id,app_id,callback_to_user,voice_url,voice_during)
            
        else:
            ret,ret_msg,res=gewe_chat.wxchat.post_text(token_id,app_id,callback_to_user,ai_res_content)    
            logger.warning(f'回应语音消息长度 {voice_during/1000}秒，超过60秒，转为文本回复')
        if ret==200:
            logger.info((f'{wxid} 向 {callback_to_user} 发送语音文本【{ai_res_content}】{ret_msg}'))

        else:
            logger.warning((f'{wxid} 向 {callback_to_user} 发送语音文本【{ai_res_content}】{ret_msg}'))
            ret,ret_msg,res=gewe_chat.wxchat.post_text(token_id,app_id,callback_to_user,ai_res_content)
            logger.info((f'{wxid} 向 {callback_to_user} 发送文本【{ai_res_content}】{ret_msg}'))
            
    else:
        logger.info(f"回复内容包含网址，不发送语音,回复文字内容：{ai_res_content}")
        ret,ret_msg,res=gewe_chat.wxchat.post_text(token_id,app_id,callback_to_user,ai_res_content)
        


   
    gewe_chat.wxchat.save_session_messages_to_cache(hash_key, {"role": "assistant", "content": ai_res_content})
    # 构造对话消息并发送到 Kafka
    input_wx_content_dialogue_message = [{"type": "text", "text": ai_res_content}]
    input_message = utils.dialogue_message(wxid, callback_to_user, input_wx_content_dialogue_message,True)
    kafka_helper.kafka_client.produce_message(input_message)
    logger.info("发送对话 %s", input_message)

def handle_voice_group(token_id,app_id, wxid,msg_data,from_wxid, to_wxid):
    logger.info('语音消息')

def handle_xml(token_id,app_id, wxid,msg_data,from_wxid, to_wxid):
    '''
    处理xml
    '''
    msg_content_xml=msg_data["Content"]["string"]
    root = ET.fromstring(msg_content_xml)
    type_value = root.find(".//appmsg/type").text
    handlers = {
            57: handle_xml_reference,
        }
    handler = handlers.get(type_value)
    if handler:
        return handler(token_id,app_id, wxid,msg_data,from_wxid, to_wxid)
    elif "邀请你加入了群聊" in msg_content_xml:  # 邀请加入群聊
        logger.warning(f"xml消息 {type_value} 邀请你加入了群聊.todo")
    else:
        print(f"xml消息 {type_value} 未解析")
    
def handle_xml_reference(token_id,app_id, wxid,msg_data,from_wxid, to_wxid):
        '''
        判断此类消息的逻辑：$.Data.MsgType=49 并且 解析$.Data.Content.string中的xml msg.appmsg.type=57
        '''       
        callback_to_user=from_wxid
        hash_key = f'__AI_OPS_WX__:MESSAGES:{wxid}:{callback_to_user}'

        msg_content= msg_data["PushContent"]   

        prompt={"role": "user", "content": [{
            "type": "text",
            "text": msg_content
        }]}
        
        # 收到的对话
        messages_to_send=gewe_chat.wxchat.save_session_messages_to_cache(hash_key, prompt)
        input_wx_content_dialogue_message=[{"type": "text", "text": msg_content}]
        input_message=utils.dialogue_message(callback_to_user,wxid,input_wx_content_dialogue_message)
        kafka_helper.kafka_client.produce_message(input_message)
        logger.info("发送对话 %s",input_message)

        # 回复的对话
        res=fast_gpt_api(messages_to_send,wxid,callback_to_user)
        reply_content=res["choices"][0]["message"]["content"]                   
        input_wx_content_dialogue_message=[{"type": "text", "text": reply_content}]
        input_message=utils.dialogue_message(wxid,callback_to_user,input_wx_content_dialogue_message,True)
        kafka_helper.kafka_client.produce_message(input_message)
        logger.info("发送对话 %s",input_message)
        gewe_chat.wxchat.save_session_messages_to_cache(hash_key, {"role": "assistant", "content": reply_content})
        gewe_chat.wxchat.post_text(token_id,app_id,callback_to_user,reply_content)

def handle_add_friend_notice(token_id,app_id, wxid,msg_data,from_wxid, to_wxid):
    '''
    好友添加请求通知
    '''
    logger.info('好友添加请求通知')

    msg_content_xml=msg_data["Content"]["string"]
    
    root = ET.fromstring(msg_content_xml)

    msg_content = root.attrib.get('content', None)
    v3= root.attrib.get('encryptusername', None)
    v4= root.attrib.get('ticket', None)
    scene=root.attrib.get('scene', None)

    
    to_contact_wxid=root.attrib.get('fromusername', None)
    wxid=msg_data["ToUserName"]["string"]

    # 自动同意好友
    # print(v3)
    # print(v4)
    # print(scene)
    # print(msg_content)
    # 操作类型，2添加好友 3同意好友 4拒绝好友
    #option=2
    option=3
    reply_add_contact_contact="亲，我是你的美丽顾问"
    ret,ret_msg=gewe_chat.wxchat.add_contacts(token_id,app_id,scene,option,v3,v4,reply_add_contact_contact)
    if ret==200:
        logger.info('自动添加好友成功')

        # 好友发送的文字
        hash_key = f'__AI_OPS_WX__:MESSAGES:{wxid}:{to_contact_wxid}'
        prompt={"role": "user", "content": [{"type": "text","text": msg_content}]}
        messages_to_send=gewe_chat.wxchat.save_session_messages_to_cache(hash_key, prompt)

        input_wx_content_dialogue_message=[{"type": "text", "text": msg_content}]
        input_message=utils.dialogue_message(to_contact_wxid,wxid,input_wx_content_dialogue_message)
        kafka_helper.kafka_client.produce_message(input_message)
        logger.info("发送对话 %s",input_message)

        callback_to_user=to_contact_wxid
        res=fast_gpt_api(messages_to_send,wxid,callback_to_user)
        reply_content=res["choices"][0]["message"]["content"]

        #保存好友信息
        gewe_chat.wxchat.save_contacts_brief_to_cache(token_id,app_id, wxid,[to_contact_wxid])

        # 保存到缓存
        gewe_chat.wxchat.save_session_messages_to_cache(hash_key, {"role": "assistant", "content": reply_content})

        # 发送信息
        gewe_chat.wxchat.post_text(token_id,app_id, to_contact_wxid,reply_content)
    
        # 发送到kafka
        input_wx_content_dialogue_message=[{"type": "text", "text": reply_content}]
        input_message=utils.dialogue_message(wxid,to_contact_wxid,input_wx_content_dialogue_message,True)
        kafka_helper.kafka_client.produce_message(input_message)
        logger.info("发送对话 %s",input_message)

    else:
        logger.warning("添加好友失败")

def handle_10002_msg(token_id,app_id, wxid,msg_data,from_wxid, to_wxid):
    '''
    群聊邀请
    撤回消息
    拍一拍消息
    地理位置
    踢出群聊通知
    解散群聊通知
    发布群公告
    '''
    msg_content_xml=msg_data["Content"]["string"]
    # 群聊邀请
    if '邀请你加入了群聊' in msg_content_xml and check_chatroom(msg_data["FromUserName"]["string"]):
        chatroom_id=msg_data["FromUserName"]["string"]
        ret,msg,data=gewe_chat.wxchat.save_contract_list(token_id,app_id,chatroom_id,3)
        logger.info(f'群聊邀请,保存到通讯录 chatroom_id {chatroom_id}  {msg}')
        gewe_chat.wxchat.update_groups_info_to_cache(token_id,app_id,wxid,chatroom_id)

    if  '移出了群聊' in msg_content_xml and 'sysmsgtemplate' in msg_content_xml :
        chatroom_id=msg_data["FromUserName"]["string"]
        ret,msg,data=gewe_chat.wxchat.save_contract_list(token_id,app_id,chatroom_id,2)
        logger.info(f'踢出群聊,移除从通讯录 chatroom_id {chatroom_id}  {msg}')
        redis_helper.redis_helper.delete_hash_field(f'__AI_OPS_WX__:GROUPS_INFO:{wxid}',chatroom_id)
        logger.info(f'清除 chatroom_id{chatroom_id} 数据')
       
    if  '已解散该群聊' in msg_content_xml and 'sysmsgtemplate' in msg_content_xml :
        chatroom_id=msg_data["FromUserName"]["string"]
        ret,msg,data=gewe_chat.wxchat.save_contract_list(token_id,app_id,chatroom_id,2)
        logger.info(f'解散群聊,移除从通讯录 chatroom_id {chatroom_id}  {msg}')
        redis_helper.redis_helper.delete_hash_field(f'__AI_OPS_WX__:GROUPS_INFO:{wxid}',chatroom_id)
        logger.info(f'清除 chatroom_id{chatroom_id} 数据')



    print('撤回消息,拍一拍消息,地理位置')

def handle_mod_contacts(token_id,app_id,wxid,msg_data):
    '''
     好友通过验证及好友资料变更的通知消息
    '''
    logger.info('好友通过验证及好友资料变更的通知消息')
    if not check_chatroom(msg_data["UserName"]["string"]):
        contact_wxid = msg_data["UserName"]["string"]

        # 更新好友信息
        ret,msg,contacts_list = gewe_chat.wxchat.fetch_contacts_list(token_id, app_id)
        friend_wxids = contacts_list['friends'][3:]  # 可以调整截取范围
        print(friend_wxids)
        #friend_wxids.remove('weixin')
        gewe_chat.wxchat.save_contacts_brief_to_cache(token_id, app_id, wxid, friend_wxids)

        # keys=redis_helper.redis_helper.client.keys('__AI_OPS_WX__:LOGININFO:*')

        # wxid=""
        # for k in keys:
        #     key=k.decode('utf-8')
        #     hash_key=f"__AI_OPS_WX__:LOGININFO:{key}"
        #     cache_app_id=redis_helper.redis_helper.get_hash_field(hash_key,"appId")
        #     if app_id==cache_app_id:
        #         wxid=redis_helper.redis_helper.get_hash_field(hash_key,"wxid")
        #         break
        # print(wxid)
        # print('~~~~~~~~~~~~~~~~~~~~~~~~~~~\n')
        # if wxid!="":
        #     print('~~~~~~~~~~~~~~~~~~~~~~~~~~~\n')
        #     hash_key=f"__AI_OPS_WX__:CONTACTS_BRIEF:{wxid}"
        #     ret,msg,contacts_list = gewe_chat.wxchat.fetch_contacts_list(token_id, app_id)
        #     friend_wxids = contacts_list['friends'][3:]  # 可以调整截取范围
        #     print(friend_wxids)
        #     #friend_wxids.remove('weixin')
        #     gewe_chat.wxchat.save_contacts_brief_to_cache(token_id, app_id, wxid, friend_wxids)

            # cache_str=redis_helper.redis_helper.get_hash_field(hash_key,"data")
            # cache = json.loads(cache_str) if cache_str else []
            # for c in cache:
            #     if c["userName"]==contact_wxid:
            #         c["nickName"] = msg_data["NickName"]
            #         c["snsBgImg"] = msg_data["SnsBgimgId"]
            #         c["smallHeadImgUrl"] = msg_data["SmallHeadImgUrl"]
            #         c["signature"]= msg_data["Signature"]
                    
            # # 更新缓存，如果有修改过的话
            # if cache:
            #     updated_cache_str = json.dumps(cache)
            #     redis_helper.redis_helper.set_hash_field(hash_key, "data", updated_cache_str)
    else:
        logger.info('群聊好友通过验证及好友资料变更的通知消息') 

def get_messages_from_cache(hash_key,object:object)->list:
    '''
    对话列表
    '''
    messages=redis_helper.redis_helper.get_hash(hash_key)
    wxid=hash_key.split(':')[-1]
    if not messages:
        messages=[{"role": "system", "content": ""}]
        messages.append(object)
        redis_helper.redis_helper.set_hash(hash_key,{"data":json.dumps(messages,ensure_ascii=False)},3600)
    else:
        messages_str=redis_helper.redis_helper.get_hash_field(hash_key,"data")
        messages = json.loads(messages_str) if messages_str else []
        #判断是否含有图片
        last_message = messages[-1]
        content = last_message.get("content", [])
        if isinstance(content, list) and content:
            last_content_type = content[-1].get("type")
            if last_content_type == 'image_url':
               content.append(object['content'][0])
               messages[-1]['content']=content
            else:
               messages.append(object)
        else:
            messages.append(object)
        redis_helper.redis_helper.set_hash(hash_key,{"data":json.dumps(messages,ensure_ascii=False)},3600)
    return messages    

# def fast_gpt_api(messages:list,session_id:str):
#     api_key="sk-jr69ONIehfGKe9JFphuNk4DU5Y5wooHKHhQv7oSnFzVbwCnW65fXO9kvH" #测试
#     #api_key="sk-uJDBdKmJVb2cmfldGOvlIY6Qx0AzqWMPD3lS1IzgQYzHNOXv9SKNI" #开发2
#     api_url = "http://106.15.182.218:3000/api/v1/chat/completions"
#     headers = {

#         "Content-Type": "application/json",
#         "Authorization": f"Bearer {api_key}"
#     }
#     data={
#         "model": "",
#         "messages":messages,
#         "chatId": session_id,
#         "detail": True
#     }
#     #print(json.dumps(data,ensure_ascii=False))
#     logger.info("[CHATGPT] 请求={}".format(json.dumps(data, ensure_ascii=False)))
#     response = requests.post(url=api_url, headers=headers, data=json.dumps(data), timeout=600)
#     response.raise_for_status()
#     response_data = response.json()
#     logger.info("[CHATGPT] 响应={}".format(json.dumps(response_data, separators=(',', ':'),ensure_ascii=False)))
#     print(response_data)
#     return response_data


def fast_gpt_api(messages:list,wixd:str,friend_wxid:str):
    c=gewe_chat.wxchat.get_wxchat_config_from_cache(wixd)
    api_key=c.get('agentTokenId',"sk-jr69ONIehfGKe9JFphuNk4DU5Y5wooHKHhQv7oSnFzVbwCnW65fXO9kvH")
    print(f'流程key：{api_key}\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n')

    #api_key="sk-jr69ONIehfGKe9JFphuNk4DU5Y5wooHKHhQv7oSnFzVbwCnW65fXO9kvH" #测试
    #api_key="sk-uJDBdKmJVb2cmfldGOvlIY6Qx0AzqWMPD3lS1IzgQYzHNOXv9SKNI" #开发2
    api_url = "http://106.15.182.218:3000/api/v1/chat/completions"
    headers = {

        "Content-Type": "application/json",
        "Authorization": f"Bearer {api_key}"
    }
    session_id=f'{wixd}-{friend_wxid}'
    data={
        "model": "",
        "messages":messages,
        "chatId": session_id,
        "detail": True
    }
    #print(json.dumps(data,ensure_ascii=False))
    logger.info("[CHATGPT] 请求={}".format(json.dumps(data, ensure_ascii=False)))
    response = requests.post(url=api_url, headers=headers, data=json.dumps(data), timeout=600)
    response.raise_for_status()
    response_data = response.json()
    logger.info("[CHATGPT] 响应={}".format(json.dumps(response_data, separators=(',', ':'),ensure_ascii=False)))
    #print(response_data)
    return response_data


def get_token_id_by_app_id(app_id: str) -> str:
    # 使用 SCAN 避免一次性返回所有的匹配键，逐步扫描
    cursor = 0
    while True:
        cursor, login_keys = redis_helper.redis_helper.client.scan(cursor, match='__AI_OPS_WX__:LOGININFO:*')
        
        # 批量获取所有键的 hash 数据
        for k in login_keys:
            r = redis_helper.redis_helper.get_hash(k)
            if r.get("appId") == app_id:
                return r.get("tokenId", "")
        
        # 如果游标为 0，则表示扫描完成
        if cursor == 0:
            break
    
    return ""

def get_login_info_by_app_id(app_id: str) ->dict:
    # 使用 SCAN 避免一次性返回所有的匹配键，逐步扫描
    cursor = 0
    while True:
        cursor, login_keys = redis_helper.redis_helper.client.scan(cursor, match='__AI_OPS_WX__:LOGININFO:*')
        
        # 批量获取所有键的 hash 数据
        for k in login_keys:
            r = redis_helper.redis_helper.get_hash(k)
            if r.get("appId") == app_id:
                return k,r
        
        # 如果游标为 0，则表示扫描完成
        if cursor == 0:
            break
    
    return ""

def contains_url(text):
    # 定义检测网址的正则表达式
    url_pattern = re.compile(
        r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+'
    )
    # 检查字符串是否包含网址
    return bool(url_pattern.search(text))


def get_first_char_if_digit(s):
    if s and s[0].isdigit():  # 判断字符串是否非空且首字符为数字
        return int(s[0])  # 返回数字形式
    return None  # 如果不是数字则返回 None

def remove_at_mention_regex(text):
    # 使用正则表达式去掉“在群聊中@了你”
    return re.sub(r"在群聊中@了你", "", text)

def extract_nickname(text)->str:
    if "在群聊中@了你" in text:
        # 如果包含 "在群聊中@了你"，提取其前面的名字
        match = re.search(r"^(.*?)在群聊中@了你", text)
        if match:
            return match.group(1).strip()
    elif ": @" in text:
        # 如果包含 ": @"，提取其前面的名字
        return text.split(": @")[0].strip()
    return ''


def check_chatroom(userName):
    pattern = r'^\d+@chatroom$' 
    if re.match(pattern, userName):
        return True
    return False