celery/postprocess0114.py

import time
from fallback import fallback
from config import simplemodel
from template import xuanxiang
import json
with open('name_label', 'r') as f:
    content = f.read()
name_label=json.loads(content)
from fallback import fallback
def aifilter1(A, #options
       B, #data
       aiclient,
             qwclient,
              sfclient,
             dw):
    options=[]
    letters = "ABCDEFGHIJKLMN"
    for i in range(len(A)):
        options.append("给定选项" + letters[i]+"，内容为"+A[i] )

    completion = aiclient.chat.completions.create(
        model="glm-4.5-air",
        #model="THUDM/GLM-Z1-9B-0414",
        #model="ernie-speed-128k",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": " 特殊处理要求一：如果工作内容提及的是内墙，则去掉所有精确含有“外墙涂料”字样的选项"},
            {"role": "user", "content": " 特殊处理要求二：如果工作内容没有明确提及抗裂腻子，则去掉所有精确含有“抗裂腻子”字样的选项"},
            {"role": "user", "content": " 特殊处理要求三：如果工作内容没有明确提及批腻子，则去掉所有精确含有“外墙批抗裂腻子”字样的选项"},
            {"role": "user", "content": " 重要提示：选项指的是给定的A、B、C之类的选项，不是指的工作内容中的可能的1、2、3这样罗列的特征"},
            {"role": "user", "content": " 重要提示：除特殊处理要求提及的内容外，不需考虑选项内容与工作内容是否符合，只需要根据特殊处理要求做出处理"},
            {"role": "user", "content": "问题描述： 给定一段工作内容: " + B['label'] + " " +  B['mc'] + " " + B['tz'] +  "，".join(options) + "。请根据处理要求做出处理，并返回结果, 删除选项必须对应到明确的特殊处理要求，不要擅自删除选项。例如，如果处理完后剩余A,B,C三个选项，请返回[A,B,C]"},
        ],
        extra_body={"thinking": {"type": "disabled"}},
        #extra_body={"enable_thinking": True},
        #stream=True
    )
    #done_thinking = False
    #json_string=""
    #thinking_json_string=""
    #for chunk in completion:
    #    thinking_chunk = chunk.choices[0].delta.reasoning_content
    #    answer_chunk = chunk.choices[0].delta.content
    #    if thinking_chunk != '':
    #        thinking_json_string = thinking_json_string +  thinking_chunk
    #    elif answer_chunk != '':
    #        if not done_thinking:
    #            done_thinking = True
    #        json_string = json_string + answer_chunk
    json_string = completion.choices[0].message.content
    #print(completion.choices[0].message.reasoning_content)
    print(json_string)
    if len([x for x in json_string if x != ',' and x != '[' and x != ']' and x != ' ' and (x < 'A' or x > 'M')]) < 5:
        answer=[]
        if 'A' in json_string and len(A) > 0:
            answer.append(A[0])
        if 'B' in json_string and len(A) > 1:
            answer.append(A[1])
        if 'C' in json_string and len(A) > 2:
            answer.append(A[2])
        if 'D' in json_string and len(A) > 3:
            answer.append(A[3])
        if 'E' in json_string and len(A) > 4:
            answer.append(A[4])
        if 'F' in json_string and len(A) > 5:
            answer.append(A[5])
        if 'G' in json_string and len(A) > 6:
            answer.append(A[6])
        if 'H' in json_string and len(A) > 7:
            answer.append(A[7])
        if 'I' in json_string and len(A) > 8:
            answer.append(A[8])
        if 'J' in json_string and len(A) > 9:
            answer.append(A[9])
        return answer
    completion = sfclient.chat.completions.create(
        #model="glm-4.5-flash",
        model=simplemodel(),
        messages=xuanxiang(json_string),
        extra_body={"thinking": {"type": "disabled"}},
        #extra_body={"enable_thinking": False},
    )
    json_string = completion.choices[0].message.content
    print(json_string)
    answer=[]
    if 'A' in json_string and len(A) > 0:
        answer.append(A[0])
    if 'B' in json_string and len(A) > 1:
        answer.append(A[1])
    if 'C' in json_string and len(A) > 2:
        answer.append(A[2])
    if 'D' in json_string and len(A) > 3:
        answer.append(A[3])
    if 'E' in json_string and len(A) > 4:
        answer.append(A[4])
    if 'F' in json_string and len(A) > 5:
        answer.append(A[5])
    if 'G' in json_string and len(A) > 6:
        answer.append(A[6])
    if 'H' in json_string and len(A) > 7:
        answer.append(A[7])
    if 'I' in json_string and len(A) > 8:
        answer.append(A[8])
    if 'J' in json_string and len(A) > 9:
        answer.append(A[9])
    return answer

def handle_nizi(selected, data,aiclient,qwclient,sfclient,label_name,name_dw,candidates):
    options=[]
    options_=[]
    options.append('不使用腻子')
    options.append('使用腻子')
    options.append('使用保温腻子')
    options.append('腻子2道（遍）')
    options.append('腻子3道（遍）')
    letters = "ABCDEFGHIJKLMN"
    for i in range(len(options)):
        options_.append("给定选项" + letters[i]+"，内容为"+options[i] )

    completion = aiclient.chat.completions.create(
        model="glm-4.5-air",
        #model="THUDM/GLM-Z1-9B-0414",
        #model="ernie-speed-128k",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "问题描述： 给定一段工作内容;"+ "，".join(options_) + "。请选出与工作内容最匹配的选项。例如，如果工作内容中不使用腻子，则输出A。再例如，如果工作内容中提到刷腻子2遍，则输出D.现在给定一段工作内容: "+ data['label'] + " " +  data['mc'] + " " + data['tz'] + "\n请给出分析过程并请输出A、B这样的字母作为答案"},
        ],
        extra_body={"thinking": {"type": "disabled"}},
        #extra_body={"enable_thinking": True},
        #stream=True
    )
    #done_thinking = False
    #json_string=""
    #thinking_json_string=""
    #for chunk in completion:
    #    thinking_chunk = chunk.choices[0].delta.reasoning_content
    #    answer_chunk = chunk.choices[0].delta.content
    #    if thinking_chunk != '':
    #        thinking_json_string = thinking_json_string +  thinking_chunk
    #    elif answer_chunk != '':
    #        if not done_thinking:
    #            done_thinking = True
    #        json_string = json_string + answer_chunk
    json_string = completion.choices[0].message.content
    #print(completion.choices[0].message.reasoning_content)
    print(json_string)
    if len(json_string) < 5:
        answer='A'
        if 'B' in json_string:
            answer='B'
        if 'C' in json_string:
            answer='C'
        if 'D' in json_string:
            answer='D'
        if 'E' in json_string:
            answer='E'
    else:
        completion = sfclient.chat.completions.create(
        #model="glm-4.5-flash",
        model=simplemodel(),
        messages=[
            {"role": "system", "content": "You are a helpful assistant.请将结果以JSON格式输出"},
            {"role": "user", "content": "问题描述： 给定一段内容: " +  json_string + "。文字中给出了一个类似于A、B的字母作为答案，请输出这个答案。不需要输出分析过程"},
        ],
        extra_body={"thinking": {"type": "disabled"}},
        #extra_body={"enable_thinking": False},
    )
        json_string = completion.choices[0].message.content
        print(json_string)
        answer='A'
        if 'B' in json_string:
            answer='B'
        if 'C' in json_string:
            answer='C'
        if 'D' in json_string:
            answer='D'
        if 'E' in json_string:
            answer='E'
    return answer
def handle_neiqiang(selected, data,aiclient,qwclient,sfclient,label_name,name_dw,candidates):
    options=[]
    options_=[]
    options.append('调和漆')##
    options.append('乳胶漆(水性水泥漆)')##
    options.append('砂胶喷涂')##
    options.append('多彩涂料喷涂')##一整套
    options.append('浮雕喷涂料')##一整套
    options.append('喷刷白水泥浆、石灰浆、石灰大白浆')##一整套
    options.append('水性防霉涂料')##
    letters = "ABCDEFGHIJKLMN"
    for i in range(len(options)):
        options_.append("给定选项" + letters[i]+"，内容为"+options[i] )

    completion = aiclient.chat.completions.create(
        model="glm-4.5-air",
        #model="THUDM/GLM-Z1-9B-0414",
        #model="ernie-speed-128k",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "问题描述： 给定一段工作内容: " + data['label'] + " " +  data['mc'] + " " + data['tz'] +  "，".join(options_) + "。请选出与工作内容最匹配的涂料选项。如果没有特别匹配的选项，则默认选择选项B。请给出你的选择，请输出A、B这样的字母作为答案"},
        ],
        extra_body={"thinking": {"type": "disabled"}},
        #extra_body={"enable_thinking": True},
        #stream=True
    )
    #done_thinking = False
    #json_string=""
    #thinking_json_string=""
    #for chunk in completion:
    #    thinking_chunk = chunk.choices[0].delta.reasoning_content
    #    answer_chunk = chunk.choices[0].delta.content
    #    if thinking_chunk != '':
    #        thinking_json_string = thinking_json_string +  thinking_chunk
    #    elif answer_chunk != '':
    #        if not done_thinking:
    #            done_thinking = True
    #        json_string = json_string + answer_chunk
    json_string = completion.choices[0].message.content
    #print(completion.choices[0].message.reasoning_content)
    print(json_string)
    if len(json_string) < 5:
        answer='A'
        if 'B' in json_string:
            answer='B'
        if 'C' in json_string:
            answer='C'
        if 'D' in json_string:
            answer='D'
        if 'E' in json_string:
            answer='E'
        if 'F' in json_string:
            answer='F'
        if 'G' in json_string:
            answer='G'
    else:
        completion = sfclient.chat.completions.create(
        #model="glm-4.5-flash",
        model=simplemodel(),
        messages=[
            {"role": "system", "content": "You are a helpful assistant.请将结果以JSON格式输出"},
            {"role": "user", "content": "问题描述： 给定一段内容: " +  json_string + "。文字中给出了一个类似于A、B的字母作为答案，请输出这个答案。不需要输出分析过程"},
        ],
        extra_body={"thinking": {"type": "disabled"}},
        #extra_body={"enable_thinking": False},
    )
        json_string = completion.choices[0].message.content
        print(json_string)
        answer='A'
        if 'B' in json_string:
            answer='B'
        if 'C' in json_string:
            answer='C'
        if 'D' in json_string:
            answer='D'
        if 'E' in json_string:
            answer='E'
        if 'F' in json_string:
            answer='F'
        if 'G' in json_string:
            answer='G'
    if answer=='A':
        return [name_label['17-160']] 
    if answer=='B':
        return [name_label['17-177']] 
    if answer=='C':
        return [name_label['17-205']] 
    if answer=='D':
        return [name_label['17-210']] 
    if answer=='E':
        return [name_label['17-220']]
    if answer=='F':
        return [name_label['17-224']] 
    if answer=='G': 
        nizi = handle_nizi(selected, data,aiclient,qwclient,sfclient,label_name,name_dw,candidates)
        if nizi == 'A':
            return [name_label['17-CB5']]
        return [name_label['17-CB4']]##满刮腻子
def handle_waiqiang(nizi, selected, data,aiclient,qwclient,sfclient,label_name,name_dw,candidates):
    options=[]
    options_=[]
    options.append('外墙丙烯酸弹性乳胶漆')##不包括腻子,二遍
    options.append('外墙苯丙乳胶漆')##不包括腻子，二遍
    options.append('外墙溶剂涂料')##一整套
    options.append('外墙弹性涂料')##包括两遍腻子，一底二面
    options.append('外墙批抗裂腻子（不刷涂料）')
    options.append('外墙彩砂喷涂')##包括腻子二遍，喷面层二遍
    options.append('喷涂外墙乳液型涂料')##一整套
    options.append('外墙真石漆')##一整套
    options.append('浮雕喷涂料外墙')##一整套
    letters = "ABCDEFGHIJKLMN"
    for i in range(len(options)):
        options_.append("给定选项" + letters[i]+"，内容为"+options[i] )

    completion = aiclient.chat.completions.create(
        model="glm-4.5-air",
        #model="THUDM/GLM-Z1-9B-0414",
        #model="ernie-speed-128k",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "问题描述： 给定一段工作内容: " + data['label'] + " " +  data['mc'] + " " + data['tz'] +  "，".join(options_) + "。请选出与工作内容最匹配的外墙涂料选项。如果工作内容只提及外墙涂料，没有更细节的介绍，则选择选项D。请给出你的选择，请输出A、B这样的字母作为答案"},
        ],
        extra_body={"thinking": {"type": "disabled"}},
        #extra_body={"enable_thinking": True},
        #stream=True
    )
    #done_thinking = False
    #json_string=""
    #thinking_json_string=""
    #for chunk in completion:
    #    thinking_chunk = chunk.choices[0].delta.reasoning_content
    #    answer_chunk = chunk.choices[0].delta.content
    #    if thinking_chunk != '':
    #        thinking_json_string = thinking_json_string +  thinking_chunk
    #    elif answer_chunk != '':
    #        if not done_thinking:
    #            done_thinking = True
    #        json_string = json_string + answer_chunk
    json_string = completion.choices[0].message.content
    #print(completion.choices[0].message.reasoning_content)
    print(json_string)
    if len(json_string) < 5:
        answer='A'
        if 'B' in json_string:
            answer='B'
        if 'C' in json_string:
            answer='C'
        if 'D' in json_string:
            answer='D'
        if 'E' in json_string:
            answer='E'
        if 'F' in json_string:
            answer='F'
        if 'G' in json_string:
            answer='G'
        if 'H' in json_string:
            answer='H'
        if 'I' in json_string:
            answer='I'
    else:
        completion = sfclient.chat.completions.create(
        #model="glm-4.5-flash",
        model=simplemodel(),
        messages=[
            {"role": "system", "content": "You are a helpful assistant.请将结果以JSON格式输出"},
            {"role": "user", "content": "问题描述： 给定一段内容: " +  json_string + "。文字中给出了一个类似于A、B的字母作为答案，请输出这个答案。不需要输出分析过程"},
        ],
        extra_body={"thinking": {"type": "disabled"}},
        #extra_body={"enable_thinking": False},
    )
        json_string = completion.choices[0].message.content
        print(json_string)
        answer='A'
        if 'B' in json_string:
            answer='B'
        if 'C' in json_string:
            answer='C'
        if 'D' in json_string:
            answer='D'
        if 'E' in json_string:
            answer='E'
        if 'F' in json_string:
            answer='F'
        if 'G' in json_string:
            answer='G'
        if 'H' in json_string:
            answer='H'
        if 'I' in json_string:
            answer='I'
    if answer=='A':
        return [name_label['NT17-补22']] + nizi
    if answer=='B':
        return [name_label['17-192']] + nizi
    if answer=='C':
        return [name_label['17-199']] + nizi
    if answer=='D':
        return [name_label['17-197']] + nizi
    if answer=='E':
        return [name_label['17-195']]
    if answer=='F':
        return [name_label['17-202']] + nizi
    if answer=='G': 
        return [name_label['17-207']]+ nizi
    if answer=='H':
        return [name_label['17-218']] + nizi
    if answer=='I':
        return [name_label['17-222']] + nizi
def handle_mohui(selected, data,aiclient,qwclient,sfclient,label_name,name_dw,candidates):
    if '外墙' in data['tz']:
        result = []
        nizi = handle_nizi(selected, data,aiclient,qwclient,sfclient,label_name,name_dw,candidates)
        if nizi == 'C':
            result = [name_label['14-CB1(1)']]
        if nizi == 'D':
            result = [name_label['17-164']]
        if nizi == 'E':
            result = [name_label['17-164']]
        return handle_waiqiang(result, selected,data,aiclient,qwclient,sfclient,label_name,name_dw,candidates)
    return handle_neiqiang(selected,data,aiclient,qwclient,sfclient,label_name,name_dw,candidates)
def postprocess0114(selected, data, aiclient, qwclient, sfclient, label_name, name_dw, candidates):
    mu =0
    jinshu=0
    mohui=0
    for entry in selected:
        if '17.1.1' in entry:
            mu = mu + 1
        if '17.1.2' in entry:
            jinshu = jinshu + 1
        if '17.1.3' in entry:
            mohui = mohui + 1
    if mohui > mu and mohui > jinshu:
        return handle_mohui(selected,data,aiclient,qwclient,sfclient,label_name,name_dw,candidates)
    prime = aifilter1(selected, data, aiclient, qwclient, sfclient, name_dw)
    if len(prime) == 0:
        selected = fallback(candidates, data, aiclient, qwclient, sfclient, None, None)
        return selected
    
    return prime