text_to_vector/utils/request_fun.py

# -*- coding: utf-8 -*-
# @Time    : 2023/7/10
# @Author  : lkj
# @description :  服务请求
import copy
import json
import grpc
from proto.text2vector_pb2 import Text2VectorReq, Text2VectorResp
import requests
from pymongo import MongoClient
from a2s.a2s_client import a2s_execute
from a2s.tools import json_serialize, json_deserialize
from config import daili


def top_t(text):
    data = {
        "text": text,
    }
    result = a2s_execute(a2s_ip=daili, topic="topic_tract", timeout=6, bytes_data=json_serialize(data))
    result=json_deserialize(result)
    if result.get('status',400) == 200:
        return result.get('result','')
    else:
        return ''


def text_to_vector(data):
    """
    文本转向量
    :param text:
    :return:
    """
    result = []
    try:
        # 调用服务端方法
        resp = Text2VectorReq(text=data)
        response = a2s_execute(daili, "t2v2", timeout=30, bytes_data=resp.SerializeToString())
        # 获取结果
        req = Text2VectorResp()
        req.ParseFromString(response)
        result = list(req.vector)
    except Exception as e:
        print(e)
    return result


def chat_glm(text):
    "百川"
    response = requests.post('http://123.207.51.36:7885/', json={"prompt":text,"identity":"剑鱼chat","top_p":0.8,"temperature":0.8,},timeout=150).json()
    result = response.get('response', '')
    glm_flg = 1
    # print('glm',result)
    for i in ['属于不同类', '不属于','可能属于', '不属于同一类', '不是同一类', '是不同类', '属于不同', '不是', '不同的', '不一定', '一定不是']:
        if i in result:
            glm_flg = 0
            break
    return glm_flg,result


def ali2(text):
    """
    阿里大模型
    :param text:
    :param classify:
    :return:
    """
    response = requests.post('http://123.207.51.36:7880', json={"prompt":text,"identity":"剑鱼chat","top_p":0.8,"temperature":0.8,'max_length':100},timeout=150  ).json()
    result = response.get('response', '')
    glm_flg = 1
    # print('al',result)
    for i in ['属于不同类', '不属于','可能属于', '不属于同一类', '不是同一类', '是不同类', '属于不同', '不是', '不同的', '不一定', '一定不是']:
        if i in result:
            glm_flg = 0
            break
    return glm_flg,result


def chat_jzy(text):
    '书生'
    response = requests.post(f'http://119.91.64.110:7885/', json={"prompt":text,"identity":"剑鱼chat","top_p":0.8,"temperature":0.8,"max_length":100},timeout=300).json()
    result = response.get('response', '')
    glm_flg = 1
    # print('zy',result)
    for i in ['属于不同类', '不属于','可能属于', '不属于同一类', '不是同一类', '是不同类', '属于不同', '不是', '不同的', '不一定', '一定不是']:
        if i in result:
            glm_flg = 0
            break
    return glm_flg,result


def three_classify(title_,detail):
    if '维修' in title_:
        title = title_.replace('维修','')
        response = requests.post('http://192.168.3.109:20623/search/', data={'title': title, 'detail': detail}).json()
        result = response.get('result', [])
        if result[0] == '货物':
            result = ['服务', '60%']
            return result
        else:
            response = requests.post('http://192.168.3.109:20623/search/',
                                     data={'title': title_, 'detail': detail}).json()
            result = response.get('result', [])
            return result
    else:
        response = requests.post('http://192.168.3.109:20623/search/', data={'title': title_, 'detail': detail}).json()
        result = response.get('result', [])
    return result


def seq_gpt(text,labels,task='分类'):
    """
    #### 三大分类测试    text:'{text}\n属于下面哪一个分类'    labels：'服务类，工程类，货物类'
    :param text:
    :param labels:
    :param task: 可选'分类'or '抽取' 默认分类
    :return:
    """
    response = requests.post('http://192.168.3.109:20016/cls',data={
        'text':f'{text}','task':f'{task}','labels':f'{labels}'}).json()
    print(response)
    result = response.get('result',{}).get('text','')
    return result

def is_goods(title):
    response = requests.post('http://192.168.3.109:20622/',data={'title':title}).json()
    result = response.get('result', [])
    return result


def ts_ent(title):
    response = requests.post('http://192.168.3.109:20631/',data={'text':title}).json()
    print(response)
    result = response.get('result', [])
    entity = result.get('entity',[])
    entity_list = []
    for e in entity:
        if e:
            for flag in ['basic','food','product', 'matter','medicine','activity']:
                if flag in e[1] and e[0] not in entity_list:
                    entity_list.append(e[0])
    res = ''.join(entity_list)
    if not res:
        res = title
    return result,res


def process_model(text):
    # response = requests.get(f'http://192.168.3.109:8998/product_detail?prompt={text}').text
    old_text = copy.deepcopy(text)
    # response = eval(response)
    # print(response.text)
    text_list = []
    # result = response.get('output', [])
    result = a2s_execute("192.168.3.240:9090","recognition_goods",timeout=30,bytes_data=json_serialize({"text":text}))
    result = eval(result).get('output',[])
    for res in result:
        type_ = res.get('type','')
        span = res.get('span','')
        if '材质' in type_ or '款式' in type_ or '产品' in type_ or '对象' in type_ or '适用场景' in type_:
            if span not in text_list:
                text_list.append(span)
        # for flag in ['品牌','系列','型号','规格','颜色','其他','修饰','组织机构']:
        #     if flag in type_:
        #         if '材质' in type_ or '款式' in type_ or '产品' in type_:
        #             continue
        #
        #         text = text.replace(span, '')
        #         break
    text = ''.join(text_list)
    if len(text)<=1:
        text = old_text
    return text


def con():
    mongo_client = MongoClient('192.168.3.71:29099')
    col_ = mongo_client['re4art']['better_goods']
    return col_


if __name__ == '__main__':
    # text = '晨光 M＆G Eplus盒 装黑色长尾夹 ABS92728 32mm 12个/盒 12盒/包 120盒/箱'
    # c = '长尾票夹'
    # print(1111,three_classify('恒源祥 H4YH10全棉长绒棉60支贡缎刺四件套1.5/1.8米床单被套200*230',''))
    # print(222,chat_glm('新华书店/百年初心成大道——党史学习教育案例选编/政治类书籍属于装订图书吗'))
    # print(333,chat_jzy(text,c))
    # exit()
    tes = '润农生物饲料研发生产线技改项目'
    # print(three_classify(tes,''))
    print(text_to_vector(tes))
    exit()
    col = con()
    count = 0

    for row in col.find({"purchasing_score": {'$gte':0.7}},{'title':1,'detail':1,'s_subscopeclass':1,'projectname':1,'purchasinglist':1,'purchasing_score':1}).sort('_id', 1):
        title = row.get('title', '')
        ids = row['_id']
        detail = row.get('detail', '')
        c = three_classify(title,detail)
        basicClass = ''
        rate = '0%'
        if c:
            basicClass = c[0]
            rate = c[1]
        # col.update_one({'_id': ids}, {'$set': {'basicClass': basicClass,'rate':rate}})
        print(ids)