celery/postprocess0110.py

import json
with open('zhaoping_rule', 'r') as f:
    content = f.read()
import json
obj=json.loads(content)
with open('name_label', 'r') as f:
    content = f.read()
import json
name_label=json.loads(content)
baohuceng = ['10-74', '10-75', '10-77', '10-78', '10-80', '10-81', '10-83', '10-84', '10-86', '10-87', '10-90']
from fallback import fallback
def aifilter4(A, #options
       B, #data
       aiclient,
             qwclient,
             dw):
    options=[]
    letters = "ABCDEFGHIJKLMN"
    for i in range(len(A)):
        options.append("给定选项" + letters[i]+"，内容为"+A[i] )

    completion = qwclient.chat.completions.create(
        #model="glm-z1-flash",
        model="Qwen/Qwen3-8B",
        #model="ernie-speed-128k",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": " 背景知识：土建施工中，保温做法有很多种，包括保温砖，保温瓦，保温砂浆，保温板等。保温板有不同材料，比如聚苯乙烯泡沫板，聚苯乙烯挤塑板，聚氨酯保温板等"},
            {"role": "user", "content": "问题描述：" + ",".join(options)  + "。请问选项中是否有保温板的选项？请回答是或者否"},
        ],
        #extra_body={"thinking": {"type": "enabled"}},
        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(thinking_json_string)
    print(json_string)
    if len(json_string) < 4:
        if '否' in json_string:
            return False
        return True
    completion = aiclient.chat.completions.create(
        model="glm-4.5-flash",
        messages=[
            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个是或者否的判断，请将该判断输出"},                       
        ],
        extra_body={"thinking": {"type": "disabled"}},
        #extra_body={"enable_thinking": False},
    )
    json_string = completion.choices[0].message.content
    print(json_string)
    if '否' in json_string:
        return False
    return True

def aifilter3(A, #options
       B, #data
       aiclient,
             qwclient,
             dw):
    options=[]
    letters = "ABCDEFGHIJKLMN"
    for i in range(len(A)):
        options.append("给定选项" + letters[i]+"，内容为"+A[i] )

    completion = qwclient.chat.completions.create(
        #model="glm-z1-flash",
        model="Qwen/Qwen3-8B",
        #model="ernie-speed-128k",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": " 背景知识：土建施工中，保温做法有很多种，包括保温砖，保温瓦，保温砂浆，保温板等。保温板有不同材料，比如聚苯乙烯泡沫板，聚苯乙烯挤塑板，聚氨酯保温板, 岩棉保温板等"},
            {"role": "user", "content": "问题描述： 给定一段工作内容: " + B['label'] + " " +  B['mc'] + " " + B['tz'] + "。请问工作内容的描述中有涉及保温板吗？请回答是或者否"},
        ],
        #extra_body={"thinking": {"type": "enabled"}},
        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(thinking_json_string)
    print(json_string)
    if len(json_string) < 4:
        if '否' in json_string:
            return False
        return True
    completion = aiclient.chat.completions.create(
        model="glm-4.5-flash",
        messages=[
            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个是或者否的判断，请将该判断输出"},                       
        ],
        extra_body={"thinking": {"type": "disabled"}},
        #extra_body={"enable_thinking": False},
    )
    json_string = completion.choices[0].message.content
    print(json_string)
    if '否' in json_string:
        return False
    return True

def aifilter1(A, #options
       B, #data
       aiclient,
             qwclient,
             dw):
    options=[]
    letters = "ABCDEFGHIJKLMN"
    for i in range(len(A)):
        options.append("给定选项" + letters[i]+"，内容为"+A[i] )

    completion = qwclient.chat.completions.create(
        #model="glm-z1-flash",
        model="Qwen/Qwen3-8B",
        #model="ernie-speed-128k",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": " 特殊处理要求：如果工作内容描述中没有明确提到素水泥浆，则去掉所有含有“素水泥浆”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。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": "enabled"}},
        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(thinking_json_string)
    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 = aiclient.chat.completions.create(
        model="glm-4.5-flash",
        messages=[
            {"role": "system", "content": "You are a helpful assistant.请将最终答案以JSON格式输出"},
            {"role": "user", "content": " 给你一段文字如下， " + json_string + ",其中给出了一个类似于[A,B,C]的数组作为结果，请将该最终结果输出"},                       
        ],
        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 aifilter2(A, #options
       B, #data
       aiclient,
             qwclient,
             dw):
    hit_wumian = False
    for entry in A:
        if entry in obj['wumian']:
            hit_wumian=True

    hit_loumian = False
    loumian_entry = ''
    for entry in A:
        if entry in obj['loumian']:
            hit_loumian=True
            loumian_entry = entry
    if hit_wumian and hit_loumian:
        return [x for x in A if x != loumian_entry]
    return A


def postprocess0110(selected, data, aiclient, qwclient, label_name, name_dw):
    ban = aifilter3(selected, data, aiclient, qwclient, name_dw)
    if ban:
        ban2 = aifilter4(selected, data, aiclient, qwclient, name_dw)
        if not ban2:
            if '墙面' in data['mc']:
                selected.append('第十一章  保温、隔热、防腐工程 11.1  保温、隔热工程 11.1.2  墙、柱、天棚及其它 外墙外保温  聚苯乙烯挤塑板 厚度25mm 混凝土墙面')
            else:
                selected.append('第十一章  保温、隔热、防腐工程 11.1  保温、隔热工程 11.1.1  屋、楼地面 屋面、楼地面保温隔热 聚苯乙烯挤塑板(厚25mm)')
    prime = aifilter1(selected, data, aiclient, qwclient, name_dw)
    if '界面剂' in data['tz']:##保温
        if len([x for x in prime if '第十四章  墙柱面工程 14.1  一般抹灰 14.1.3  保温砂浆及抗裂基层 刷界面剂' in x]) == 0:
            prime.append('第十四章  墙柱面工程 14.1  一般抹灰 14.1.3  保温砂浆及抗裂基层 刷界面剂 混凝土面') ##需要换
    if '玻纤网' in data['tz']:##保温
        if '第十四章  墙柱面工程 14.1  一般抹灰 14.1.3  保温砂浆及抗裂基层 墙面耐碱玻纤网格布 一层' not in prime:
            prime.append('第十四章  墙柱面工程 14.1  一般抹灰 14.1.3  保温砂浆及抗裂基层 墙面耐碱玻纤网格布 一层') ##需要换

    return prime