BIDI_ML_INFO_EXTRACTION/BiddingKG/dl/interface/predictor.py

'''
Created on 2018年12月26日

@author: User
'''

import os
import sys
from BiddingKG.dl.common.nerUtils import *
sys.path.append(os.path.abspath("../.."))
# from keras.engine import topology
# from keras import models
# from keras import layers
# from keras_contrib.layers.crf import CRF
# from keras.preprocessing.sequence import pad_sequences
# from keras import optimizers,losses,metrics
from BiddingKG.dl.common.Utils import *
from BiddingKG.dl.interface.modelFactory import *
import tensorflow as tf
from BiddingKG.dl.product.data_util import decode, process_data
from BiddingKG.dl.interface.Entitys import Entity
from BiddingKG.dl.complaint.punish_predictor import Punish_Extract
from BiddingKG.dl.money.re_money_total_unit import extract_total_money, extract_unit_money
from bs4 import BeautifulSoup
import copy
import calendar
import datetime
# import fool   # 统一用 selffool ，阿里云上只有selffool 包

from threading import RLock
dict_predictor = {"codeName":{"predictor":None,"Lock":RLock()},
              "prem":{"predictor":None,"Lock":RLock()},
              "epc":{"predictor":None,"Lock":RLock()},
              "roleRule":{"predictor":None,"Lock":RLock()},
              "roleRuleFinal":{"predictor":None,"Lock":RLock()},
              "tendereeRuleRecall":{"predictor":None,"Lock":RLock()},
                  "form":{"predictor":None,"Lock":RLock()},
                  "time":{"predictor":None,"Lock":RLock()},
                  "punish":{"predictor":None,"Lock":RLock()},
                  "product":{"predictor":None,"Lock":RLock()},
                "product_attrs":{"predictor":None,"Lock":RLock()},
                  "channel": {"predictor": None, "Lock": RLock()},
                  "deposit_payment_way": {"predictor": None, "Lock": RLock()},
                  "total_unit_money": {"predictor": None, "Lock": RLock()},
                  "industry": {"predictor": None, "Lock": RLock()}
                  }


def getPredictor(_type):
    if _type in dict_predictor:
        with dict_predictor[_type]["Lock"]:
            if dict_predictor[_type]["predictor"] is None:
                if _type == "codeName":
                    dict_predictor[_type]["predictor"] = CodeNamePredict()
                if _type == "prem":
                    dict_predictor[_type]["predictor"] = PREMPredict()
                if _type == "epc":
                    dict_predictor[_type]["predictor"] = EPCPredict()
                if _type == "roleRule":
                    dict_predictor[_type]["predictor"] = RoleRulePredictor()
                if _type == "roleRuleFinal":
                    dict_predictor[_type]["predictor"] = RoleRuleFinalAdd()
                if _type == "tendereeRuleRecall":
                    dict_predictor[_type]["predictor"] = TendereeRuleRecall()
                if _type == "form":
                    dict_predictor[_type]["predictor"] = FormPredictor()
                if _type == "time":
                    dict_predictor[_type]["predictor"] = TimePredictor()
                if _type == "punish":
                    dict_predictor[_type]["predictor"] = Punish_Extract()
                if _type == "product":
                    dict_predictor[_type]["predictor"] = ProductPredictor()
                if _type == "product_attrs":
                    dict_predictor[_type]["predictor"] = ProductAttributesPredictor()
                if _type == "channel":
                    dict_predictor[_type]["predictor"] = DocChannel()
                if _type == 'deposit_payment_way':
                    dict_predictor[_type]["predictor"] = DepositPaymentWay()
                if _type == 'total_unit_money':
                    dict_predictor[_type]["predictor"] = TotalUnitMoney()
                if _type == 'industry':
                    dict_predictor[_type]["predictor"] = IndustryPredictor()
            return dict_predictor[_type]["predictor"]
    raise NameError("no this type of predictor")


# 编号名称模型
class CodeNamePredict():
    
    def __init__(self,EMBED_DIM=None,BiRNN_UNITS=None,lazyLoad=getLazyLoad()):
        
        self.model = None
        self.MAX_LEN = None
        self.model_code = None
        if EMBED_DIM is None:
            self.EMBED_DIM = 60
        else:
            self.EMBED_DIM = EMBED_DIM
        if BiRNN_UNITS is None:
            self.BiRNN_UNITS = 200
        else:
            self.BiRNN_UNITS = BiRNN_UNITS
        self.filepath = os.path.dirname(__file__)+"/../projectCode/models/model_project_"+str(self.EMBED_DIM)+"_"+str(self.BiRNN_UNITS)+".hdf5"
        #self.filepath = "../projectCode/models/model_project_60_200_200ep017-loss6.456-val_loss7.852-val_acc0.969.hdf5"
        self.filepath_code = os.path.dirname(__file__)+"/../projectCode/models/model_code.hdf5"
        vocabpath = os.path.dirname(__file__)+"/codename_vocab.pk"
        classlabelspath = os.path.dirname(__file__)+"/codename_classlabels.pk"
        self.vocab = load(vocabpath)
        self.class_labels = load(classlabelspath)
        
        #生成提取编号和名称的正则
        id_PC_B = self.class_labels.index("PC_B")
        id_PC_M = self.class_labels.index("PC_M")
        id_PC_E = self.class_labels.index("PC_E")
        id_PN_B = self.class_labels.index("PN_B")
        id_PN_M = self.class_labels.index("PN_M")
        id_PN_E = self.class_labels.index("PN_E")
        self.PC_pattern = re.compile(str(id_PC_B)+str(id_PC_M)+"*"+str(id_PC_E))
        self.PN_pattern = re.compile(str(id_PN_B)+str(id_PN_M)+"*"+str(id_PN_E))
        # print("pc",self.PC_pattern)
        # print("pn",self.PN_pattern)
        self.word2index = dict((w,i) for i,w in enumerate(np.array(self.vocab)))
        
        self.inputs = None
        self.outputs = None
        self.sess_codename = tf.Session(graph=tf.Graph())
        self.sess_codesplit = tf.Session(graph=tf.Graph())
        self.inputs_code = None
        self.outputs_code = None
        if not lazyLoad:
            self.getModel()
            self.getModel_code()
        
        
        
    def getModel(self):
        '''
        @summary: 取得编号和名称模型
        '''
        if self.inputs is None:
            log("get model of codename")
            with self.sess_codename.as_default():
                with self.sess_codename.graph.as_default():
                    meta_graph_def = tf.saved_model.loader.load(self.sess_codename, ["serve"], export_dir=os.path.dirname(__file__)+"/codename_savedmodel_tf")
                    signature_key = tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
                    signature_def = meta_graph_def.signature_def
                    self.inputs = self.sess_codename.graph.get_tensor_by_name(signature_def[signature_key].inputs["inputs"].name)
                    self.inputs_length = self.sess_codename.graph.get_tensor_by_name(signature_def[signature_key].inputs["inputs_length"].name)
                    self.keepprob = self.sess_codename.graph.get_tensor_by_name(signature_def[signature_key].inputs["keepprob"].name)
                    self.logits = self.sess_codename.graph.get_tensor_by_name(signature_def[signature_key].outputs["logits"].name)
                    self.trans = self.sess_codename.graph.get_tensor_by_name(signature_def[signature_key].outputs["trans"].name)

                return self.inputs,self.inputs_length,self.keepprob,self.logits,self.trans
        else:
            return self.inputs,self.inputs_length,self.keepprob,self.logits,self.trans
        '''    
        if self.model is None:
            self.model = self.getBiLSTMCRFModel(self.MAX_LEN, self.vocab, self.EMBED_DIM, self.BiRNN_UNITS, self.class_labels,weights=None)
            self.model.load_weights(self.filepath)
        return self.model
        '''
    
    def getModel_code(self):
        if self.inputs_code is None:
            log("get model of code")
            with self.sess_codesplit.as_default():
                with self.sess_codesplit.graph.as_default():
                    meta_graph_def = tf.saved_model.loader.load(self.sess_codesplit, ["serve"], export_dir=os.path.dirname(__file__)+"/codesplit_savedmodel")
                    signature_key = tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
                    signature_def = meta_graph_def.signature_def
                    self.inputs_code = []
                    self.inputs_code.append(self.sess_codesplit.graph.get_tensor_by_name(signature_def[signature_key].inputs["input0"].name))
                    self.inputs_code.append(self.sess_codesplit.graph.get_tensor_by_name(signature_def[signature_key].inputs["input1"].name))
                    self.inputs_code.append(self.sess_codesplit.graph.get_tensor_by_name(signature_def[signature_key].inputs["input2"].name))
                    self.outputs_code = self.sess_codesplit.graph.get_tensor_by_name(signature_def[signature_key].outputs["outputs"].name)
                    self.sess_codesplit.graph.finalize()
                    return self.inputs_code,self.outputs_code
        else:
            return self.inputs_code,self.outputs_code
        '''
        if self.model_code is None:
            log("get model of model_code")
            with self.sess_codesplit.as_default():
                with self.sess_codesplit.graph.as_default():
                    self.model_code = models.load_model(self.filepath_code, custom_objects={'precision':precision,'recall':recall,'f1_score':f1_score})
        return self.model_code
        '''
    
    def getBiLSTMCRFModel(self,MAX_LEN,vocab,EMBED_DIM,BiRNN_UNITS,chunk_tags,weights):
        '''
        model = models.Sequential()
        model.add(layers.Embedding(len(vocab), EMBED_DIM, mask_zero=True))  # Random embedding
        model.add(layers.Bidirectional(layers.LSTM(BiRNN_UNITS // 2, return_sequences=True)))
        crf = CRF(len(chunk_tags), sparse_target=True)
        model.add(crf)
        model.summary()
        model.compile('adam', loss=crf.loss_function, metrics=[crf.accuracy])
        return model
        '''
        input = layers.Input(shape=(None,))
        if weights is not None:
            embedding = layers.embeddings.Embedding(len(vocab),EMBED_DIM,mask_zero=True,weights=[weights],trainable=True)(input)
        else:
            embedding = layers.embeddings.Embedding(len(vocab),EMBED_DIM,mask_zero=True)(input)
        bilstm = layers.Bidirectional(layers.LSTM(BiRNN_UNITS//2,return_sequences=True))(embedding)
        bilstm_dense = layers.TimeDistributed(layers.Dense(len(chunk_tags)))(bilstm)
        crf = CRF(len(chunk_tags),sparse_target=True)
        crf_out = crf(bilstm_dense)
        model = models.Model(input=[input],output = [crf_out])
        model.summary()
        model.compile(optimizer = 'adam', loss = crf.loss_function, metrics = [crf.accuracy])
        return model
    
    #根据规则补全编号或名称两边的符号
    def fitDataByRule(self,data):
        symbol_dict = {"(":")",
                       "（":"）",
                       "[":"]",
                       "【":"】",
                       ")":"(",
                       "）":"（",
                       "]":"[",
                       "】":"【"}
        leftSymbol_pattern = re.compile("[\(（\[【]")
        rightSymbol_pattern = re.compile("[\)）\]】]")
        leftfinds = re.findall(leftSymbol_pattern,data)
        rightfinds = re.findall(rightSymbol_pattern,data)
        result = data
        if len(leftfinds)+len(rightfinds)==0:
            return data
        elif len(leftfinds)==len(rightfinds):
            return data
        elif abs(len(leftfinds)-len(rightfinds))==1:
            if len(leftfinds)>len(rightfinds):
                if symbol_dict.get(data[0]) is not None:
                    result = data[1:]
                else:
                    #print(symbol_dict.get(leftfinds[0]))
                    result = data+symbol_dict.get(leftfinds[0])
            else:
                if symbol_dict.get(data[-1]) is not None:
                    result = data[:-1]
                else:
                    result = symbol_dict.get(rightfinds[0])+data
        return  result

    def decode(self,logits, trans, sequence_lengths, tag_num):
        viterbi_sequences = []
        for logit, length in zip(logits, sequence_lengths):
            score = logit[:length]
            viterbi_seq, viterbi_score = viterbi_decode(score, trans)
            viterbi_sequences.append(viterbi_seq)
        return viterbi_sequences
    
    def predict(self,list_sentences,list_entitys=None,MAX_AREA = 5000):
        #@summary: 获取每篇文章的code和name
        pattern_score = re.compile("工程|服务|采购|施工|项目|系统|招标|中标|公告|学校|[大中小]学校?|医院|公司|分公司|研究院|政府采购中心|学院|中心校?|办公室|政府|财[政务]局|办事处|委员会|[部总支]队|警卫局|幼儿园|党委|党校|银行|分行|解放军|发电厂|供电局|管理所|供电公司|卷烟厂|机务段|研究[院所]|油厂|调查局|调查中心|出版社|电视台|监狱|水厂|服务站|信用合作联社|信用社|交易所|交易中心|交易中心党校|科学院|测绘所|运输厅|管理处|局|中心|机关|部门?|处|科|厂|集团|图书馆|馆|所|厅|楼|区|酒店|场|基地|矿|餐厅|酒店")

        result = []
        index_unk = self.word2index.get("<unk>")
        # index_pad = self.word2index.get("<pad>")
        if list_entitys is None:
            list_entitys = [[] for _ in range(len(list_sentences))]
        for list_sentence,list_entity in zip(list_sentences,list_entitys):
            if len(list_sentence)==0:
                result.append([{"code":[],"name":""}])
                continue
            doc_id = list_sentence[0].doc_id
            # sentences = []
            # for sentence in list_sentence:
            #     if len(sentence.sentence_text)>MAX_AREA:
            #         for _sentence_comma in re.split("[;；，\n]",sentence):
            #             _comma_index = 0
            #             while(_comma_index<len(_sentence_comma)):
            #                 sentences.append(_sentence_comma[_comma_index:_comma_index+MAX_AREA])
            #                 _comma_index += MAX_AREA
            #     else:
            #         sentences.append(sentence+"。")
            list_sentence.sort(key=lambda x:len(x.sentence_text),reverse=True)
            _begin_index = 0
            
            item = {"code":[],"name":""}
            code_set = set()
            dict_name_freq_score = dict()
            while(True):
                MAX_LEN = len(list_sentence[_begin_index].sentence_text)
                if MAX_LEN>MAX_AREA:
                    MAX_LEN = MAX_AREA
                _LEN = MAX_AREA//MAX_LEN
                #预测

                x = [[self.word2index.get(word,index_unk)for word in sentence.sentence_text[:MAX_AREA]]for sentence in list_sentence[_begin_index:_begin_index+_LEN]]
                # x = [[getIndexOfWord(word) for word in sentence.sentence_text[:MAX_AREA]]for sentence in list_sentence[_begin_index:_begin_index+_LEN]]
                x_len = [len(_x) if len(_x) < MAX_LEN else MAX_LEN for _x in x]
                x = pad_sequences(x,maxlen=MAX_LEN,padding="post",truncating="post")

                if USE_API:
                    requests_result = requests.post(API_URL + "/predict_codeName", json={"inouts": x.tolist(), "inouts_len": x_len},verify=True)
                    predict_y = json.loads(requests_result.text)['result']
                    # print("cost_time:", json.loads(requests_result.text)['cost_time'])
                    # print(MAX_LEN,_LEN,_begin_index)
                else:
                    with self.sess_codename.as_default():
                        t_input,t_input_length,t_keepprob,t_logits,t_trans = self.getModel()
                        _logits,_trans = self.sess_codename.run([t_logits,t_trans],feed_dict={t_input:x,
                                                                                              t_input_length:x_len,
                                                                                              t_keepprob:1.0})
                        predict_y = self.decode(_logits,_trans,x_len,7)
                        # print('==========',_logits)

                        '''
                        for item11 in np.argmax(predict_y,-1):
                            print(item11)
                        print(predict_y)
                        '''
                # print(predict_y)
                for sentence,predict in zip(list_sentence[_begin_index:_begin_index+_LEN],np.array(predict_y)):
                    pad_sentence = sentence.sentence_text[:MAX_LEN]
                    join_predict = "".join([str(s) for s in predict])
                    # print(pad_sentence)
                    # print(join_predict)
                    code_x = []
                    code_text = []
                    temp_entitys = []
                    for iter in re.finditer(self.PC_pattern,join_predict):
                        get_len = 40
                        if iter.span()[0]<get_len:
                            begin = 0
                        else:
                            begin = iter.span()[0]-get_len
                        end = iter.span()[1]+get_len
                        code_x.append(embedding_word([pad_sentence[begin:iter.span()[0]],pad_sentence[iter.span()[0]:iter.span()[1]].replace("，",""),pad_sentence[iter.span()[1]:end]],shape=(3,get_len,60)))
                        code_text.append(pad_sentence[iter.span()[0]:iter.span()[1]].replace("，",""))
                        _entity = Entity(doc_id=sentence.doc_id,entity_id="%s_%s_%s_%s"%(sentence.doc_id,sentence.sentence_index,iter.span()[0],iter.span()[1]),entity_text=pad_sentence[iter.span()[0]:iter.span()[1]].replace("，",""),entity_type="code",sentence_index=sentence.sentence_index,begin_index=0,end_index=0,wordOffset_begin=iter.span()[0],wordOffset_end=iter.span()[1],in_attachment=sentence.in_attachment)
                        temp_entitys.append(_entity)
                    #print("code",code_text)
                    if len(code_x)>0:
                        code_x = np.transpose(np.array(code_x,dtype=np.float32),(1,0,2,3))
                        if USE_PAI_EAS:
                            request = tf_predict_pb2.PredictRequest()
                            request.inputs["input0"].dtype = tf_predict_pb2.DT_FLOAT
                            request.inputs["input0"].array_shape.dim.extend(np.shape(code_x[0]))
                            request.inputs["input0"].float_val.extend(np.array(code_x[0],dtype=np.float64).reshape(-1))
                            request.inputs["input1"].dtype = tf_predict_pb2.DT_FLOAT
                            request.inputs["input1"].array_shape.dim.extend(np.shape(code_x[1]))
                            request.inputs["input1"].float_val.extend(np.array(code_x[1],dtype=np.float64).reshape(-1))
                            request.inputs["input2"].dtype = tf_predict_pb2.DT_FLOAT
                            request.inputs["input2"].array_shape.dim.extend(np.shape(code_x[2]))
                            request.inputs["input2"].float_val.extend(np.array(code_x[2],dtype=np.float64).reshape(-1))
                            request_data = request.SerializeToString()
                            list_outputs = ["outputs"]
                            _result = vpc_requests(codeclasses_url, codeclasses_authorization, request_data, list_outputs)
                            if _result is not None:
                                predict_code = _result["outputs"]
                            else:
                                with self.sess_codesplit.as_default():
                                    with self.sess_codesplit.graph.as_default():
                                        predict_code = self.getModel_code().predict([code_x[0],code_x[1],code_x[2]])
                        else:
                            with self.sess_codesplit.as_default():
                                with self.sess_codesplit.graph.as_default():
                                    inputs_code,outputs_code = self.getModel_code()
                                    predict_code = limitRun(self.sess_codesplit,[outputs_code],feed_dict={inputs_code[0]:code_x[0],inputs_code[1]:code_x[1],inputs_code[2]:code_x[2]})[0]

                                    #predict_code = self.sess_codesplit.run(outputs_code,feed_dict={inputs_code[0]:code_x[0],inputs_code[1]:code_x[1],inputs_code[2]:code_x[2]})
                                    #predict_code = self.getModel_code().predict([code_x[0],code_x[1],code_x[2]])
                        for h in range(len(predict_code)):
                            if predict_code[h][0]>0.5:
                                the_code = self.fitDataByRule(code_text[h])

                                #add code to entitys
                                list_entity.append(temp_entitys[h])

                                if the_code not in code_set:
                                    code_set.add(the_code)
                                    item['code'] = list(code_set)
                    for iter in re.finditer(self.PN_pattern,join_predict):
                        _name = self.fitDataByRule(pad_sentence[iter.span()[0]:iter.span()[1]])

                        #add name to entitys
                        _entity = Entity(doc_id=sentence.doc_id,entity_id="%s_%s_%s_%s"%(sentence.doc_id,sentence.sentence_index,iter.span()[0],iter.span()[1]),entity_text=_name,entity_type="name",sentence_index=sentence.sentence_index,begin_index=0,end_index=0,wordOffset_begin=iter.span()[0],wordOffset_end=iter.span()[1],in_attachment=sentence.in_attachment)
                        list_entity.append(_entity)
                        w = 1 if re.search('(项目|工程|招标|合同|标项|标的|计划|询价|询价单|询价通知书|申购)(名称|标题|主题)[:：\s]', pad_sentence[iter.span()[0]-10:iter.span()[0]])!=None else 0.5
                        if _name not in dict_name_freq_score:
                            # dict_name_freq_score[_name] = [1,len(re.findall(pattern_score,_name))+len(_name)*0.1]
                            dict_name_freq_score[_name] = [1, (len(re.findall(pattern_score, _name)) + len(_name) * 0.05)*w]
                        else:
                            dict_name_freq_score[_name][0] += 1
                    '''
                    for iter in re.finditer(self.PN_pattern,join_predict):
                        print("name-",self.fitDataByRule(pad_sentence[iter.span()[0]:iter.span()[1]]))
                    if item[1]['name']=="":
                        for iter in re.finditer(self.PN_pattern,join_predict):
                            #item[1]['name']=item[1]['name']+";"+self.fitDataByRule(pad_sentence[iter.span()[0]:iter.span()[1]])
                            item[1]['name']=self.fitDataByRule(pad_sentence[iter.span()[0]:iter.span()[1]])
                            break
                    '''
                if _begin_index+_LEN>=len(list_sentence):
                    break
                _begin_index += _LEN
            
            list_name_freq_score = []

            # 2020/11/23 大网站规则调整
            if len(dict_name_freq_score) == 0:
                name_re1 = '(项目|工程|招标|合同|标项|标的|计划|询价|询价单|询价通知书|申购)(名称|标题|主题)[:：\s]+([^，。：；]{2,60})[，。]'
                for sentence in list_sentence:
                    # pad_sentence = sentence.sentence_text
                    othername = re.search(name_re1, sentence.sentence_text)
                    if othername != None:
                        project_name = othername.group(3)
                        beg = find_index([project_name], sentence.sentence_text)[0]
                        end = beg + len(project_name)
                        _name = self.fitDataByRule(sentence.sentence_text[beg:end])
                        # add name to entitys
                        _entity = Entity(doc_id=sentence.doc_id, entity_id="%s_%s_%s_%s" % (
                        sentence.doc_id, sentence.sentence_index, beg, end), entity_text=_name,
                                         entity_type="name", sentence_index=sentence.sentence_index, begin_index=0,
                                         end_index=0, wordOffset_begin=beg, wordOffset_end=end,in_attachment=sentence.in_attachment)
                        list_entity.append(_entity)
                        w = 1
                        if _name not in dict_name_freq_score:
                            # dict_name_freq_score[_name] = [1,len(re.findall(pattern_score,_name))+len(_name)*0.1]
                            dict_name_freq_score[_name] = [1, (len(re.findall(pattern_score, _name)) + len(_name) * 0.05) * w]
                        else:
                            dict_name_freq_score[_name][0] += 1
                # othername = re.search(name_re1, sentence.sentence_text)
                # if othername != None:
                #     _name = othername.group(3)
                #     if _name not in dict_name_freq_score:
                #         dict_name_freq_score[_name] = [1, len(re.findall(pattern_score, _name)) + len(_name) * 0.1]
                #     else:
                #         dict_name_freq_score[_name][0] += 1

            for _name in dict_name_freq_score.keys():
                list_name_freq_score.append([_name,dict_name_freq_score[_name]])
            # print(list_name_freq_score)
            if len(list_name_freq_score)>0:
                list_name_freq_score.sort(key=lambda x:x[1][0]*x[1][1],reverse=True)
                item['name'] = list_name_freq_score[0][0]
                # if list_name_freq_score[0][1][0]>1:
                #     item[1]['name'] = list_name_freq_score[0][0]
                # else:
                #     list_name_freq_score.sort(key=lambda x:x[1][1],reverse=True)
                #     item[1]["name"] = list_name_freq_score[0][0]
                
            #下面代码加上去用正则添加某些识别不到的项目编号
            if item['code'] == []:
                for sentence in list_sentence:
                    # othercode = re.search('(采购计划编号|询价编号)[\)）]?[:：]?([\[\]a-zA-Z0-9\-]{5,30})', sentence.sentence_text)
                    # if othercode != None:
                    #     item[1]['code'].append(othercode.group(2))
                    # 2020/11/23 大网站规则调整
                    othercode = re.search('(项目|采购|招标|品目|询价|竞价|询价单|磋商|订单|账单|交易|文件|计划|场次|标的|标段|标包|分包|标段\(包\)|招标文件|合同|通知书|公告)(单号|编号|标号|编码|代码|备案号|号)[:：\s]+([^，。；：、]{8,30}[a-zA-Z0-9\号])[\)，。]', sentence.sentence_text)
                    if othercode != None:
                        item['code'].append(othercode.group(3))
            item['code'].sort(key=lambda x:len(x),reverse=True)
            result.append(item)

            list_sentence.sort(key=lambda x: x.sentence_index,reverse=False)
        return result
                        
                
    '''
    #当数据量过大时会报错
    def predict(self,articles,MAX_LEN = None):
        sentences = []
        for article in articles:
            for sentence in article.content.split("。"):
                sentences.append([sentence,article.id])
        if MAX_LEN is None:
            sent_len = [len(sentence[0]) for sentence in sentences]
            MAX_LEN = max(sent_len)
            #print(MAX_LEN)
           
        #若为空，则直接返回空
        result = [] 
        if MAX_LEN==0:
            for article in articles:
                result.append([article.id,{"code":[],"name":""}])
            return result
        
        index_unk = self.word2index.get("<unk>")
        index_pad = self.word2index.get("<pad>")
        
        x = [[self.word2index.get(word,index_unk)for word in sentence[0]]for sentence in sentences]
        x = pad_sequences(x,maxlen=MAX_LEN,padding="post",truncating="post")
        
        predict_y = self.getModel().predict(x)
        
        
        last_doc_id = ""
        item = []
        for sentence,predict in zip(sentences,np.argmax(predict_y,-1)):
            pad_sentence = sentence[0][:MAX_LEN]
            doc_id = sentence[1]
            join_predict = "".join([str(s) for s in predict])
            if doc_id!=last_doc_id:
                if last_doc_id!="":
                    result.append(item)
                item = [doc_id,{"code":[],"name":""}]
                code_set = set()
            code_x = []
            code_text = []
            for iter in re.finditer(self.PC_pattern,join_predict):
                get_len = 40
                if iter.span()[0]<get_len:
                    begin = 0
                else:
                    begin = iter.span()[0]-get_len
                end = iter.span()[1]+get_len
                code_x.append(embedding_word([pad_sentence[begin:iter.span()[0]],pad_sentence[iter.span()[0]:iter.span()[1]],pad_sentence[iter.span()[1]:end]],shape=(3,get_len,60)))
                code_text.append(pad_sentence[iter.span()[0]:iter.span()[1]])
            if len(code_x)>0:
                code_x = np.transpose(np.array(code_x),(1,0,2,3))
                predict_code = self.getModel_code().predict([code_x[0],code_x[1],code_x[2]])
                for h in range(len(predict_code)):
                    if predict_code[h][0]>0.5:
                        the_code = self.fitDataByRule(code_text[h])
                        if the_code not in code_set:
                            code_set.add(the_code)
                            item[1]['code'] = list(code_set)
            if item[1]['name']=="":
                for iter in re.finditer(self.PN_pattern,join_predict):
                    #item[1]['name']=item[1]['name']+";"+self.fitDataByRule(pad_sentence[iter.span()[0]:iter.span()[1]])
                    item[1]['name']=self.fitDataByRule(pad_sentence[iter.span()[0]:iter.span()[1]])
                    break
                
            last_doc_id = doc_id
        result.append(item)
        return result
    '''
        
#角色金额模型        
class PREMPredict():

    
    def __init__(self):
        #self.model_role_file = os.path.abspath("../role/models/model_role.model.hdf5")
        self.model_role_file = os.path.dirname(__file__)+"/../role/log/new_biLSTM-ep012-loss0.028-val_loss0.040-f10.954.h5"
        self.model_role = Model_role_classify_word()
        self.model_money = Model_money_classify()
        
        return
    
    def search_role_data(self,list_sentences,list_entitys):
        '''
        @summary:根据句子list和实体list查询角色模型的输入数据
        @param:
            list_sentences:文章的sentences
            list_entitys:文章的entitys
        @return:角色模型的输入数据
        '''
        text_list = []
        data_x = []
        points_entitys = []
        for list_entity,list_sentence in zip(list_entitys,list_sentences):

            list_entity.sort(key=lambda x:x.sentence_index)
            list_sentence.sort(key=lambda x:x.sentence_index)
            p_entitys = 0
            p_sentences = 0
            while(p_entitys<len(list_entity)):
                entity = list_entity[p_entitys]
                if entity.entity_type in ['org','company']:
                    while(p_sentences<len(list_sentence)):
                        sentence = list_sentence[p_sentences]
                        if entity.doc_id==sentence.doc_id and entity.sentence_index==sentence.sentence_index:
                            text_list.append(sentence.sentence_text[max(0, entity.wordOffset_begin-10):entity.wordOffset_end+10])
                            #item_x = embedding(spanWindow(tokens=sentence.tokens,begin_index=entity.begin_index,end_index=entity.end_index,size=settings.MODEL_ROLE_INPUT_SHAPE[1]),shape=settings.MODEL_ROLE_INPUT_SHAPE)
                            item_x = self.model_role.encode(tokens=sentence.tokens,begin_index=entity.begin_index,end_index=entity.end_index,entity_text=entity.entity_text)
                            data_x.append(item_x)
                            points_entitys.append(entity)
                            break
                        p_sentences += 1
                    
                p_entitys += 1
            
        if len(points_entitys)==0:
            return None
        
        return [data_x,points_entitys, text_list]
    
    
    def search_money_data(self,list_sentences,list_entitys):
        '''
        @summary:根据句子list和实体list查询金额模型的输入数据
        @param:
            list_sentences:文章的sentences
            list_entitys:文章的entitys
        @return:金额模型的输入数据
        '''
        text_list = []
        data_x = []
        points_entitys = []
        for list_entity,list_sentence in zip(list_entitys,list_sentences):

            list_entity.sort(key=lambda x:x.sentence_index)
            list_sentence.sort(key=lambda x:x.sentence_index)
            p_entitys = 0
    
            while(p_entitys<len(list_entity)):
                entity = list_entity[p_entitys]
                if entity.entity_type=="money":
                    p_sentences = 0
                    while(p_sentences<len(list_sentence)):
                        sentence = list_sentence[p_sentences]
                        if entity.doc_id==sentence.doc_id and entity.sentence_index==sentence.sentence_index:
                            text_list.append(sentence.sentence_text[max(0, entity.wordOffset_begin - 8):entity.wordOffset_end])
                            #item_x = embedding(spanWindow(tokens=sentence.tokens,begin_index=entity.begin_index,end_index=entity.end_index,size=settings.MODEL_MONEY_INPUT_SHAPE[1]),shape=settings.MODEL_MONEY_INPUT_SHAPE)
                            #item_x = embedding_word(spanWindow(tokens=sentence.tokens, begin_index=entity.begin_index, end_index=entity.end_index, size=10, center_include=True, word_flag=True),shape=settings.MODEL_MONEY_INPUT_SHAPE)
                            item_x = self.model_money.encode(tokens=sentence.tokens,begin_index=entity.begin_index,end_index=entity.end_index)
                            data_x.append(item_x)
                            points_entitys.append(entity)
                            break
                        p_sentences += 1
                p_entitys += 1
        
        if len(points_entitys)==0:
            return None
        
        return [data_x,points_entitys, text_list]
    
    def predict_role(self,list_sentences, list_entitys):
        datas = self.search_role_data(list_sentences, list_entitys)

        if datas is None:
            return
        points_entitys = datas[1]
        text_list = datas[2]


        if USE_PAI_EAS:
            _data = datas[0]
            _data = np.transpose(np.array(_data),(1,0,2))
            request = tf_predict_pb2.PredictRequest()
            request.inputs["input0"].dtype = tf_predict_pb2.DT_FLOAT
            request.inputs["input0"].array_shape.dim.extend(np.shape(_data[0]))
            request.inputs["input0"].float_val.extend(np.array(_data[0],dtype=np.float64).reshape(-1))
            request.inputs["input1"].dtype = tf_predict_pb2.DT_FLOAT
            request.inputs["input1"].array_shape.dim.extend(np.shape(_data[1]))
            request.inputs["input1"].float_val.extend(np.array(_data[1],dtype=np.float64).reshape(-1))
            request.inputs["input2"].dtype = tf_predict_pb2.DT_FLOAT
            request.inputs["input2"].array_shape.dim.extend(np.shape(_data[2]))
            request.inputs["input2"].float_val.extend(np.array(_data[2],dtype=np.float64).reshape(-1))
            request_data = request.SerializeToString()
            list_outputs = ["outputs"]
            _result = vpc_requests(role_url, role_authorization, request_data, list_outputs)
            if _result is not None:
                predict_y = _result["outputs"]
            else:
                predict_y = self.model_role.predict(datas[0])
        else:
            predict_y = self.model_role.predict(np.array(datas[0],dtype=np.float64))
        for i in range(len(predict_y)):
            entity = points_entitys[i]
            label = np.argmax(predict_y[i])
            values = predict_y[i]
            text = text_list[i]
            if label == 2:
                if re.search('中标单位和.{,25}签订合同', text):
                    label = 0
                    values[label] = 0.501
                elif re.search('尊敬的供应商：.{,25}我公司', text):
                    label = 0
                    values[label] = 0.801
            elif label == 1 and re.search('委托(单位|人|方)[是为：]+', text[:10]) and re.search('受委托(单位|人|方)[是为：]+', text[:10])==None:
                label = 0
                values[label] = 0.501
            elif label == 1 and re.search('([，。：]|^)(服务|中选)机构(名称)?', text[:-10]):
                label = 2
                values[label] = 0.501
            entity.set_Role(label, values)

    def predict_money(self,list_sentences,list_entitys):
        datas = self.search_money_data(list_sentences, list_entitys)
        if datas is None:
            return
        points_entitys = datas[1]
        _data = datas[0]
        text_list = datas[2]
        if USE_PAI_EAS:
            _data = np.transpose(np.array(_data),(1,0,2,3))
            request = tf_predict_pb2.PredictRequest()
            request.inputs["input0"].dtype = tf_predict_pb2.DT_FLOAT
            request.inputs["input0"].array_shape.dim.extend(np.shape(_data[0]))
            request.inputs["input0"].float_val.extend(np.array(_data[0],dtype=np.float64).reshape(-1))
            request.inputs["input1"].dtype = tf_predict_pb2.DT_FLOAT
            request.inputs["input1"].array_shape.dim.extend(np.shape(_data[1]))
            request.inputs["input1"].float_val.extend(np.array(_data[1],dtype=np.float64).reshape(-1))
            request.inputs["input2"].dtype = tf_predict_pb2.DT_FLOAT
            request.inputs["input2"].array_shape.dim.extend(np.shape(_data[2]))
            request.inputs["input2"].float_val.extend(np.array(_data[2],dtype=np.float64).reshape(-1))
            request_data = request.SerializeToString()
            list_outputs = ["outputs"]
            _result = vpc_requests(money_url, money_authorization, request_data, list_outputs)
            if _result is not None:
                predict_y = _result["outputs"]
            else:
                predict_y = self.model_money.predict(_data)
        else:
            predict_y = self.model_money.predict(_data)
        for i in range(len(predict_y)):
            entity = points_entitys[i]
            label = np.argmax(predict_y[i])
            values = predict_y[i]
            text = text_list[i]
            if label == 1 and re.search('[:：，。](总金额|总价|单价)', text):
                values[label] = 0.49
            elif label ==0 and entity.notes in ["投资", "工程造价"]:
                values[label] = 0.49
            entity.set_Money(label, values)

    def correct_money_by_rule(self, title, list_entitys, list_articles):
        if len(re.findall('监理|施工|设计|勘察', title)) == 1 and re.search('施工|总承包|epc|EPC', title) == None:
            keyword = re.search('监理|设计|勘察', title).group(0)
            for list_entity in list_entitys:
                for _entity in list_entity:
                    # print('keyword:',keyword, '_entity.notes :',_entity.notes)
                    if _entity.entity_type == "money" and _entity.notes == keyword and _entity.label == 2:
                        # if channel_dic['docchannel'] == "招标公告":
                        if re.search('中标|成交|中选|中价|中租|结果|入围', title + list_articles[0].content[:100]) == None:
                            _entity.values[0] = 0.51
                            _entity.set_Money(0, _entity.values)  # 2021/11/18 根据公告类别把费用改为招标或中投标金额
                        else:
                            _entity.values[1] = 0.51
                            _entity.set_Money(1, _entity.values)

    def predict(self,list_sentences,list_entitys):
        self.predict_role(list_sentences,list_entitys)
        self.predict_money(list_sentences,list_entitys)
        
        
#联系人模型    
class EPCPredict():
    
    def __init__(self):
        self.model_person = Model_person_classify()


    
    def search_person_data(self,list_sentences,list_entitys):
        '''
        @summary:根据句子list和实体list查询联系人模型的输入数据
        @param:
            list_sentences:文章的sentences
            list_entitys:文章的entitys
        @return:联系人模型的输入数据
        '''

        data_x = []
        points_entitys = []
        for list_entity,list_sentence in zip(list_entitys,list_sentences):
            
            p_entitys = 0
            dict_index_sentence = {}
            for _sentence in list_sentence:
                dict_index_sentence[_sentence.sentence_index] = _sentence

            _list_entity = [entity for entity in list_entity if entity.entity_type=="person"]
            while(p_entitys<len(_list_entity)):
                entity = _list_entity[p_entitys]
                if entity.entity_type=="person":
                    sentence = dict_index_sentence[entity.sentence_index]

                    item_x = self.model_person.encode(tokens=sentence.tokens,begin_index=entity.begin_index,end_index=entity.end_index)
                    data_x.append(item_x)
                    points_entitys.append(entity)

                p_entitys += 1

        if len(points_entitys)==0:
            return None
        
        # return [data_x,points_entitys,dianhua]
        return [data_x,points_entitys]

    def predict_person(self,list_sentences, list_entitys):
        datas = self.search_person_data(list_sentences, list_entitys)
        if datas is None:
            return
        points_entitys = datas[1]
        # phone = datas[2]
        if USE_PAI_EAS:
            _data = datas[0]
            _data = np.transpose(np.array(_data),(1,0,2,3))
            request = tf_predict_pb2.PredictRequest()
            request.inputs["input0"].dtype = tf_predict_pb2.DT_FLOAT
            request.inputs["input0"].array_shape.dim.extend(np.shape(_data[0]))
            request.inputs["input0"].float_val.extend(np.array(_data[0],dtype=np.float64).reshape(-1))
            request.inputs["input1"].dtype = tf_predict_pb2.DT_FLOAT
            request.inputs["input1"].array_shape.dim.extend(np.shape(_data[1]))
            request.inputs["input1"].float_val.extend(np.array(_data[1],dtype=np.float64).reshape(-1))
            request_data = request.SerializeToString()
            list_outputs = ["outputs"]
            _result = vpc_requests(person_url, person_authorization, request_data, list_outputs)
            if _result is not None:
                predict_y = _result["outputs"]
            else:
                predict_y = self.model_person.predict(datas[0])
        else:
            predict_y = self.model_person.predict(datas[0])
        # assert len(predict_y)==len(points_entitys)==len(phone)
        assert len(predict_y)==len(points_entitys)
        for i in range(len(predict_y)):
            entity = points_entitys[i]
            label = np.argmax(predict_y[i])
            values = []
            for item in predict_y[i]:
                values.append(item)
            # phone_number = phone[i]
            # entity.set_Person(label,values,phone_number)
            entity.set_Person(label,values,[])
        # 为联系人匹配电话
        # self.person_search_phone(list_sentences, list_entitys)

    def person_search_phone(self,list_sentences, list_entitys):
        def phoneFromList(phones):
            # for phone in phones:
            #     if len(phone)==11:
            #         return re.sub('电话[：|:]|联系方式[：|:]','',phone)
            return re.sub('电话[：|:]|联系方式[：|:]', '', phones[0])

        for list_entity, list_sentence in zip(list_entitys, list_sentences):
            # p_entitys = 0
            # p_sentences = 0
            #
            # key_word = re.compile('电话[：|:].{0,4}\d{7,12}|联系方式[：|:].{0,4}\d{7,12}')
            # # phone = re.compile('1[3|4|5|7|8][0-9][-—－]?\d{4}[-—－]?\d{4}|\d{3,4}[-—－]\d{7,8}/\d{3,8}|\d{3,4}[-—－]\d{7,8}转\d{1,4}|\d{3,4}[-—－]\d{7,8}|[\（|\(]0\d{2,3}[\）|\)]-?\d{7,8}-?\d{,4}')  # 联系电话
            # # 2020/11/25 增加发现的号码段
            # phone = re.compile('1[3|4|5|6|7|8|9][0-9][-—－]?\d{4}[-—－]?\d{4}|'
            #                    '\d{3,4}[-—－][1-9]\d{6,7}/\d{3,8}|'
            #                    '\d{3,4}[-—－]\d{7,8}转\d{1,4}|'
            #                    '\d{3,4}[-—－]?[1-9]\d{6,7}|'
            #                    '[\（|\(]0\d{2,3}[\）|\)]-?\d{7,8}-?\d{,4}|'
            #                    '[1-9]\d{6,7}')  # 联系电话
            # dict_index_sentence = {}
            # for _sentence in list_sentence:
            #     dict_index_sentence[_sentence.sentence_index] = _sentence
            #
            # dict_context_itemx = {}
            # last_person = "####****++++$$^"
            # last_person_phone = "####****++++$^"
            # _list_entity = [entity for entity in list_entity if entity.entity_type == "person"]
            # while (p_entitys < len(_list_entity)):
            #     entity = _list_entity[p_entitys]
            #     if entity.entity_type == "person" and entity.label in [1,2,3]:
            #         sentence = dict_index_sentence[entity.sentence_index]
            #         # item_x = embedding(spanWindow(tokens=sentence.tokens,begin_index=entity.begin_index,end_index=entity.end_index,size=settings.MODEL_PERSON_INPUT_SHAPE[1]),shape=settings.MODEL_PERSON_INPUT_SHAPE)
            #
            #         # s = spanWindow(tokens=sentence.tokens,begin_index=entity.begin_index,end_index=entity.end_index,size=20)
            #
            #         # 2021/5/8 取上下文的句子，解决表格处理的分句问题
            #         left_sentence = dict_index_sentence.get(entity.sentence_index - 1)
            #         left_sentence_tokens = left_sentence.tokens if left_sentence else []
            #         right_sentence = dict_index_sentence.get(entity.sentence_index + 1)
            #         right_sentence_tokens = right_sentence.tokens if right_sentence else []
            #         entity_beginIndex = entity.begin_index + len(left_sentence_tokens)
            #         entity_endIndex = entity.end_index + len(left_sentence_tokens)
            #         context_sentences_tokens = left_sentence_tokens + sentence.tokens + right_sentence_tokens
            #         s = spanWindow(tokens=context_sentences_tokens, begin_index=entity_beginIndex,
            #                        end_index=entity_endIndex, size=20)
            #
            #         _key = "".join(["".join(x) for x in s])
            #         if _key in dict_context_itemx:
            #             _dianhua = dict_context_itemx[_key][0]
            #         else:
            #             s1 = ''.join(s[1])
            #             # s1 = re.sub('，）', '-', s1)
            #             s1 = re.sub('\s', '', s1)
            #             have_key = re.findall(key_word, s1)
            #             have_phone = re.findall(phone, s1)
            #             s0 = ''.join(s[0])
            #             # s0 = re.sub('，）', '-', s0)
            #             s0 = re.sub('\s', '', s0)
            #             have_key2 = re.findall(key_word, s0)
            #             have_phone2 = re.findall(phone, s0)
            #
            #             s3 = ''.join(s[1])
            #             # s0 = re.sub('，）', '-', s0)
            #             s3 = re.sub(',|，|\s', '', s3)
            #             have_key3 = re.findall(key_word, s3)
            #             have_phone3 = re.findall(phone, s3)
            #
            #             s4 = ''.join(s[0])
            #             # s0 = re.sub('，）', '-', s0)
            #             s4 = re.sub(',|，|\s', '', s0)
            #             have_key4 = re.findall(key_word, s4)
            #             have_phone4 = re.findall(phone, s4)
            #
            #             _dianhua = ""
            #             if have_phone:
            #                 if entity.entity_text != last_person and s0.find(last_person) != -1 and s1.find(
            #                         last_person_phone) != -1:
            #                     if len(have_phone) > 1:
            #                         _dianhua = phoneFromList(have_phone[1:])
            #                 else:
            #                     _dianhua = phoneFromList(have_phone)
            #             elif have_key:
            #                 if entity.entity_text != last_person and s0.find(last_person) != -1 and s1.find(
            #                         last_person_phone) != -1:
            #                     if len(have_key) > 1:
            #                         _dianhua = phoneFromList(have_key[1:])
            #                 else:
            #                     _dianhua = phoneFromList(have_key)
            #             elif have_phone2:
            #                 if entity.entity_text != last_person and s0.find(last_person) != -1 and s0.find(
            #                         last_person_phone) != -1:
            #                     if len(have_phone2) > 1:
            #                         _dianhua = phoneFromList(have_phone2[1:])
            #                 else:
            #                     _dianhua = phoneFromList(have_phone2)
            #             elif have_key2:
            #                 if entity.entity_text != last_person and s0.find(last_person) != -1 and s0.find(
            #                         last_person_phone) != -1:
            #                     if len(have_key2) > 1:
            #                         _dianhua = phoneFromList(have_key2[1:])
            #                 else:
            #                     _dianhua = phoneFromList(have_key2)
            #             elif have_phone3:
            #                 if entity.entity_text != last_person and s4.find(last_person) != -1 and s3.find(
            #                         last_person_phone) != -1:
            #                     if len(have_phone3) > 1:
            #                         _dianhua = phoneFromList(have_phone3[1:])
            #                 else:
            #                     _dianhua = phoneFromList(have_phone3)
            #             elif have_key3:
            #                 if entity.entity_text != last_person and s4.find(last_person) != -1 and s3.find(
            #                         last_person_phone) != -1:
            #                     if len(have_key3) > 1:
            #                         _dianhua = phoneFromList(have_key3[1:])
            #                 else:
            #                     _dianhua = phoneFromList(have_key3)
            #             elif have_phone4:
            #                 if entity.entity_text != last_person and s4.find(last_person) != -1 and s4.find(
            #                         last_person_phone) != -1:
            #                     if len(have_phone4) > 1:
            #                         _dianhua = phoneFromList(have_phone4)
            #                 else:
            #                     _dianhua = phoneFromList(have_phone4)
            #             elif have_key4:
            #                 if entity.entity_text != last_person and s4.find(last_person) != -1 and s4.find(
            #                         last_person_phone) != -1:
            #                     if len(have_key4) > 1:
            #                         _dianhua = phoneFromList(have_key4)
            #                 else:
            #                     _dianhua = phoneFromList(have_key4)
            #             else:
            #                 _dianhua = ""
            #             # dict_context_itemx[_key] = [item_x, _dianhua]
            #             dict_context_itemx[_key] = [_dianhua]
            #         # points_entitys.append(entity)
            #         # dianhua.append(_dianhua)
            #         last_person = entity.entity_text
            #         if _dianhua:
            #             # 更新联系人entity联系方式（person_phone）
            #             entity.person_phone = _dianhua
            #             last_person_phone = _dianhua
            #         else:
            #             last_person_phone = "####****++++$^"
            #     p_entitys += 1

            from scipy.optimize import linear_sum_assignment
            from BiddingKG.dl.interface.Entitys import Match
            def dispatch(match_list):
                main_roles = list(set([match.main_role for match in match_list]))
                attributes = list(set([match.attribute for match in match_list]))

                label = np.zeros(shape=(len(main_roles), len(attributes)))
                for match in match_list:
                    main_role = match.main_role
                    attribute = match.attribute
                    value = match.value
                    label[main_roles.index(main_role), attributes.index(attribute)] = value + 10000
                # print(label)
                gragh = -label
                # km算法
                row, col = linear_sum_assignment(gragh)
                max_dispatch = [(i, j) for i, j, value in zip(row, col, gragh[row, col]) if value]
                return [Match(main_roles[row], attributes[col]) for row, col in max_dispatch]
            # km算法
            key_word = re.compile('((?:电话|联系方式|联系人).{0,4}?)(\d{7,12})')
            phone = re.compile('1[3|4|5|6|7|8|9][0-9][-—－―]?\d{4}[-—－―]?\d{4}|'
                               '\+86.?1[3|4|5|6|7|8|9]\d{9}|'
                               '0\d{2,3}[-—－―][1-9]\d{6,7}/[1-9]\d{6,10}|'
                               '0\d{2,3}[-—－―]\d{7,8}转\d{1,4}|'
                               '0\d{2,3}[-—－―]?[1-9]\d{6,7}|'
                               '[\（|\(]0\d{2,3}[\）|\)]-?\d{7,8}-?\d{,4}|'
                               '[1-9]\d{6,7}')
            phone_entitys = []
            for _sentence in list_sentence:
                sentence_text = _sentence.sentence_text
                res_set = set()
                for i in re.finditer(phone,sentence_text):
                    res_set.add((i.group(),i.start(),i.end()))
                for i in re.finditer(key_word,sentence_text):
                    res_set.add((i.group(2),i.start()+len(i.group(1)),i.end()))
                for item in list(res_set):
                    phone_left = sentence_text[max(0,item[1]-10):item[1]]
                    phone_right = sentence_text[item[2]:item[2]+8]
                    # 排除传真号 和 其它错误项
                    if re.search("传，?真|信，?箱|邮，?箱",phone_left):
                        if not re.search("电，?话",phone_left):
                            continue
                    if re.search("帐，?号|编，?号|报，?价|证，?号|价，?格|[\(（]万?元[\)）]",phone_left):
                        continue
                    if re.search("[.,]\d{2,}",phone_right):
                        continue
                    _entity = Entity(_sentence.doc_id, None, item[0], "phone", _sentence.sentence_index, None, None,item[1], item[2],in_attachment=_sentence.in_attachment)
                    phone_entitys.append(_entity)
            person_entitys = []
            for entity in list_entity:
                if entity.entity_type == "person":
                    entity.person_phone = ""
                    person_entitys.append(entity)
            _list_entity = phone_entitys + person_entitys
            _list_entity = sorted(_list_entity,key=lambda x:(x.sentence_index,x.wordOffset_begin))

            words_num_dict = dict()
            last_words_num = 0
            list_sentence = sorted(list_sentence, key=lambda x: x.sentence_index)
            for sentence in list_sentence:
                _index = sentence.sentence_index
                if _index == 0:
                    words_num_dict[_index] = 0
                else:
                    words_num_dict[_index] = words_num_dict[_index - 1] + last_words_num
                last_words_num = len(sentence.sentence_text)
            match_list = []
            for index in range(len(_list_entity)):
                entity = _list_entity[index]
                if entity.entity_type=="person" and entity.label in [1,2,3]:
                    match_nums = 0
                    for after_index in range(index + 1, min(len(_list_entity), index + 5)):
                        after_entity = _list_entity[after_index]
                        if after_entity.entity_type=="phone":
                            sentence_distance = after_entity.sentence_index - entity.sentence_index
                            distance = (words_num_dict[after_entity.sentence_index] + after_entity.wordOffset_begin) - (
                                    words_num_dict[entity.sentence_index] + entity.wordOffset_end)
                            if sentence_distance < 2 and distance < 50:
                                value = (-1 / 2 * (distance ** 2)) / 10000
                                match_list.append(Match(entity, after_entity, value))
                                match_nums += 1
                            else:
                                break
                        if after_entity.entity_type=="person":
                            if after_entity.label not in [1,2,3]:
                                break
                    if not match_nums:
                        for previous_index in range(index-1, max(0,index-5), -1):
                            previous_entity = _list_entity[previous_index]
                            if previous_entity.entity_type == "phone":
                                sentence_distance = entity.sentence_index - previous_entity.sentence_index
                                distance = (words_num_dict[entity.sentence_index] + entity.wordOffset_begin) - (
                                        words_num_dict[previous_entity.sentence_index] + previous_entity.wordOffset_end)
                                if sentence_distance < 1 and distance<30:
                                    # 前向 没有 /10000
                                    value = (-1 / 2 * (distance ** 2))
                                    match_list.append(Match(entity, previous_entity, value))
                                else:
                                    break

            result = dispatch(match_list)
            for match in result:
                entity = match.main_role
                # 更新 list_entity
                entity_index = list_entity.index(entity)
                list_entity[entity_index].person_phone = match.attribute.entity_text


    def predict(self,list_sentences,list_entitys):
        self.predict_person(list_sentences,list_entitys)
            
#表格预测
class FormPredictor():
    
    def __init__(self,lazyLoad=getLazyLoad()):
        self.model_file_line = os.path.dirname(__file__)+"/../form/model/model_form.model_line.hdf5"
        self.model_file_item = os.path.dirname(__file__)+"/../form/model/model_form.model_item.hdf5"
        self.model_form_item = Model_form_item()
        self.model_form_context = Model_form_context()
        self.model_dict = {"line":[None,self.model_file_line]}
        
        
    def getModel(self,type):
        if type=="item":
            return self.model_form_item
        elif type=="context":
            return self.model_form_context
        else:
            return self.getModel(type)

    def encode(self,data,**kwargs):
        return encodeInput([data], word_len=50, word_flag=True,userFool=False)[0]
        return encodeInput_form(data)

    
    def predict(self,form_datas,type):
        if type=="item":
            return self.model_form_item.predict(form_datas)
        elif type=="context":
            return self.model_form_context.predict(form_datas)
        else:
            return self.getModel(type).predict(form_datas)

    

#角色规则
#依据正则给所有无角色的实体赋予角色，给予等于阈值的最低概率
class RoleRulePredictor():
    
    def __init__(self):
        # (?P<tenderee_left_w1> 正则组名 后面的 w1 为概率权重关键词
        self.pattern_tenderee_left = "(?P<tenderee_left>((遴选|寻源|采购|招标|竞价|议价|比选|委托|询比?价|比价|评选|谈判|邀标|邀请|洽谈|约谈|选取|抽取|抽选|项目|需求|最终|建设|业主|甲|转让|招租|议标|合同主体|挂牌|出租|出让|买受|出售|标卖|处置)" \
                                    "(人|方|单位|组织|用户|业主|主体|部门|公司)|文章来源|委托机构|产权所有人|需求?方|买方|业主|权属人|甲方当事人|询价书企业|比选发起人|结算单位)"\
                                    "[）)]?(信息|联系方式|概况)?[,，:：]?([(（](1|2|1.1|1.2)[）)])?((公司|单位)?名称)?([(（](全称|盖章)[）)])?(是|为|：|:|\s*)+$)"
        self.pattern_tenderee_left_w0 = "(?P<tenderee_left>(，|。|^)(项目)?((遴选|寻源|采购|招标|竞价|议价|比选|委托|询比?价|比价|评选|谈判|邀标|邀请|洽谈|约谈|选取|抽取|抽选|项目|需求|最终|建设|业主|甲|转让|招租|议标|合同主体|挂牌|出租|出让|买受|出售|标卖|处置)" \
                                        "(人|方|单位|组织|用户|业主|主体|部门|公司)|文章来源|委托机构|产权所有人|需求?方|买方|业主|权属人|甲方当事人|询价书企业|比选发起人|结算单位)"\
                                        "[）)]?(信息|联系方式|概况)?[,，。:：]?([(（]?(1|2|1.1|1.2)[）)]?)?((公司|单位)?名称)?([(（](全称|盖章)[）)])?(是|为|：|:|，|\s*)+$)"
        self.pattern_tenderee_left_w1 = "(?P<tenderee_left_w1>(，|。|^)(项目)?((遴选|寻源|采购|招标|竞价|议价|比选|委托|询比?价|比价|评选|谈判|邀标|邀请|洽谈|约谈|选取|抽取|抽选)" \
                                     "(人|公司|单位|组织|用户|业主|主体|方|部门))" \
                                     "(是|为|：|:|\s*)+$)"
        self.pattern_tenderee_center = "(?P<tenderee_center>(受.{5,20}委托|现将[\w()（）]{5,20}[\d年月季度至]+采购意向))"
        self.pattern_tenderee_right = "(?P<tenderee_right>^([(（](以下简称)?[，\"“]*(招标|采购)(人|单位|机构)[，\"”]*[)）]|^委托|^将于[\d年月日，:：]+进行|^现委托|^的\w{2,10}正在进行|[\d年月季度至]+采购意向))"  #|(^[^.。，,:：](采购|竞价|招标|施工|监理|中标|物资)(公告|公示|项目|结果|招标))|的.*正在进行询比价)
        self.pattern_tendereeORagency_right = "(?P<tendereeORagency_right>(^拟对|^现?就|^现对))"
        self.pattern_agency_left = "(?P<agency_left>(代理(?:人|机构|公司|单位|组织)|专业采购机构|集中采购机构|招标组织机构|集采机构|[招议)）]+标机构)(名称)?(.{,4}名，?称|全称|是|为|：|:|[,，]?\s*)$|(受.{5,20}委托，?$))"
        self.pattern_agency_right = "(?P<agency_right>^([(（](以下简称)?[，\"“]*(代理)(人|单位|机构)[，\"”]*[)）])|^受.{5,20}委托|^受委?托，)"  # |^受托  会与 受托生产等冲突，代理表达一般会在后面有逗号
        # 2020//11/24 大网站规则 中标关键词添加 选定单位|指定的中介服务机构
        self.pattern_winTenderer_left = "(?P<winTenderer_left>(乙|承做|施工|供货|承包|承建|承租|竞得|受让|签约)(候选)?(人|单位|机构|供应商|方|公司|厂商|商)[：:是为]+$|" \
                                        "(选定单位|指定的中介服务机构|实施主体|承制单位|供方)[：:是为]+$|((评审结果|名次|排名|中标结果)[:：]*第?[一1]名?)[：:是为]+$|" \
                                        "单一来源(采购)?(供应商|供货商|服务商|方式向)$|((中标|成交)(结果|信息))[：:是为]+$|(供应|供货|承销|服务|实施)(机构|单位|商|方)(名称)?[：:是为]+$)"
        self.pattern_winTenderer_left_w0 = "(?P<winTenderer_left_w1>(，|。|^)((中标|中选|中价|成交)(候选)?(人|单位|机构|供应商|客户|方|公司|厂商|商)|第?[一1]名)(名称)?[,，]?([(（]按综合排名排序[)）])?[：:,，]$)" #解决表头识别不到加逗号情况，需前面为，。空
        self.pattern_winTenderer_left_w1 = "(?P<winTenderer_left_w1>(中标|中选|中价|成交)(候选)?(人|单位|机构|供应商|客户|方|公司|厂商|商)(名称)?([(（]按综合排名排序[)）])?[：:是为]+$)" #取消逗号 并拒绝执行改进计划的供应商，华新水泥将可能终止与其合作关系
        # self.pattern_winTenderer_center = "(?P<winTenderer_center>第[一1].{,20}[是为]((中标|中选|中价|成交|施工)(人|单位|机构|供应商|公司)|供应商)[：:是为])"
        # self.pattern_winTenderer_right = "(?P<winTenderer_right>(^[是为\(]((采购(供应商|供货商|服务商)|(第[一1]|预)?(拟?(中标|中选|中价|成交)(候选)?(人|单位|机构|供应商|公司|厂商)))))|^(报价|价格)最低，确定为本项目成交供应商)"
        self.pattern_winTenderer_right = "(?P<winTenderer_right>(^[是为]((采购|中标)(供应商|供货商|服务商)|(第[一1]|预)?(拟?(中标|中选|中价|成交)(候选)?(人|单位|机构|供应商|公司|厂商)))|" \
                                        "^(报价|价格)最低，确定为本项目成交供应商|^：贵公司参与|^：?你方于|^中标。|^成为[\w、()（）]+项目的成交供应商|^[（(]中标人名称[）)]))"
        self.pattern_winTenderer_whole = "(?P<winTenderer_center>贵公司.{,15}以.{,15}中标|最终由.{,15}竞买成功|经.{,15}决定[以由].{,15}公司中标|(谈判结果：|确定)由.{5,20}(向我单位)?供货)|中标通知书.{,15}你方"   # 2020//11/24 大网站规则 中标关键词添加 谈判结果：由.{5,20}供货

        # self.pattern_winTenderer_location = "(中标|中选|中价|乙|成交|承做|施工|供货|承包|竞得|受让)(候选)?(人|单位|机构|供应商|方|公司|厂商|商)|(供应商|供货商|服务商)[：:]?$|(第[一1](名|((中标|中选|中价|成交)?(候选)?(人|单位|机构|供应商))))(是|为|：|:|\s*$)|((评审结果|名次|排名)[:：]第?[一1]名?)|(单一来源(采购)?方式向.?$)"

        self.pattern_secondTenderer_left = "(?P<secondTenderer_left>((第[二2](名|((中标|中选|中价|成交)(候选)?(人|单位|机构|供应商|公司))))(名称)?[：:是为]+$)|((评审结果|名次|排名)[:：]第?[二2]名?，?投标商名称[:：]+$))"
        self.pattern_secondTenderer_right = "(?P<secondTenderer_right>^[是为\(]第[二2](名|(中标|中选|中价|成交)(候选)?(人|单位|机构|供应商|公司)))"
        
        self.pattern_thirdTenderer_left = "(?P<thirdTenderer_left>(第[三3](名|((中标|中选|中价|成交)(候选)?(人|单位|机构|供应商|公司))))(名称)?[：:是为]+$|((评审结果|名次|排名)[:：]第?[三3]名?，?投标商名称[:：]+$))"
        self.pattern_thirdTenderer_right = "(?P<thirdTenderer_right>^[是为\(]第[三3](名|(中标|中选|中价|成交)(候选)?(人|单位|机构|供应商|公司)))"

        self.pattern_whole = [self.pattern_tenderee_left_w1,
                              self.pattern_tenderee_left,
                              self.pattern_tenderee_left_w0,
                              self.pattern_tenderee_center,
                              self.pattern_tenderee_right,
                              self.pattern_tendereeORagency_right,
                              self.pattern_agency_left,
                              self.pattern_agency_right,
                              self.pattern_winTenderer_left_w1,
                              self.pattern_winTenderer_left,
                              self.pattern_winTenderer_left_w0,
                              self.pattern_winTenderer_whole,
                              self.pattern_winTenderer_right,
                              self.pattern_secondTenderer_left,
                              self.pattern_secondTenderer_right,
                              self.pattern_thirdTenderer_left,
                              self.pattern_thirdTenderer_right
                              ]  # 需按顺序排列， 第二、三中标要在中标正则后面

        self.SET_NOT_TENDERER = set(["人民政府","人民法院","中华人民共和国","人民检察院","评标委员会","中国政府","中国海关","中华人民共和国政府"])
        
        self.pattern_money_tenderee = re.compile("投标最高限价|采购计划金额|项目预算|招标金额|采购金额|项目金额|建安费用|采购(单位|人)委托价|限价|拦标价|预算金额")
        self.pattern_money_tenderer = re.compile("((合同|成交|中标|应付款|交易|投标|验收|订单)[）\)]?(总?金额|结果|[单报]?价))|总价|标的基本情况")
        self.pattern_money_tenderer_whole = re.compile("(以金额.*中标)|中标供应商.*单价|以.*元中标")
        self.pattern_money_other = re.compile("代理费|服务费")
        self.pattern_pack = "(([^承](包|标[段号的包]|分?包|包组)编?号?|项目)[:：]?[\(（]?[0-9A-Za-z一二三四五六七八九十]{1,4})[^至]?|(第?[0-9A-Za-z一二三四五六七八九十]{1,4}(包号|标[段号的包]|分?包))|[0-9]个(包|标[段号的包]|分?包|包组)"
        
    def _check_input(self,text, ignore=False):
        if not text:
            return []
        
        if not isinstance(text, list):
            text = [text]
        
        null_index = [i for i, t in enumerate(text) if not t]
        if null_index and not ignore:
            raise Exception("null text in input ")
        
        return text


    def predict(self, list_articles, list_sentences, list_entitys, list_codenames, on_value=0.5):

        for article, list_entity, list_sentence, list_codename in zip(list_articles, list_entitys, list_sentences,
                                                                      list_codenames):
            list_sentence.sort(key=lambda x: x.sentence_index)  # 2022/1/5 按句子顺序排序
            # list_name = list_codename["name"]
            list_name = []  # 2022/1/5  改为实体列表内所有项目名称
            for entity in list_entity:
                if entity.entity_type == 'name':
                    list_name.append(entity.entity_text)
            list_name = self._check_input(list_name) + [article.title]
            for p_entity in list_entity:

                if p_entity.entity_type in ["org", "company"]:
                    # 只解析角色为无的或者概率低于阈值的
                    if p_entity.label is None:
                        continue
                    # 将上下文包含标题的实体概率置为0.6，因为标题中的实体不一定是招标人
                    if str(p_entity.label) == "0":
                        find_flag = False
                        for _sentence in list_sentence:
                            if _sentence.sentence_index == p_entity.sentence_index:
                                _span = spanWindow(tokens=_sentence.tokens, begin_index=p_entity.begin_index,
                                                   end_index=p_entity.end_index, size=20, center_include=True,
                                                   word_flag=True, use_text=True,
                                                   text=re.sub("）", ")", re.sub("（", "(", p_entity.entity_text)))
                                for _name in list_name:
                                    if _name != "" and str(_span[1] + _span[2][:len(str(_name))]).find(_name) >= 0:
                                        find_flag = True
                                        if p_entity.values[0] > on_value:
                                            p_entity.values[0] = 0.6 + (p_entity.values[0] - 0.6) / 10
                                        else:
                                            p_entity.values[0] = on_value  # 2022/03/08 修正类似 223985179 公司在文章开头的项目名称概率又没达到0.5的情况
                        if find_flag:
                            continue

                    # 正则从概率低于阈值或其他类别中召回角色
                    role_prob = float(p_entity.values[int(p_entity.label)])
                    if role_prob < on_value or str(p_entity.label) == "5":
                        # 将标题中的实体置为招标人
                        _list_name = self._check_input(list_name, ignore=True)
                        find_flag = False
                        for _name in _list_name:  # 2022/1/5修正只要项目名称出现过的角色，所有位置都标注为招标人
                            if str(_name).find(re.sub("）", ")", re.sub("（", "(",
                                                                       p_entity.entity_text))) >= 0 and p_entity.sentence_index < 4:
                                for _sentence in list_sentence:
                                    if _sentence.sentence_index == p_entity.sentence_index:
                                        _span = spanWindow(tokens=_sentence.tokens, begin_index=p_entity.begin_index,
                                                           end_index=p_entity.end_index, size=20, center_include=True,
                                                           word_flag=True, use_text=True, text=re.sub("）", ")",
                                                                                                      re.sub("（", "(",
                                                                                                             p_entity.entity_text)))
                                        if str(_span[1] + _span[2][:len(str(_name))]).find(
                                                _name) >= 0:
                                            find_flag = True
                                            _label = 0
                                            p_entity.label = _label
                                            p_entity.values[int(_label)] = on_value
                                            break
                                    if p_entity.sentence_index >= 4:
                                        break
                            if find_flag:
                                break
                            # if str(_name).find(p_entity.entity_text)>=0:
                            #     find_flag = True
                            #     _label = 0
                            #     p_entity.label = _label
                            #     p_entity.values[int(_label)] = on_value
                            #     break
                        # 若是实体在标题中，默认为招标人，不进行以下的规则匹配
                        if find_flag:
                            continue

                        for s_index in range(len(list_sentence)):
                            if p_entity.doc_id == list_sentence[s_index].doc_id and p_entity.sentence_index == \
                                    list_sentence[s_index].sentence_index:
                                tokens = list_sentence[s_index].tokens
                                begin_index = p_entity.begin_index
                                end_index = p_entity.end_index
                                size = 15
                                spans = spanWindow(tokens, begin_index, end_index, size, center_include=True,
                                                   word_flag=True, use_text=False)
                                # _flag = False

                                # 添加中标通知书类型特殊处理
                                try:
                                    if s_index == 0 and re.search('中标通知书.{,30}[，：]%s：'%p_entity.entity_text.replace('(', '').replace(')', ''),
                                                                  list_sentence[s_index].sentence_text.replace('(', '').replace(')', '')[:100]):
                                        p_entity.label = 2
                                        p_entity.values[2] = 0.5
                                        # log('正则召回实体: %s, %s, %s, %d, %.4f, %s'%(_group,  _v_group, p_entity.entity_text, p_entity.label, p_entity.values[p_entity.label], list_spans[_i_span]))
                                        break
                                except Exception as e:
                                    print('正则报错:', e)

                                # 使用正则+距离解决冲突
                                # 2021/6/11update center: spans[1] --> spans[0][-30:]+spans[1]
                                list_spans = [spans[0][-30:], spans[0][-10:] + spans[1] + spans[2][:25], spans[2]] # 实体左、中、右 信息
                                for _i_span in range(len(list_spans)):
                                    _flag = False
                                    _prob_weight = 1

                                    # print(list_spans[_i_span],p_entity.entity_text)
                                    for _pattern in self.pattern_whole:
                                        for _iter in re.finditer(_pattern, list_spans[_i_span]):
                                            for _group, _v_group in _iter.groupdict().items():
                                                if _v_group is not None and _v_group != "":
                                                    _role = _group.split("_")[0]
                                                    if _role == "tendereeORagency":   # 2022/3/9 新增不确定招标代理判断逻辑
                                                        # print('p_entity_sentenceindex:', p_entity.sentence_index)
                                                        if p_entity.sentence_index>=1:  # 只在第一句进行这种模糊匹配
                                                            continue
                                                        if re.search('医院|学校|大学|中学|小学|幼儿园|政府|部|委员会|署|行|局|厅|处|室|科|股|站', p_entity.entity_text)\
                                                            or re.search('(采购|招标|投标|交易|代理|拍卖|咨询|顾问|管理)', p_entity.entity_text) == None:
                                                            _role = 'tenderee'
                                                        else:
                                                            _role = "agency"
                                                    _direct = _group.split("_")[1]
                                                    _weight = _group.split("_")[2] if len(_group.split("_"))==3 else ""
                                                    # _label = {"tenderee": 0, "agency": 1, "winTenderer": 2,
                                                    #           "secondTenderer": 3, "thirdTenderer": 4}.get(_role)
                                                    if _i_span == 0 and _direct == "left" and re.search('各.{,5}供应商|尊敬的供应商|业绩|拟招|(交易|采购|招标)服务(单位|机构)',  #135463002 拟招一家供应商为宜宾市第三人民医院、李庄同济医院院区提供消防维保服务
                                                                                                        list_spans[0]) == None:  # 2021/12/22 修正错误中标召回 例子208668937
                                                        _flag = True
                                                        _label = {"tenderee": 0, "agency": 1, "winTenderer": 2,
                                                                  "secondTenderer": 3, "thirdTenderer": 4}.get(_role)
                                                        _prob_weight = 1.2 if _weight=='w1' else 1
                                                        # print('_v_group:',_group, _v_group, p_entity.entity_text)

                                                    if _i_span == 1 and _direct == "center":
                                                        _flag = True
                                                        _label = {"tenderee": 0, "agency": 1, "winTenderer": 2,
                                                                  "secondTenderer": 3, "thirdTenderer": 4}.get(_role)
                                                        _prob_weight = 1.2 if _weight == 'w1' else 1
                                                        # print('_v_group:', _group, _v_group, p_entity.entity_text)

                                                    if _i_span == 2 and _direct == "right":
                                                        _flag = True
                                                        _label = {"tenderee": 0, "agency": 1, "winTenderer": 2,
                                                                  "secondTenderer": 3, "thirdTenderer": 4}.get(_role)
                                                        _prob_weight = 1.2 if _weight == 'w1' else 1
                                                        # print('_v_group:', _group, _v_group, p_entity.entity_text)

                                    # 得到结果
                                    if _flag:
                                        p_entity.label = _label
                                        p_entity.values[int(_label)] = on_value*_prob_weight + p_entity.values[int(_label)] / 10
                                        # log('正则召回实体: %s, %s, %s, %d, %.4f, %s'%(_group,  _v_group, p_entity.entity_text, p_entity.label, p_entity.values[p_entity.label], list_spans[_i_span]))
                                        break

                # 其他金额通过正则召回可能是招标或中投标的金额
                if p_entity.entity_type in ["money"]:
                    if str(p_entity.label) == "2":
                        for _sentence in list_sentence:
                            if _sentence.sentence_index == p_entity.sentence_index:
                                _span = spanWindow(tokens=_sentence.tokens, begin_index=p_entity.begin_index,
                                                   end_index=p_entity.end_index, size=20, center_include=True,
                                                   word_flag=True, text=p_entity.entity_text)
                                if re.search(self.pattern_money_tenderee, _span[0]) is not None and re.search(
                                        self.pattern_money_other, _span[0]) is None:
                                    p_entity.values[0] = 0.8 + p_entity.values[0] / 10
                                    p_entity.label = 0
                                if re.search(self.pattern_money_tenderer, _span[0]) is not None:
                                    if re.search(self.pattern_money_other, _span[0]) is not None:
                                        if re.search(self.pattern_money_tenderer, _span[0]).span()[1] > \
                                                re.search(self.pattern_money_other, _span[0]).span()[1]:
                                            p_entity.values[1] = 0.8 + p_entity.values[1] / 10
                                            p_entity.label = 1
                                    else:
                                        p_entity.values[1] = 0.8 + p_entity.values[1] / 10
                                        p_entity.label = 1
                                if re.search(self.pattern_money_tenderer_whole,
                                             "".join(_span)) is not None and re.search(self.pattern_money_other,
                                                                                       _span[0]) is None:
                                    p_entity.values[1] = 0.8 + p_entity.values[1] / 10
                                    p_entity.label = 1

            # 增加招标金额扩展，招标金额+连续的未识别金额，并且都可以匹配到标段信息，则将为识别的金额设置为招标金额
            list_p = []
            state = 0
            for p_entity in list_entity:
                for _sentence in list_sentence:
                    if _sentence.sentence_index == p_entity.sentence_index:
                        _span = spanWindow(tokens=_sentence.tokens, begin_index=p_entity.begin_index,
                                           end_index=p_entity.end_index, size=20, center_include=True, word_flag=True,
                                           text=p_entity.entity_text)

                        if state == 2:
                            for _p in list_p[1:]:
                                _p.values[0] = 0.8 + _p.values[0] / 10
                                _p.label = 0
                            state = 0
                            list_p = []

                        if state == 0:
                            if p_entity.entity_type in ["money"]:
                                if str(p_entity.label) == "0" and re.search(self.pattern_pack,
                                                                            _span[0] + "-" + _span[2]) is not None:
                                    state = 1
                                    list_p.append(p_entity)
                        elif state == 1:
                            if p_entity.entity_type in ["money"]:
                                if str(p_entity.label) in ["0", "2"] and re.search(self.pattern_pack,
                                                                                   _span[0] + "-" + _span[
                                                                                       2]) is not None and re.search(
                                        self.pattern_money_other,
                                        _span[0] + "-" + _span[2]) is None and p_entity.sentence_index == list_p[
                                    0].sentence_index:
                                    list_p.append(p_entity)
                                else:
                                    state = 2

            if len(list_p) > 1:
                for _p in list_p[1:]:
                    # print("==",_p.entity_text,_p.sentence_index,_p.label)
                    _p.values[0] = 0.8 + _p.values[0] / 10
                    _p.label = 0
                state = 0
                list_p = []

            for p_entity in list_entity:
                # 将属于集合中的不可能是中标人的标签置为无
                if p_entity.entity_text in self.SET_NOT_TENDERER:
                    p_entity.label = 5

'''正则补充最后一句实体日期格式为招标或代理 2021/12/30'''
class RoleRuleFinalAdd():
    def predict(self, list_articles,list_sentences, list_entitys, list_codenames):
        # text_end = list_articles[0].content.split('##attachment##')[0][-40:]
        main_sentences = [sentence for sentence in list_sentences[0] if not sentence.in_attachment]
        end_tokens = []
        for sentence in main_sentences[-5:]:
            end_tokens.extend(sentence.tokens)
        text_end = "".join(end_tokens[-30:])
        # print(text_end)
        # sear_ent = re.search('[，。]([\u4e00-\u9fa5()（）]{5,20})，?\s*[.]{2,4}年.{1,2}月.{1,2}日', text_end)
        sear_ent = re.search('[，。；]([\u4e00-\u9fa5()（）]{5,20}(，?[\u4e00-\u9fa5]{,6}(分公司|部))?)，?\s*[0-9零一二三四五六七八九十〇]{2,4}[年\-/][0-9零一二三四五六七八九十]{1,2}[月\-/][0-9零一二三四五六七八九十]{1,2}日?', text_end)
        sear_ent2 = re.search('(户名|开户名称)[:：]([\u4e00-\u9fa5()（）]{5,20})[，。]', list_articles[0].content[:5000])
        sear_ent3 = re.search('(报名咨询|[收送交]货地点)[，：]([\u4e00-\u9fa5()（）]{5,20})[0-9\-]*[，。]', list_articles[0].content[:5000])
        sear_ent4 = re.search('(发布(?:人|单位|机构|企业)|项目业主)[，:：]([\u4e00-\u9fa5()（）]{5,20})[，。]', list_articles[0].content[:5000])
        sear_list = [sear_ent4 , sear_ent3 , sear_ent2 , sear_ent]

        tenderee_notfound = True
        agency_notfound = True
        tenderee_list = []
        ents = []
        for ent in list_entitys[0]:
            if ent.entity_type in ['org', 'company']:
                if ent.label == 0:
                    tenderee_list.append(ent.entity_text)
                    tenderee_notfound = False
                elif ent.label == 1:
                    agency_notfound = False
                elif ent.label == 5:
                    ents.append(ent)
        if sear_ent or sear_ent2 or sear_ent3 or sear_ent4:
            for _sear_ent in [_sear for _sear in sear_list if _sear]:
                # if sear_ent4:
                #     ent_re = sear_ent4.group(2)
                # elif sear_ent3:
                #     ent_re = sear_ent3.group(2)
                # elif sear_ent2:
                #     ent_re = sear_ent2.group(2)
                # else:
                #     ent_re = sear_ent.group(1)
                if _sear_ent==sear_ent4:
                    ent_re = _sear_ent.group(2)
                elif _sear_ent==sear_ent3:
                    ent_re = _sear_ent.group(2)
                elif _sear_ent==sear_ent2:
                    ent_re = _sear_ent.group(2)
                else:
                    ent_re = _sear_ent.group(1)
                # print('ent_re', ent_re)
                ent_re = ent_re.replace('，', '').replace("(","（").replace(")","）")

                if tenderee_notfound == True and (re.search('医院|学校|大学|中学|小学|幼儿园|政府|部|委员会|署|行|局|厅|处|室|科|股|站', ent_re)
                                                  or re.search('(采购|招标|投标|交易|代理|拍卖|咨询|顾问|管理)', ent_re) == None):
                    n = 0
                    for i in range(len(ents) - 1, -1, -1):
                        if not ents[i].in_attachment:
                            n += 1
                        if n > 3 and _sear_ent==sear_ent: # 文章末尾角色加日期这种只找后三个实体
                            break
                        if ents[i].entity_text == ent_re or (ents[i].entity_text in ent_re and len(ents[i].entity_text)/len(ent_re)>0.6):
                            ents[i].label = 0
                            ents[i].values[0] = 0.5
                            tenderee_notfound = False
                            # log('正则最后补充实体： %s'%(ent_re))
                            break
                elif agency_notfound == True and re.search('(采购|招标|投标|交易|代理|拍卖|咨询|顾问|管理)', ent_re) and ent_re not in tenderee_list:
                    n = 0
                    for i in range(len(ents) - 1, -1, -1):
                        if not ents[i].in_attachment:
                            n += 1
                        if n > 3 and _sear_ent==sear_ent:  # 文章末尾角色加日期这种只找后三个实体
                            break
                        if ents[i].entity_text == ent_re or (ents[i].entity_text in ent_re and len(ents[i].entity_text)/len(ent_re)>0.6):
                            ents[i].label = 1
                            ents[i].values[1] = 0.5
                            agency_notfound = False
                            # log('正则最后补充实体： %s'%(ent_re))
                            break
                if not tenderee_notfound:
                    break

        elif list_codenames[0]['name'] != "":  #把标题包含的公司实体作为招标人
            # tenderee_notfound = True
            # ents = []
            # for ent in list_entitys[0]:
            #     if ent.entity_type in ['org', 'company']:
            #         if ent.label == 0:
            #             tenderee_notfound = False
            #         elif ent.label == 1:
            #             agency_notfound = False
            #         elif ent.label == 5:
            #             ents.append(ent)
            if tenderee_notfound == True:
                # print('list_codenames',list_codenames[0]['name'])
                for ent in ents:
                    if ent.entity_text in list_codenames[0]['name']:
                        ent.label = 0
                        ent.values[0] = 0.5
                        # log('正则召回标题中包含的实体:%s'%ent.entity_text)
                        break

# 招标人角色召回规则
class TendereeRuleRecall():
    def __init__(self):
        self.tenderee_left = re.compile("(发布(人|单位|机构)|需求方(信息[，：])?(单位|公司)?名称|购买主体|收货单位|项目申请单位|发起组织|联系单位|"
                                        "询价(机构|企业)|联系(人|方式)，?(单位|公司)(名称)?|联系(人|方式)，名称)[:：][^。；，]{,5}$")

        self.tenderee_right = re.compile("^[^。；：:]{,5}[(（](以?下简?称)?，?[，\"“]*[我本][\u4e00-\u9fa5]{1,2}[，\"”]*[)）]|"
                                         "^[\(（][^。；：:\)）]{,5}称(?:招标|采购)(?:人|单位)|"
                                        "^[^。；：:]{,10}[对就][^。；，]+，?[^。；，]{,20}进行[^。；，]*(采购|询比?价|遴选|招投?标|征集)|"
                                         "^[^。；：:]{,10}关于[^。；，]+，?[^。；，]{,20}的[^。；，]{,20}公告|"
                                         "^[^。；，：:]{,10}的[^。；，]+，?[^。；，]{,20}正在[^。；，]{,5}进行|"
                                         "^[^。；，：:]{,10}的[^。；，]+，?[^。，；]{,20}已?[^。；，]{,20}批准|"
                                         "^[^。；，：:]{,15}(选定|选取|征集|遴选)[^。；，]{,20}(供应商|(代理|咨询|设计)[^。；，]{,5}机构|代理人)")
        self.tenderee_right2 = re.compile("^[^。；，：:]{,10}(招标办|采购部|办事处|采购小?组)")
        self.tenderee_right3 = re.compile("^[^。；，：:]{,10}(对|就|关于|的)(?P<project>[^。；，？！：:]+)")
        # 公告主语判断规则
        self.subject = re.compile("[我本][院校局]")
        # 未识别实体召回正则
        self.unrecognized1 = re.compile("(?P<tenderee_left>((遴选|采购|招标|竞价|议价|比选|委托|询比?价|评选|谈判|邀标|邀请|洽谈|约谈)" \
                                        "(人|商|公司|单位|组织|用户|业主|主体|方|部门))" \
                                        "(信息[，：]?)?((公司|单位)?名称)?([(（](全称|盖章)[）)])?(是|为|：|:)+)(?P<unrecognized>[^，。：:；]+)[，。；：:]")
        self.unrecognized2 = re.compile("(?P<tenderee_left>((项目|需求|最终|建设|业主|转让|招租|甲|议标|合同主体|挂牌|出租|出让|买受|选取|抽取|抽选|出售|标卖|比价|处置)" \
                                "(人|公司|单位|组织|用户|业主|主体|方|部门)|文章来源|委托机构|产权所有人|需求?方|买方|业主|(业主|采购人|招标人)联系方式[，：]公司名称：|权属人|甲方当事人|询价书企业|比选发起人|项目单位[，：]单位名称|结算单位)"\
                                "[）)]?(信息[，：])?((公司|单位)?名称)?([(（](全称|盖章)[）)])?(是|为|：|:)+)(?P<unrecognized>[^，。：:；]+)[，。；：:]")
        # 未识别实体尾部判断
        self.unrecognized_end1 = re.compile(
            "^[\u4e00-\u9fa5]{2,}?(?:公司|医院|学校|学院|大学|中学|小学|幼儿园|政府|指挥部|办公室|项目部|业主大会|监狱|教育局|委员会|研究所|招标办|采购部|办事处|水利局|公墓|中心|联合社|合作社)")
        self.unrecognized_end2 = re.compile("^[\u4e00-\u9fa5]{4,}(?:署|局|厅|处|室|科|部|站|所|股|行|园)")

    def predict(self, list_articles,list_sentences, list_entitys, list_codenames):
        # tenderee_notfound = True
        # agency_notfound = True
        self.get_tenderee = False
        ents = []
        list_name = []
        for ent in list_entitys[0]:
            if ent.entity_type == 'name':
                list_name.append(ent.entity_text)
            if ent.entity_type in ['org', 'company']:
                if ent.label == 0:
                    # tenderee_notfound = False
                    self.get_tenderee = True
                # elif ent.label == 1:
                #     agency_notfound = False
                elif ent.label == 5:
                    ents.append(ent)
        if not self.get_tenderee:
            self.entity_context_rule(ents,list_name,list_sentences)
        if not self.get_tenderee:
            self.subject_rule(ents,list_articles,list_sentences)
        if not self.get_tenderee:
            self.unrecognized_entity_rule(self.unrecognized1,list_sentences,list_entitys,0.55)
        if not self.get_tenderee:
            self.unrecognized_entity_rule(self.unrecognized2,list_sentences,list_entitys,0.5)

    #entity上下文正则判断
    def entity_context_rule(self,entitys,list_name,list_sentences):
        for ent in entitys:
            _sentence = list_sentences[0][ent.sentence_index]
            _span = spanWindow(tokens=_sentence.tokens, begin_index=ent.begin_index,
                               end_index=ent.end_index, size=40, center_include=True,
                               word_flag=True, use_text=True,
                               text=re.sub("）", ")", re.sub("（", "(", ent.entity_text)))
            if re.search(self.tenderee_left,_span[0]):
                ent.label = 0
                ent.values[0] = 0.5 + ent.values[0] / 10
                self.get_tenderee = True
            elif re.search(self.tenderee_right,_span[2]):
                ent.label = 0
                ent.values[0] = 0.5 + ent.values[0] / 10
                self.get_tenderee = True
            elif re.search(self.tenderee_right2, _span[2]):
                ent.label = 0
                ent.values[0] = 0.5 + ent.values[0] / 10
                self.get_tenderee = True
            elif list_name:
                pj_name = re.search(self.tenderee_right3, _span[2])
                if pj_name:
                    pj_name = pj_name.groupdict()["project"]
                    for _name in list_name:
                        if _name in pj_name:
                            ent.label = 0
                            ent.values[0] = 0.5
                            self.get_tenderee = True
                            break
    # 公告主语判断
    def subject_rule(self, entitys,list_articles,list_sentences):
        content = list_articles[0].content.split('##attachment##')[0]
        if re.search(self.subject,content):
            _subject = re.search(self.subject,content).group()
            for ent in entitys:
                if re.search("院",_subject) and re.search("医院|学院",ent.entity_text):
                    ent.label = 0
                    ent.values[0] = 0.5 + ent.values[0] / 10
                    self.get_tenderee = True
                elif re.search("校",_subject) and re.search("学校|学院|大学|高中|初中|中学|小学",ent.entity_text):
                    ent.label = 0
                    ent.values[0] = 0.5 + ent.values[0] / 10
                    self.get_tenderee = True
                elif re.search("局", _subject) and re.search("局", ent.entity_text):
                    _sentence = list_sentences[0][ent.sentence_index]
                    _span = spanWindow(tokens=_sentence.tokens, begin_index=ent.begin_index,
                                       end_index=ent.end_index, size=20, center_include=True,
                                       word_flag=True, use_text=True,
                                       text=re.sub("）", ")", re.sub("（", "(", ent.entity_text)))
                    if not re.search("监督|投诉",_span[0][-10:]):
                        ent.label = 0
                        ent.values[0] = 0.5 + ent.values[0] / 10
                        self.get_tenderee = True

    # 正则召回未识别实体
    def unrecognized_entity_rule(self,pattern,list_sentences,list_entitys,on_value=0.5):
        list_sentence = list_sentences[0]
        for in_attachment in [False,True]:
            for sentence in [sentence for sentence in list_sentence if sentence.in_attachment==in_attachment]:
                sentence_text = sentence.sentence_text
                tokens = sentence.tokens
                doc_id = sentence.doc_id
                in_attachment = sentence.in_attachment
                list_tokenbegin = []
                begin = 0
                for i in range(0, len(tokens)):
                    list_tokenbegin.append(begin)
                    begin += len(str(tokens[i]))
                list_tokenbegin.append(begin + 1)
                for _match in re.finditer(pattern,sentence_text):
                    _groupdict = _match.groupdict()
                    _match_text = _match.group()
                    _unrecognized_text = _groupdict["unrecognized"]
                    # print(_unrecognized_text)
                    # if _match_text[-1] in [':','：']:
                    #     _unrecognized = re.search(self.unrecognized_end1,_unrecognized_text)
                    #     if not _unrecognized:
                    #         _unrecognized = re.search(self.unrecognized_end2, _unrecognized_text)
                    #     if _unrecognized:
                    #         _unrecognized = _unrecognized.group()
                    #     else:
                    #         continue
                    # else:
                    #     _unrecognized = _unrecognized_text
                    _unrecognized = re.search(self.unrecognized_end1,_unrecognized_text)
                    if not _unrecognized:
                        _unrecognized = re.search(self.unrecognized_end2, _unrecognized_text)
                    if _unrecognized:
                        _unrecognized = _unrecognized.group()
                    else:
                        continue
                    # print(_unrecognized)
                    if re.search("某",_unrecognized) or len(_unrecognized)>15:
                        continue
                    begin_index_temp = _match.start()+len(_groupdict['tenderee_left'])
                    for j in range(len(list_tokenbegin)):
                        if list_tokenbegin[j] == begin_index_temp:
                            begin_index = j
                            break
                        elif list_tokenbegin[j] > begin_index_temp:
                            begin_index = j - 1
                            break
                    index = begin_index_temp + len(_unrecognized)
                    end_index_temp = index
                    for j in range(begin_index, len(list_tokenbegin)):
                        if list_tokenbegin[j] >= index:
                            end_index = j - 1
                            break
                    entity_id = "%s_%d_%d_%d" % (doc_id, sentence.sentence_index, begin_index, end_index)
                    entity_text = _unrecognized
                    new_entity = Entity(doc_id, entity_id, entity_text, 'company', sentence.sentence_index, begin_index, end_index,
                               begin_index_temp, end_index_temp, in_attachment=in_attachment)
                    new_entity.label = 0
                    new_entity.values = [on_value,0,0,0,0,0]
                    list_entitys[0].append(new_entity)
                    self.get_tenderee = True
            if self.get_tenderee:
                list_entitys[0] = sorted(list_entitys[0], key=lambda x: (x.sentence_index, x.begin_index))
                break

# 时间类别
class TimePredictor():
    def __init__(self):
        self.sess = tf.Session(graph=tf.Graph())
        self.inputs_code = None
        self.outputs_code = None
        self.input_shape = (2,40,128)
        self.load_model()

    def load_model(self):
        model_path = os.path.dirname(__file__)+'/timesplit_model'
        if self.inputs_code is None:
            log("get model of time")
            with self.sess.as_default():
                with self.sess.graph.as_default():
                    meta_graph_def = tf.saved_model.loader.load(self.sess, tags=["serve"], export_dir=model_path)
                    signature_key = tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
                    signature_def = meta_graph_def.signature_def
                    self.inputs_code = []
                    self.inputs_code.append(
                        self.sess.graph.get_tensor_by_name(signature_def[signature_key].inputs["input0"].name))
                    self.inputs_code.append(
                        self.sess.graph.get_tensor_by_name(signature_def[signature_key].inputs["input1"].name))
                    self.outputs_code = self.sess.graph.get_tensor_by_name(signature_def[signature_key].outputs["outputs"].name)
                    return self.inputs_code, self.outputs_code
        else:
            return self.inputs_code, self.outputs_code

    def search_time_data(self,list_sentences,list_entitys):
        data_x = []
        points_entitys = []
        for list_sentence, list_entity in zip(list_sentences, list_entitys):
            p_entitys = 0
            p_sentences = 0
            list_sentence.sort(key=lambda x: x.sentence_index)
            while(p_entitys<len(list_entity)):
                entity = list_entity[p_entitys]
                if entity.entity_type in ['time']:
                    while(p_sentences<len(list_sentence)):
                        sentence = list_sentence[p_sentences]
                        if entity.doc_id == sentence.doc_id and entity.sentence_index == sentence.sentence_index:
                            # left = sentence.sentence_text[max(0,entity.wordOffset_begin-self.input_shape[1]):entity.wordOffset_begin]
                            # right = sentence.sentence_text[entity.wordOffset_end:entity.wordOffset_end+self.input_shape[1]]
                            s = spanWindow(tokens=sentence.tokens,begin_index=entity.begin_index,end_index=entity.end_index,size=self.input_shape[1])
                            left = s[0]
                            right = s[1]
                            context = [left, right]
                            x = self.embedding_words(context, shape=self.input_shape)
                            data_x.append(x)
                            points_entitys.append(entity)
                            break
                        p_sentences += 1
                p_entitys += 1
        if len(points_entitys)==0:
            return None
        data_x = np.transpose(np.array(data_x), (1, 0, 2, 3))
        return [data_x, points_entitys]

    def embedding_words(self, datas, shape):
        '''
        @summary:查找词汇对应的词向量
        @param:
            datas:词汇的list
            shape:结果的shape
        @return: array,返回对应shape的词嵌入
        '''
        model_w2v = getModel_w2v()
        embed = np.zeros(shape)
        length = shape[1]
        out_index = 0
        for data in datas:
            index = 0
            for item in data:
                item_not_space = re.sub("\s*", "", item)
                if index >= length:
                    break
                if item_not_space in model_w2v.vocab:
                    embed[out_index][index] = model_w2v[item_not_space]
                    index += 1
                else:
                    embed[out_index][index] = model_w2v['unk']
                    index += 1
            out_index += 1
        return embed

    def predict(self, list_sentences,list_entitys):
        datas = self.search_time_data(list_sentences, list_entitys)
        if datas is None:
            return
        points_entitys = datas[1]
        with self.sess.as_default():
            predict_y = limitRun(self.sess,[self.outputs_code], feed_dict={self.inputs_code[0]:datas[0][0]
                ,self.inputs_code[1]:datas[0][1]})[0]
            for i in range(len(predict_y)):
                entity = points_entitys[i]
                label = np.argmax(predict_y[i])
                values = []
                for item in predict_y[i]:
                    values.append(item)
                if label != 0:
                    if not timeFormat(entity.entity_text):
                        label = 0
                        values[0] = 0.5
                entity.set_Role(label, values)

# 产品字段提取
class ProductPredictor():
    def __init__(self):
        vocabpath = os.path.dirname(__file__) + "/codename_vocab.pk"
        self.vocab = load(vocabpath)
        self.word2index = dict((w, i) for i, w in enumerate(np.array(self.vocab)))
        self.sess = tf.Session(graph=tf.Graph())
        self.load_model()

    def load_model(self):
        # model_path = os.path.dirname(__file__)+'/product_savedmodel/product.pb'
        model_path = os.path.dirname(__file__)+'/product_savedmodel/productAndfailreason.pb'
        with self.sess.as_default():
            with self.sess.graph.as_default():
                output_graph_def = tf.GraphDef()
                with open(model_path, 'rb') as f:
                    output_graph_def.ParseFromString(f.read())
                    tf.import_graph_def(output_graph_def, name='')
                    self.sess.run(tf.global_variables_initializer())
                    self.char_input = self.sess.graph.get_tensor_by_name('CharInputs:0')
                    self.length = self.sess.graph.get_tensor_by_name("Sum:0")
                    self.dropout = self.sess.graph.get_tensor_by_name("Dropout:0")
                    self.logit = self.sess.graph.get_tensor_by_name("logits/Reshape:0")
                    self.tran = self.sess.graph.get_tensor_by_name("crf_loss/transitions:0")

    def decode(self,logits, lengths, matrix):
        paths = []
        small = -1000.0
        # start = np.asarray([[small] * 4 + [0]])
        start = np.asarray([[small]*7+[0]])
        for score, length in zip(logits, lengths):
            score = score[:length]
            pad = small * np.ones([length, 1])
            logits = np.concatenate([score, pad], axis=1)
            logits = np.concatenate([start, logits], axis=0)
            path, _ = viterbi_decode(logits, matrix)
            paths.append(path[1:])
        return paths

    def predict(self, list_sentences,list_entitys=None,list_articles=[], fail=False, MAX_AREA=5000):
        '''
        预测实体代码，每个句子最多取MAX_AREA个字，超过截断
        :param list_sentences: 多篇公告句子列表,[[一篇公告句子列表],[公告句子列表]]
        :param list_entitys: 多篇公告实体列表
        :param MAX_AREA: 每个句子最多截取多少字
        :return: 把预测出来的实体放进实体类
        '''
        with self.sess.as_default() as sess:
            with self.sess.graph.as_default():
                result = []
                product_list = []
                if fail and list_articles!=[]:
                    text_list = [list_articles[0].content[:MAX_AREA]]
                    chars = [[self.word2index.get(it, self.word2index.get('<unk>')) for it in text] for text in text_list]
                    if USE_API:
                        requests_result = requests.post(API_URL + "/predict_product",
                                               json={"inputs": chars}, verify=True)
                        batch_paths = json.loads(requests_result.text)['result']
                        lengths = json.loads(requests_result.text)['lengths']
                    else:
                        lengths, scores, tran_ = sess.run([self.length, self.logit, self.tran],
                                                          feed_dict={
                                                              self.char_input: np.asarray(chars),
                                                              self.dropout: 1.0
                                                          })
                        batch_paths = self.decode(scores, lengths, tran_)
                    for text, path, length in zip(text_list, batch_paths, lengths):
                        tags = ''.join([str(it) for it in path[:length]])
                        # 提取产品
                        for it in re.finditer("12*3", tags):
                            start = it.start()
                            end = it.end()
                            _entity = Entity(doc_id=list_articles[0].doc_id, entity_id="%s_%s_%s_%s" % (
                                list_articles[0].doc_id, 0, start, end),
                                             entity_text=text[start:end],
                                             entity_type="product", sentence_index=0,
                                             begin_index=0, end_index=0, wordOffset_begin=start,
                                             wordOffset_end=end)
                            list_entitys[0].append(_entity)
                            product_list.append(text[start:end])
                        # 提取失败原因
                        for it in re.finditer("45*6", tags):
                            start = it.start()
                            end = it.end()
                            result.append(text[start:end].replace('？', '').strip())
                    reasons = []
                    for it in result:
                        if "(√)" in it or "（√）" in it:
                            reasons = [it]
                            break
                        if reasons != [] and (it not in reasons[-1] and it not in reasons):
                            reasons.append(it)
                        elif reasons == []:
                            reasons.append(it)
                    return {'fail_reason':'；'.join(reasons)}, product_list

                if list_entitys is None:
                    list_entitys = [[] for _ in range(len(list_sentences))]
                for list_sentence, list_entity in zip(list_sentences,list_entitys):
                    if len(list_sentence)==0:
                        result.append({"product":[]})
                        continue
                    list_sentence.sort(key=lambda x:len(x.sentence_text), reverse=True)
                    _begin_index = 0
                    item = {"product":[]}
                    temp_list = []
                    while True:
                        MAX_LEN = len(list_sentence[_begin_index].sentence_text)
                        if MAX_LEN > MAX_AREA:
                            MAX_LEN = MAX_AREA
                        _LEN = MAX_AREA//MAX_LEN
                        chars = [sentence.sentence_text[:MAX_LEN] for sentence in list_sentence[_begin_index:_begin_index+_LEN]]
                        chars = [[self.word2index.get(it, self.word2index.get('<unk>')) for it in l] for l in chars]
                        chars = pad_sequences(chars, maxlen=MAX_LEN, padding="post", truncating="post")
                        if USE_API:
                            requests_result = requests.post(API_URL + "/predict_product",
                                                   json={"inputs": chars.tolist()}, verify=True)
                            batch_paths = json.loads(requests_result.text)['result']
                            lengths = json.loads(requests_result.text)['lengths']
                        else:
                            lengths, scores, tran_ = sess.run([self.length, self.logit, self.tran],
                                                              feed_dict={
                                                                        self.char_input: np.asarray(chars),
                                                                        self.dropout: 1.0
                                                                        })
                            batch_paths = self.decode(scores, lengths, tran_)
                        for sentence, path, length in zip(list_sentence[_begin_index:_begin_index+_LEN],batch_paths, lengths):
                            tags = ''.join([str(it) for it in path[:length]])
                            for it in re.finditer("12*3", tags):
                                start = it.start()
                                end = it.end()
                                _entity = Entity(doc_id=sentence.doc_id, entity_id="%s_%s_%s_%s" % (
                                sentence.doc_id, sentence.sentence_index, start, end),
                                                 entity_text=sentence.sentence_text[start:end],
                                                 entity_type="product", sentence_index=sentence.sentence_index,
                                                 begin_index=0, end_index=0, wordOffset_begin=start,
                                                 wordOffset_end=end,in_attachment=sentence.in_attachment)
                                list_entity.append(_entity)
                                temp_list.append(sentence.sentence_text[start:end])
                                product_list.append(sentence.sentence_text[start:end])

                        # item["product"] = list(set(temp_list))
                        # result.append(item)
                        if _begin_index+_LEN >= len(list_sentence):
                            break
                        _begin_index += _LEN
                    item["product"] = list(set(temp_list))
                    result.append(item) # 修正bug
                return {'fail_reason': ""},product_list


# 产品数量单价品牌规格提取 #2021/11/10 添加表格中的项目、需求、预算、时间要素提取
class ProductAttributesPredictor():
    def __init__(self,):
        self.p1 = '(设备|货物|商品|产品|物品|货品|材料|物资|物料|物件|耗材|备件|食材|食品|品目|标的|标的物|标项|资产|拍卖物|仪器|器材|器械|药械|药品|药材|采购品?|项目|招标|工程|服务)[\)）]?(名称|内容|描述)'
        self.p2 = '设备|货物|商品|产品|物品|货品|材料|物资|物料|物件|耗材|备件|食材|食品|品目|标的|标的物|资产|拍卖物|仪器|器材|器械|药械|药品|药材|采购品|项目|品名|菜名|内容|名称'
        with open(os.path.dirname(__file__)+'/header_set.pkl', 'rb') as f:
            self.header_set = pickle.load(f)
    def isTrueTable(self, table):
        '''真假表格规则：
        1、包含<caption>或<th>标签为真
        2、包含大量链接、表单、图片或嵌套表格为假
        3、表格尺寸太小为假
        4、外层<table>嵌套子<table>,一般子为真，外为假'''
        if table.find_all(['caption', 'th']) != []:
            return True
        elif len(table.find_all(['form', 'a', 'img'])) > 5:
            return False
        elif len(table.find_all(['tr'])) < 2:
            return False
        elif len(table.find_all(['table'])) >= 1:
            return False
        else:
            return True

    def getTrs(self, tbody):
        # 获取所有的tr
        trs = []
        objs = tbody.find_all(recursive=False)
        for obj in objs:
            if obj.name == "tr":
                trs.append(obj)
            if obj.name == "tbody":
                for tr in obj.find_all("tr", recursive=False):
                    trs.append(tr)
        return trs

    def getTable(self, tbody):
        trs = self.getTrs(tbody)
        inner_table = []
        if len(trs) < 2:
            return inner_table
        for tr in trs:
            tr_line = []
            tds = tr.findChildren(['td', 'th'], recursive=False)
            if len(tds) < 2:
                continue
            for td in tds:
                td_text = re.sub('\s', '', td.get_text())
                tr_line.append(td_text)
            inner_table.append(tr_line)
        return inner_table

    def fixSpan(self, tbody):
        # 处理colspan, rowspan信息补全问题
        trs = self.getTrs(tbody)
        ths_len = 0
        ths = list()
        trs_set = set()
        # 修改为先进行列补全再进行行补全，否则可能会出现表格解析混乱
        # 遍历每一个tr

        for indtr, tr in enumerate(trs):
            ths_tmp = tr.findChildren('th', recursive=False)
            # 不补全含有表格的tr
            if len(tr.findChildren('table')) > 0:
                continue
            if len(ths_tmp) > 0:
                ths_len = ths_len + len(ths_tmp)
                for th in ths_tmp:
                    ths.append(th)
                trs_set.add(tr)
            # 遍历每行中的element
            tds = tr.findChildren(recursive=False)
            if len(tds) < 3:
                continue  # 列数太少的不补全
            for indtd, td in enumerate(tds):
                # 若有colspan 则补全同一行下一个位置
                if 'colspan' in td.attrs and str(re.sub("[^0-9]", "", str(td['colspan']))) != "":
                    col = int(re.sub("[^0-9]", "", str(td['colspan'])))
                    if col < 10 and len(td.get_text()) < 500:
                        td['colspan'] = 1
                        for i in range(1, col, 1):
                            td.insert_after(copy.copy(td))
        for indtr, tr in enumerate(trs):
            ths_tmp = tr.findChildren('th', recursive=False)
            # 不补全含有表格的tr
            if len(tr.findChildren('table')) > 0:
                continue
            if len(ths_tmp) > 0:
                ths_len = ths_len + len(ths_tmp)
                for th in ths_tmp:
                    ths.append(th)
                trs_set.add(tr)
            # 遍历每行中的element
            tds = tr.findChildren(recursive=False)
            same_span = 0
            if len(tds) > 1 and 'rowspan' in tds[0].attrs:
                span0 = tds[0].attrs['rowspan']
                for td in tds:
                    if 'rowspan' in td.attrs and td.attrs['rowspan'] == span0:
                        same_span += 1
            if same_span == len(tds):
                continue

            for indtd, td in enumerate(tds):
                # 若有rowspan 则补全下一行同样位置
                if 'rowspan' in td.attrs and str(re.sub("[^0-9]", "", str(td['rowspan']))) != "":
                    row = int(re.sub("[^0-9]", "", str(td['rowspan'])))
                    td['rowspan'] = 1
                    for i in range(1, row, 1):
                        # 获取下一行的所有td， 在对应的位置插入
                        if indtr + i < len(trs):
                            tds1 = trs[indtr + i].findChildren(['td', 'th'], recursive=False)
                            if len(tds1) >= (indtd) and len(tds1) > 0:
                                if indtd > 0:
                                    tds1[indtd - 1].insert_after(copy.copy(td))
                                else:
                                    tds1[0].insert_before(copy.copy(td))
                            elif len(tds1) > 0 and len(tds1) == indtd - 1:
                                tds1[indtd - 2].insert_after(copy.copy(td))

    def get_monthlen(self, year, month):
        '''输入年份、月份 int类型 得到该月份天数'''
        try:
            weekday, num = calendar.monthrange(int(year), int(month))
        except:
            num = 30
        return str(num)
    def fix_time(self, text, html, page_time):
        '''输入日期字段返回格式化日期'''
        for it in [('十二', '12'),('十一', '11'),('十','10'),('九','9'),('八','8'),('七','7'),
                   ('六','6'),('五','5'),('四','4'),('三','3'),('二','2'),('一','1')]:
            if it[0] in text:
                text = text.replace(it[0], it[1])
        if re.search('^\d{1,2}月$', text):
            m = re.search('^(\d{1,2})月$', text).group(1)
            if len(m) < 2:
                m = '0' + m
            year = re.search('(\d{4})年(.{,12}采购意向)?', html)
            if year:
                y = year.group(1)
                num = self.get_monthlen(y, m)
                if len(num) < 2:
                    num = '0' + num
                order_begin = "%s-%s-01" % (y, m)
                order_end = "%s-%s-%s" % (y, m, num)
            elif page_time != "":
                year = re.search('\d{4}', page_time)
                if year:
                    y = year.group(0)
                    num = self.get_monthlen(y, m)
                    if len(num) < 2:
                        num = '0' + num
                    order_begin = "%s-%s-01" % (y, m)
                    order_end = "%s-%s-%s" % (y, m, num)
                else:
                    y = str(datetime.datetime.now().year)
                    num = self.get_monthlen(y, m)
                    if len(num) < 2:
                        num = '0' + num
                    order_begin = "%s-%s-01" % (y, m)
                    order_end = "%s-%s-%s" % (y, m, num)
            else:
                y = str(datetime.datetime.now().year)
                num = self.get_monthlen(y, m)
                if len(num) < 2:
                    num = '0' + num
                order_begin = "%s-%s-01" % (y, m)
                order_end = "%s-%s-%s" % (y, m, num)
            return order_begin, order_end

        t1 = re.search('^(\d{4})(年|/|\.|-)(\d{1,2})月?$', text)
        if t1:
            year = t1.group(1)
            month = t1.group(3)
            num = self.get_monthlen(year, month)
            if len(month)<2:
                month = '0'+month
            if len(num) < 2:
                num = '0'+num
            order_begin = "%s-%s-01" % (year, month)
            order_end = "%s-%s-%s" % (year, month, num)
            return order_begin, order_end
        t2 = re.search('^(\d{4})(年|/|\.|-)(\d{1,2})(月|/|\.|-)(\d{1,2})日?$', text)
        if t2:
            y = t2.group(1)
            m = t2.group(3)
            d = t2.group(5)
            m = '0'+ m if len(m)<2 else m
            d = '0'+d if len(d)<2 else d
            order_begin = order_end = "%s-%s-%s"%(y,m,d)
            return order_begin, order_end
        # 时间样式："202105"
        t3 = re.search("^(20\d{2})(\d{1,2})$",text)
        if t3:
            year = t3.group(1)
            month = t3.group(2)
            if int(month)>0 and int(month)<=12:
                num = self.get_monthlen(year, month)
                if len(month) < 2:
                    month = '0' + month
                if len(num) < 2:
                    num = '0' + num
                order_begin = "%s-%s-01" % (year, month)
                order_end = "%s-%s-%s" % (year, month, num)
                return order_begin, order_end
        # 时间样式："20210510"
        t4 = re.search("^(20\d{2})(\d{2})(\d{2})$", text)
        if t4:
            year = t4.group(1)
            month = t4.group(2)
            day = t4.group(3)
            if int(month) > 0 and int(month) <= 12 and int(day)>0 and int(day)<=31:
                order_begin = order_end = "%s-%s-%s"%(year,month,day)
                return order_begin, order_end
        all_match = re.finditer('^(?P<y1>\d{4})(年|/|\.)(?P<m1>\d{1,2})(?:(月|/|\.)(?:(?P<d1>\d{1,2})日)?)?'
                                '(到|至|-)(?:(?P<y2>\d{4})(年|/|\.))?(?P<m2>\d{1,2})(?:(月|/|\.)'
                                '(?:(?P<d2>\d{1,2})日)?)?$', text)
        y1 = m1 = d1 = y2 = m2 = d2 = ""
        found_math = False
        for _match in all_match:
            if len(_match.group()) > 0:
                found_math = True
                for k, v in _match.groupdict().items():
                    if v!="" and v is not None:
                        if k == 'y1':
                            y1 = v
                        elif k == 'm1':
                            m1 = v
                        elif k == 'd1':
                            d1 = v
                        elif k == 'y2':
                            y2 = v
                        elif k == 'm2':
                            m2 = v
                        elif k == 'd2':
                            d2 = v
        if not found_math:
            return "", ""
        y2 = y1 if y2 == "" else y2
        d1 = '1' if d1 == "" else d1
        d2 = self.get_monthlen(y2, m2) if d2 == "" else d2
        m1 = '0' + m1 if len(m1) < 2 else m1
        m2 = '0' + m2 if len(m2) < 2 else m2
        d1 = '0' + d1 if len(d1) < 2 else d1
        d2 = '0' + d2 if len(d2) < 2 else d2
        order_begin = "%s-%s-%s"%(y1,m1,d1)
        order_end = "%s-%s-%s"%(y2,m2,d2)
        return order_begin, order_end

    def find_header(self, items, p1, p2):
        '''
        inner_table 每行正则检查是否为表头，是则返回表头所在列序号，及表头内容
        :param items: 列表，内容为每个td 文本内容
        :param p1: 优先表头正则
        :param p2: 第二表头正则
        :return: 表头所在列序号，是否表头，表头内容
        '''
        flag = False
        header_dic = {'名称': '', '数量': '', '单价': '', '品牌': '', '规格': '', '需求': '', '预算': '', '时间': ''}
        product = ""  # 产品
        quantity = ""  # 数量
        unitPrice = ""  # 单价
        brand = ""  # 品牌
        specs = ""  # 规格
        demand = "" # 采购需求
        budget = "" # 预算金额
        order_time = "" # 采购时间

        for i in range(min(4, len(items))):
            it = items[i]
            if len(it) < 15 and re.search(p1, it) != None:
                flag = True
                product = it
                header_dic['名称'] = i
                break
        if not flag:
            for i in range(min(4, len(items))):
                it = items[i]
                if len(it) < 15 and re.search(p2, it) and re.search(
                        '编号|编码|号|情况|报名|单位|位置|地址|数量|单价|价格|金额|品牌|规格类型|型号|公司|中标人|企业|供应商|候选人', it) == None:
                    flag = True
                    product = it
                    header_dic['名称'] = i
                    break
        if flag:
            for j in range(i + 1, len(items)):
                if len(items[j]) > 20 and len(re.sub('[\(（].*[）\)]|[^\u4e00-\u9fa5]', '', items[j])) > 10:
                    continue
                if header_dic['数量']=="" and re.search('数量', items[j]):
                    header_dic['数量'] = j
                    quantity = items[j]
                elif re.search('单价', items[j]):
                    header_dic['单价'] = j
                    unitPrice = items[j]
                elif re.search('品牌', items[j]):
                    header_dic['品牌'] = j
                    brand = items[j]
                elif re.search('规格', items[j]):
                    header_dic['规格'] = j
                    specs = items[j]

                elif re.search('需求', items[j]):
                    header_dic['需求'] = j
                    demand = items[j]
                elif re.search('预算', items[j]):
                    header_dic['预算'] = j
                    budget = items[j]
                elif re.search('时间|采购实施月份|采购月份|采购日期', items[j]):
                    header_dic['时间'] = j
                    order_time = items[j]

            if header_dic.get('名称', "") != "" :
                num = 0
                for it in (quantity, unitPrice, brand, specs, product, demand, budget, order_time):
                    if it != "":
                        num  += 1
                if num >=2:
                    return header_dic, flag, (product, quantity, unitPrice, brand, specs), (product, demand, budget, order_time)
        flag = False
        return header_dic, flag, (product, quantity, unitPrice, brand, specs), (product, demand, budget, order_time)

    def predict(self, docid='', html='', page_time=""):
        '''
        正则寻找table表格内 产品相关信息
        :param html:公告HTML原文
        :return:公告表格内 产品、数量、单价、品牌、规格 ，表头，表头列等信息
        '''


        soup = BeautifulSoup(html, 'lxml')
        flag_yx = True if re.search('采购意向', html) else False
        tables = soup.find_all(['table'])
        headers = []
        headers_demand = []
        header_col = []
        product_link = []
        demand_link = []
        total_product_money = 0
        for i in range(len(tables)-1, -1, -1):
            table = tables[i]
            if table.parent.name == 'td' and len(table.find_all('td')) <= 3:
                table.string = table.get_text()
                table.name = 'turntable'
                continue
            if not self.isTrueTable(table):
                continue
            self.fixSpan(table)
            inner_table = self.getTable(table)
            i = 0
            found_header = False
            header_colnum = 0
            if flag_yx:
                col0_l = []
                col1_l = []
                for tds in inner_table:
                    if len(tds) == 2:
                        col0_l.append(re.sub('：', '', tds[0]))
                        col1_l.append(tds[1])
                if len(set(col0_l) & self.header_set) > len(col0_l) * 0.2:
                    header_list2 = []
                    product = demand = budget = order_begin = order_end = ""
                    for i in range(len(col0_l)):
                        if re.search('项目名称', col0_l[i]):
                            header_list2.append(col0_l[i])
                            product = col1_l[i]
                        elif re.search('采购需求|需求概况', col0_l[i]):
                            header_list2.append(col0_l[i])
                            demand = col1_l[i]
                        elif re.search('采购预算|预算金额', col0_l[i]):
                            header_list2.append(col0_l[i])
                            budget = col1_l[i]
                            re_price = re.findall("[零壹贰叁肆伍陆柒捌玖拾佰仟萬億圆十百千万亿元角分]{3,}|\d[\d,]*(?:\.\d+)?万?", budget)
                            if re_price:
                                budget = re_price[0]
                                if '万元' in col0_l[i] and '万' not in budget:
                                    budget += '万元'
                                budget = str(getUnifyMoney(budget))
                            else:
                                budget = ""
                        elif re.search('采购时间|采购实施月份|采购月份|采购日期', col0_l[i]):
                            header_list2.append(col0_l[i])
                            order_time = col1_l[i].strip()
                            order_begin, order_end = self.fix_time(order_time, html, page_time)
                    if order_begin != "" and order_end!="":
                        order_begin_year = int(order_begin.split("-")[0])
                        order_end_year = int(order_end.split("-")[0])
                        # 限制附件错误识别时间
                        if order_begin_year>=2050 or order_end_year>=2050:
                            order_begin = order_end = ""
                    if product!= "" and demand != "" and budget!="" and order_begin != "":
                        link = {'project_name': product, 'product': [], 'demand': demand, 'budget': budget,
                                'order_begin': order_begin, 'order_end': order_end}
                        if link not in demand_link:
                            demand_link.append(link)
                            headers_demand.append('_'.join(header_list2))
                        continue
            while i < (len(inner_table)):
                tds = inner_table[i]
                not_empty = [it for it in tds if it != ""]
                if len(set(not_empty)) < len(not_empty) * 0.5 or len(tds)<2:
                    i += 1
                    continue
                product = ""  # 产品
                quantity = ""  # 数量
                unitPrice = ""  # 单价
                brand = ""  # 品牌
                specs = ""  # 规格
                demand = ""  # 采购需求
                budget = ""  # 预算金额
                order_time = ""  # 采购时间
                order_begin = ""
                order_end = ""
                if len(set(tds) & self.header_set) > len(tds) * 0.2:
                    header_dic, found_header, header_list, header_list2 = self.find_header(tds, self.p1, self.p2)
                    if found_header:
                        headers.append('_'.join(header_list))
                        headers_demand.append('_'.join(header_list2))
                        header_colnum = len(tds)
                        header_col.append('_'.join(tds))
                    i += 1
                    continue
                elif found_header:
                    if len(tds) != header_colnum:  # 表头、属性列数不一致跳过
                        i += 1
                        continue
                    id1 = header_dic.get('名称', "")
                    id2 = header_dic.get('数量', "")
                    id3 = header_dic.get('单价', "")
                    id4 = header_dic.get('品牌', "")
                    id5 = header_dic.get('规格', "")

                    id6 = header_dic.get('需求', "")
                    id7 = header_dic.get('预算', "")
                    id8 = header_dic.get('时间', "")
                    if re.search('[a-zA-Z\u4e00-\u9fa5]', tds[id1]) and tds[id1] not in self.header_set and \
                            re.search('备注|汇总|合计|总价|价格|金额|公司|附件|详见|无$|xxx', tds[id1]) == None:
                        product = tds[id1]
                        if id2 != "":
                            if re.search('\d+|[壹贰叁肆伍陆柒捌玖拾一二三四五六七八九十]', tds[id2]):
                                quantity = tds[id2]
                            else:
                                quantity = ""
                        if id3 != "":
                            if re.search('\d+|[零壹贰叁肆伍陆柒捌玖拾佰仟萬億十百千万亿元角分]{3,}', tds[id3]):
                                unitPrice = tds[id3]
                                re_price = re.findall("[零壹贰叁肆伍陆柒捌玖拾佰仟萬億圆十百千万亿元角分]{3,}|\d[\d,]*(?:\.\d+)?万?",unitPrice)
                                if re_price:
                                    unitPrice = re_price[0]
                                    if '万元' in header_list[2] and '万' not in unitPrice:
                                        unitPrice += '万元'
                                    # unitPrice = re.sub("[^0-9.零壹贰叁肆伍陆柒捌玖拾佰仟萬億圆十百千万亿元角分]", "", unitPrice)
                                    unitPrice = str(getUnifyMoney(unitPrice))
                                else:
                                    unitPrice = ""
                            else:
                                unitPrice = ""
                        if id4 != "":
                            if re.search('\w', tds[id4]):
                                brand = tds[id4]
                            else:
                                brand = ""
                        if id5 != "":
                            if re.search('\w', tds[id5]):
                                specs = tds[id5]
                            else:
                                specs = ""
                        if id6 != "":
                            if re.search('\w', tds[id6]):
                                demand = tds[id6]
                            else:
                                demand = ""
                        if id7 != "":
                            if re.search('\d+|[零壹贰叁肆伍陆柒捌玖拾佰仟萬億十百千万亿元角分]{3,}', tds[id7]):
                                budget = tds[id7]
                                re_price = re.findall("[零壹贰叁肆伍陆柒捌玖拾佰仟萬億圆十百千万亿元角分]{3,}|\d[\d,]*(?:\.\d+)?万?", budget)
                                if re_price:
                                    budget = re_price[0]
                                    if '万元' in header_list2[2] and '万' not in budget:
                                        budget += '万元'
                                    budget = str(getUnifyMoney(budget))
                                else:
                                    budget = ""
                            else:
                                budget = ""
                        if id8 != "":
                            if re.search('\w', tds[id8]):
                                order_time = tds[id8].strip()
                                order_begin, order_end = self.fix_time(order_time, html, page_time)
                        if quantity != "" or unitPrice != "" or brand != "" or specs != "":
                            link = {'product': product, 'quantity': quantity, 'unitPrice': unitPrice,
                                                      'brand': brand[:50], 'specs':specs}
                            if link not in product_link:
                                product_link.append(link)
                                mat = re.match('([0-9.,]+)[(（]?\w{,3}[)）]?$', link['quantity'])
                                if link['unitPrice'] != "" and mat:
                                    try:
                                        total_product_money += float(link['unitPrice'])*float(mat.group(1).replace(',', ''))
                                    except:
                                        log('产品属性单价数量相乘出错, 单价： %s, 数量： %s'%(link['unitPrice'], link['quantity']))
                        if order_begin != "" and order_end != "":
                            order_begin_year = int(order_begin.split("-")[0])
                            order_end_year = int(order_end.split("-")[0])
                            # 限制附件错误识别时间
                            if order_begin_year >= 2050 or order_end_year >= 2050:
                                order_begin = order_end = ""
                        if budget != "" and order_time != "":
                            link = {'project_name': product, 'product':[], 'demand': demand, 'budget': budget, 'order_begin':order_begin, 'order_end':order_end}
                            if link not in demand_link:
                                demand_link.append(link)
                    i += 1
                else:
                    i += 1
        if len(product_link)>0:
            attr_dic = {'product_attrs':{'data':product_link, 'header':headers, 'header_col':header_col}}
        else:
            attr_dic = {'product_attrs': {'data': [], 'header': [], 'header_col': []}}
        if len(demand_link)>0:
            demand_dic = {'demand_info':{'data':demand_link, 'header':headers_demand, 'header_col':header_col}}
        else:
            demand_dic = {'demand_info':{'data':[], 'header':[], 'header_col':[]}}
        return [attr_dic, demand_dic], total_product_money

    def predict_without_table(self,product_attrs,list_sentences,list_entitys,codeName,prem, html='', page_time=""):
        if len(prem[0]['prem'])==1:
            list_sentence = list_sentences[0]
            list_entity = list_entitys[0]
            _data = product_attrs[1]['demand_info']['data']
            re_bidding_time = re.compile("(采购时间|采购实施月份|采购月份|采购日期)[:：，].{0,2}$")
            order_times = []
            for entity in list_entity:
                if entity.entity_type=='time':
                    sentence = list_sentence[entity.sentence_index]
                    s = spanWindow(tokens=sentence.tokens, begin_index=entity.begin_index,
                                   end_index=entity.end_index,size=20)
                    entity_left = "".join(s[0])
                    if re.search(re_bidding_time,entity_left):
                        time_text = entity.entity_text.strip()
                        standard_time = re.compile("((?P<year>\d{4}|\d{2})\s*[-\/年\.]\s*(?P<month>\d{1,2})\s*[-\/月\.]\s*(?P<day>\d{1,2})日?)")
                        time_match = re.search(standard_time,time_text)
                        if time_match:
                            time_text = time_match.group()
                        order_times.append(time_text)
            # print(order_times)
            order_times = [tuple(self.fix_time(order_time, html, page_time)) for order_time in order_times]
            order_times = [order_time for order_time in order_times if order_time[0]!=""]
            if len(set(order_times))==1:
                order_begin,order_end = order_times[0]
                project_name = codeName[0]['name']
                pack_info = [pack for pack in prem[0]['prem'].values()]
                budget = pack_info[0].get('tendereeMoney',0)
                product = prem[0]['product']
                link = {'project_name': project_name, 'product': product, 'demand': project_name, 'budget': budget,
                        'order_begin': order_begin, 'order_end': order_end}
                _data.append(link)
            product_attrs[1]['demand_info']['data'] = _data
        return product_attrs

    def add_product_attrs(self,channel_dic, product_attrs,  list_sentences,list_entitys,codeName,prem,text,page_time):
        if channel_dic['docchannel']['docchannel']=="采购意向" and len(product_attrs[1]['demand_info']['data']) == 0:
            product_attrs = self.predict_without_table(product_attrs, list_sentences,
                                                                                          list_entitys,codeName,prem,text,page_time)
        if len(product_attrs[1]['demand_info']['data'])>0:
            for d in product_attrs[1]['demand_info']['data']:
                for product in set(prem[0]['product']):
                    if product in d['project_name'] and product not in d['product']:
                        d['product'].append(product)  #把产品在项目名称中的添加进需求要素中


# docchannel类型提取
class DocChannel():
  def __init__(self, life_model='/channel_savedmodel/channel.pb', type_model='/channel_savedmodel/doctype.pb'):
    self.lift_sess, self.lift_title, self.lift_content, self.lift_prob, self.lift_softmax,\
    self.mask, self.mask_title = self.load_life(life_model)
    self.type_sess, self.type_title, self.type_content, self.type_prob, self.type_softmax,\
    self.type_mask, self.type_mask_title = self.load_type(type_model)
    self.sequen_len = 200  # 150 200
    self.title_len = 30
    self.sentence_num = 10
    self.kws = '供货商|候选人|供应商|入选人|项目|选定|预告|中标|成交|补遗|延期|报名|暂缓|结果|意向|出租|补充|合同|限价|比选|指定|工程|废标|取消|中止|流标|资质|资格|地块|招标|采购|货物|租赁|计划|宗地|需求|来源|土地|澄清|失败|探矿|预审|变更|变卖|遴选|撤销|意见|恢复|采矿|更正|终止|废置|报建|流拍|供地|登记|挂牌|答疑|中选|受让|拍卖|竞拍|审查|入围|更改|条件|洽谈|乙方|后审|控制|暂停|用地|询价|预'

    lb_type = ['采招数据', '土地矿产', '拍卖出让', '产权交易', '新闻资讯']
    lb_life = ['采购意向', '招标预告', '招标公告', '招标答疑', '公告变更', '资审结果', '中标信息', '合同公告', '废标公告']
    self.id2type = {k: v for k, v in enumerate(lb_type)}
    self.id2life = {k: v for k, v in enumerate(lb_life)}

    self.load_pattern()

  def load_pattern(self):
      self.type_dic = {
          '土地矿产': '供地结果|(土地|用地|宗地|地块|海域|矿)的?(基本信息|基本情况|概况|信息|详情|来源|用途|性质|编号|位置|坐落|使用年限|出让年限)|(土地|山地|农田)(经营权)?(出让|出租|招租|租赁|承包|流转)|流转土地',
          '拍卖出让': '(拍卖|变卖|流拍|竞拍)的?(公告|活动|信息|结果|成交|主体|标的|资产|财产|方式|类型|流程|程序|规则|价格|保证金|时间)|(公开|进行|密封)(拍卖|变卖|竞拍)|第[一二三]次拍卖|(资产|司法|网络)拍卖|交易方式.{,2}拍卖|拍卖会',
          '产权交易': '(产权|资产|权证)的?(类型|信息|名称|编号|(基本)?情况)|(经营权|承包权|使用权|租赁权|股权|债权|排污权|化学需氧量|储备量)(挂牌|转让|出让)|竞价销售|销售结果|房屋所有权房产|免租期限|交易期限|(受让|转让|承租|出租|买受)(人|方)|(店面|店铺|商铺|铺位?|门面|门市|食堂|饭堂|校舍|车位|停车场|厂?房|仓?库|馆|资产|物业|房产|房屋|场地|农田|鱼?塘)\w{,4}(处置|招租|出租|续租|租赁|转让)|(出租|转让|产权|资产)(项目|中标|成交|流标|废标)|出租(用途|类型)|转让底价|租赁(标的物|情况)',
          '采招数据': '(采购|招标)(条件|范围|文件|内容)|(申请人|投标人|供应商|报价人|参选人)的?资格要求；'  # |变更|答疑|澄清|中标|成交|合同|废标|流标 |(采购|招标|代理)(人|机构|单位)|
      }

      self.title_type_dic = {
          '土地矿产': '(土地|用地|宗地|荒地|山地|海域|矿)(出让|出租|招租|租赁|承包|流转|使用权|经营权|征收|划拨|中标|成交)|供地结果|矿业权|探矿权|采矿权|(土地|用地|宗地|地块)(使用权)?(终止|中止|网上)?(挂牌|出让|拍卖|招拍|划拨)|征收土地',
          '拍卖出让': '(拍卖|变卖|流拍|竞拍)的?(公告|公示)|拍卖|变卖|流拍|竞拍',
          '产权交易': '经营权|承包权|使用权|租赁权|股权|债权|排污权|化学需氧量|储备量|竞价销售|销售结果|出租|招租|拍租|竞租|续租|挂牌|出让',
          '采招数据': '(采购|招标|询价|议价|比价|比选|遴选|邀请|邀标|磋商|洽谈|约谈|谈判)的?(公告|公示|中标|成交|结果|$)|工程招标',
          # |竞价 采招/产权都有竞价方式 # 意向|需求|预公?告|报建|总承包|工程|施工|设计|勘察|代理|监理 |变更|答疑|澄清|中标|成交|合同|废标|流标
          '新闻资讯': '(考试|面试|笔试)成绩|成绩的?(公告|公示|公布)|公开招聘|招聘(公告|简章|启事|合同制)|疫情防控\s{,5}(通知|情况|提示)'
      }
      self.life_dic = {
          '采购意向': '采购意向|招标意向|选取意向|意向公告|意向公示',
          '招标预告': '(预计|计划)(采购|招标)(时间|日期)|采购(计划编号|需求方案|预告|预案)|(预|需求)公示|需求(方案|信息|论证|公告|公示)',
          '招标公告': '(采购|招标|竞选|报名)条件|报名(时间|流程|方法|\w{,5}材料)|参加竞价采购交易资格|(申请人|投标人|供应商|报价人|参选人)的?资格要求|获取(采购|招标|询价|议价|竞价|比价|比选|遴选|邀请|邀标|磋商|洽谈|约谈|谈判|竞谈|应答)文件|(采购|招标|询价|议价|竞价|比价|比选|遴选|邀请|邀标|磋商|洽谈|约谈|谈判|竞谈|应答)文件的?(获取|领取)',
          '资审结果': '资审及业绩公示|资审结果及业绩|资格后审情况报告|资格(后审|预审|审查)结果(公告|公示)|(预审|审查)工作已经?结束|未通过原因', #|资格
          '招标答疑': '现澄清(为|如下)|答疑补遗|澄清内容如下|第[0-9一二三四五]次澄清|答疑澄清|(最高(投标)?限价|控制价|拦标价)公示',  # |异议的回复
          '公告变更': '第[\d一二]次变更|(更正|变更)(公告|公示|信息|内容|事项|原因|理由|日期|时间|如下)|原公告((主要)?(信息|内容)|发布时间)|(变更|更正)[前后]内容|现?在?(变更|更正|修改|更改)(内容)?为|(公告|如下|信息|内容|事项|结果|文件|发布|时间|日期)(更正|变更)',
          '候选人公示': '候选人公示|评标结果公示',
          '中标信息': '供地结果信息|采用单源直接采购的?情况说明|[特现]?将\w{,4}(成交|中标|中选|选定结果|选取结果|入围结果)\w{,4}(进行公示|公[示布]如下)|(中标|中选)(供应商|承包商|候选人|入围单位)如下|拟定供应商的情况|((中标|中选)(候选人|人|成交)|成交)\w{,3}(信息|情况)[：:\s]',
          '中标信息2': '\s(成交|中标|中选)(信息|日期|时间|总?金额|价格)[：:\s]|(采购|招标|成交|中标|中选|评标)结果|单一来源采购原因|拟采取单一来源方式采购',
          '中标信息3': '(中标|中选|成交|拟定|拟选用|最终选定的?|受让|唯一)(供应商|供货商|服务商|机构|企业|公司|单位|候选人|人)(名称)?[：:\s]|[、\s](第一名|(拟定|推荐|入围)?(供应商|供货商)|(中选|中标|供货)单位|中选人)[：:\s]',
          '中标信息neg': '按项目控制价下浮\d%即为成交价|成交原则|不得确定为(中标|成交)|招标人按下列原则选择中标人|评选成交供应商：|拟邀请供应商|除单一来源采购项目外|单一来源除外|(各.{,5}|尊敬的)(供应商|供货商)[：\s]|竞拍起止时间：|询价结果[\s\n:：]*不公开|本项目已具备招标条件|现对该项目进行招标公告|发布\w{2}结果后\d天内送达|本次\w{2}结果不对外公示',
      # |确定成交供应商[：，\s]
          '合同公告': '合同(公告|公示|信息|内容)|合同(编号|名称|主体|基本情况|签订日期)|(供应商乙方|乙方供应商)：|合同总?金额',
          '废标公告': '(终止|中止|废标|流标|失败|作废|异常|撤销)(结果)?(公告|公示|招标|采购|竞价)|(谈判结果为|结果类型)：?废标|((本|该)项目|本标段|本次(招标)?)((采购|招标)?(失败|终止|流标|废标)|予以废标|(按|做|作)?(流标|废标)处理)|(采购|招标|询价|议价|竞价|比价|比选|遴选|邀请|邀标|磋商|洽谈|约谈|谈判|竞谈|应答|项目)(终止|中止|废标|流标|失败|作废|异常|撤销)',
          '废标公告2': '(无效|中止|终止|废标|流标|失败|作废|异常|撤销)的?原因|本项目因故取消|本(项目|次)(公开)?\w{2}失败|已终止\s*原因：|(人数|供应商|单位)不足|已终止'
      }
      self.title_life_dic = {
          '采购意向': '采购意向|招标意向|选取意向|意向公告|意向公示|意向公开',
          '招标预告': '预公?告|预公示|报建公告|(批前|标前)公示|(供应|招标)计划表?$|(论证|征求|征集)(供应商)?意见|意见征询|需求评审公告|需求(公告|公示|意见)',
          '公告变更': '第[\d一二]次变更|(变更|更正(事项)?|更改|延期|暂停)(招标|采购)?的?(公告|公示|通知)|变更$|更正$',
          '招标答疑': '质疑|澄清|答疑(文件)?|补遗书?|(最高(投标)?限价|控制价|拦标价)(公示|公告|$)',
          '废标公告': '(终止|中止|废标|废除|流标|失败|作废|异常|撤销|取消成?交?|流拍)(结果|竞价|项目)?的?(公告|公示|$)|(终止|中止)(采购|招标|询价|议价|竞价|比价|比选|遴选|邀请|邀标|磋商|洽谈|约谈|谈判|拍卖|招租|交易|出让)',
          '合同公告': '(合同(成交|变更)?|(履约|验收)(结果)?)(公告|公示|信息|公式|公开|签订)|合同备案|合同书|合同$',
          '候选人公示': '候选人(变更)?公示|评标(结果)?公示|中标前?公示|中标预公示',
          '中标信息': '(中标|中选|中价|中租|成交|入选|确认)(候选人|人|供应商|记录|结果|变更)?(公告|公示|结果)|未?入围(公示|公告)|(遴选|采购|招标|竞价|议价|比选|询比?价|评选|谈判|邀标|邀请|洽谈|约谈|评标|发包|遴选|交易)\w{,2}结果|开标(记录|信息|情况)|单一来源|中标通知书|中标$',
          '资审结果': '((资格|资质)(审查|预审|后审|审核)|资审)结果(公告|公示)?|(资质|资格)(预审|后审)公示|资审及业绩公示',
          '招标公告': '(采购|招标|询价|议价|竞价|比价|比选|遴选|邀请|邀标|磋商|洽谈|约谈|谈判|拍卖|招租|交易|出让)的?(公告|公示|$)|公开(采购|招标|招租|拍卖|挂牌|出让)|(资审|预审|后审)公告',
      }

  def load_life(self,life_model):
    with tf.Graph().as_default() as graph:
      output_graph_def = graph.as_graph_def()
      with open(os.path.dirname(__file__)+life_model, 'rb') as f:
        output_graph_def.ParseFromString(f.read())
        tf.import_graph_def(output_graph_def, name='')
        # print("%d ops in the final graph" % len(output_graph_def.node))
        del output_graph_def
        sess = tf.Session(graph=graph)
        sess.run(tf.global_variables_initializer())
        inputs = sess.graph.get_tensor_by_name('inputs/inputs:0')
        prob = sess.graph.get_tensor_by_name('inputs/dropout:0')
        title = sess.graph.get_tensor_by_name('inputs/title:0')
        mask = sess.graph.get_tensor_by_name('inputs/mask:0')
        mask_title = sess.graph.get_tensor_by_name('inputs/mask_title:0')
        # logit = sess.graph.get_tensor_by_name('output/logit:0')
        softmax = sess.graph.get_tensor_by_name('output/softmax:0')
        return sess, title, inputs, prob, softmax, mask, mask_title

  def load_type(self,type_model):
    with tf.Graph().as_default() as graph:
      output_graph_def = graph.as_graph_def()
      with open(os.path.dirname(__file__)+type_model, 'rb') as f:
        output_graph_def.ParseFromString(f.read())
        tf.import_graph_def(output_graph_def, name='')
        # print("%d ops in the final graph" % len(output_graph_def.node))
        del output_graph_def
        sess = tf.Session(graph=graph)
        sess.run(tf.global_variables_initializer())
        inputs = sess.graph.get_tensor_by_name('inputs/inputs:0')
        prob = sess.graph.get_tensor_by_name('inputs/dropout:0')
        title = sess.graph.get_tensor_by_name('inputs/title:0')
        mask = sess.graph.get_tensor_by_name('inputs/mask:0')
        mask_title = sess.graph.get_tensor_by_name('inputs/mask_title:0')
        # logit = sess.graph.get_tensor_by_name('output/logit:0')
        softmax = sess.graph.get_tensor_by_name('output/softmax:0')
        return sess, title, inputs, prob, softmax, mask, mask_title

  def predict_process(self, docid='', doctitle='', dochtmlcon=''):
    # print('准备预处理')
    def get_kw_senten(s, span=10):
      doc_sens = []
      tmp = 0
      num = 0
      end_idx = 0
      for it in re.finditer(self.kws, s):  # '|'.join(keywordset)
        left = s[end_idx:it.end()].split()
        right = s[it.end():].split()
        tmp_seg = s[tmp:it.start()].split()
        if len(tmp_seg) > span or tmp == 0:
          doc_sens.append(' '.join(left[-span:] + right[:span]))
          end_idx = it.end() + 1 + len(' '.join(right[:span]))
          tmp = it.end()
          num += 1
          if num >= self.sentence_num:
            break
      if doc_sens == []:
        doc_sens.append(s)
      return doc_sens

    def word2id(wordlist, max_len=self.sequen_len):
      ids = [getIndexOfWords(w) for w in wordlist]
      ids = ids[:max_len] if len(ids) >= max_len else ids + [0] * (max_len - len(ids))
      assert len(ids) == max_len
      return ids

    cost_time = dict()
    datas = []
    datas_title = []
    try:
      segword_title = ' '.join(selffool.cut(doctitle)[0])
      segword_content = dochtmlcon
    except:
      segword_content = ''
      segword_title = ''
    if isinstance(segword_content, float):
      segword_content = ''
    if isinstance(segword_title, float):
      segword_title = ''
    segword_content = segword_content.replace(' 中 选 ', ' 中选 ').replace(' 中 标 ', ' 中标 ').replace(' 补 遗 ', ' 补遗 '). \
      replace(' 更 多', '').replace(' 更多', '').replace(' 中 号 ', ' 中标 ').replace(' 中 选人 ', ' 中选人 '). \
      replace(' 点击 下载 查看', '').replace(' 咨询 报价 请 点击', '').replace('终结', '终止')
    segword_title = re.sub('[^\s\u4e00-\u9fa5]', '', segword_title)
    segword_content = re.sub('[^\s\u4e00-\u9fa5]', '', segword_content)
    doc_word_list = segword_content.split()
    if len(doc_word_list) > self.sequen_len / 2:
      doc_sens = get_kw_senten(' '.join(doc_word_list[100:500]))
      doc_sens = ' '.join(doc_word_list[:100]) + '\n' + '\n'.join(doc_sens)
    else:
      doc_sens = ' '.join(doc_word_list[:self.sequen_len])
    # print('标题：',segword_title)
    # print('正文：',segword_content)
    datas.append(doc_sens.split())
    datas_title.append(segword_title.split())
    # print('完成预处理')
    return datas, datas_title

  def is_houxuan(self, title, content):
    '''
    通过标题和中文内容判断是否属于候选人公示类别
    :param title: 公告标题
    :param content: 公告正文文本内容
    :return: 1 是候选人公示 ；0 不是
    '''
    if re.search('候选人的?公示|评标结果|评审结果|中标公示', title):  # (中标|成交|中选|入围)
      if re.search('变更公告|更正公告|废标|终止|答疑|澄清', title):
        return 0
      return 1
    if re.search('候选人的?公示', content[:100]):
      if re.search('公示(期|活动)?已经?结束|公示期已满|中标结果公告|中标结果公示|变更公告|更正公告|废标|终止|答疑|澄清', content[:100]):
        return 0
      return 1
    else:
      return 0

  def predict(self, title='', list_sentence='', web_source_no='', original_docchannel=''):
    not_extract_dic = {
        104: '招标文件',
        106: '法律法规',
        107: '新闻资讯',
        108: '拟建项目',
        109: '展会推广',
        110: '企业名录',
        111: '企业资质',
        112: '全国工程人员',
        113: '业主采购'
    }
    if original_docchannel in not_extract_dic:
        return {'docchannel': {'docchannel':'', 'doctype':not_extract_dic[original_docchannel], "original_docchannel_id": str(original_docchannel)}}
    if web_source_no in ['02104-7']:
      return {'docchannel': {'docchannel':'', 'doctype':'采招数据'}}

    if isinstance(list_sentence, list):
      token_l = [it.tokens for it in list_sentence]
      tokens = [it for l in token_l for it in l]
      content = ' '.join(tokens[:500])

    title = re.sub('[^\u4e00-\u9fa5]', '', title)
    if len(title)>50:
        title = title[:20]+title[-30:]
    data_content, data_title = self.predict_process(docid='', doctitle=title[-50:], dochtmlcon=content) # 标题最多取50字
    text_len = len(data_content[0]) if len(data_content[0])<self.sequen_len else self.sequen_len
    title_len = len(data_title[0]) if len(data_title[0])<self.title_len else self.title_len
    result = {'docchannel': {'docchannel':'', 'doctype':'', "original_docchannel_id": str(original_docchannel)}}

    array_content = embedding(data_content, shape=(len(data_content), self.sequen_len, 128))
    array_title = embedding(data_title, shape=(len(data_title), self.title_len, 128))
    pred = self.type_sess.run(self.type_softmax,
                                    feed_dict={
                                              self.type_title: array_title,
                                              self.type_content: array_content,
                                              self.type_mask:[[0]*text_len+[1]*(self.sequen_len-text_len)],
                                              self.type_mask_title:[[0]*title_len+[1]*(self.title_len-title_len)],
                                              self.type_prob:1}
                            )
    id = np.argmax(pred, axis=1)[0]
    prob = pred[0][id]
    result['docchannel']['doctype'] = self.id2type[id]
    # print('公告类别：', self.id2type[id], '概率：',prob)
    # if id == 0:
    if result['docchannel']['doctype'] not in ['', '新闻资讯']:
      pred = self.lift_sess.run(self.lift_softmax,
                                      feed_dict={
                                                self.lift_title: array_title,
                                                self.lift_content: array_content,
                                                self.mask: [[0] * text_len + [1] * (self.sequen_len - text_len)],
                                                self.mask_title: [[0] * title_len + [1] * (self.title_len - title_len)],
                                                self.lift_prob:1}
                              )
      id = np.argmax(pred, axis=1)[0]
      prob = pred[0][id]
      result['docchannel']['docchannel'] = self.id2life[id]
      # print('生命周期：纯模型预测',self.id2life[id], '概率：',prob)
      # if id == 6:
      if result['docchannel']['docchannel'] == '中标信息':
        if self.is_houxuan(''.join([it for it in title if it.isalpha()]), ''.join([it for it in content if it.isalpha()])):
          result['docchannel']['docchannel'] = '候选人公示'
          # return '候选人公示', prob
          # return [{'docchannel': '候选人公示'}]

    return result
    #   return [{'docchannel':self.id2life[id]}]
    # else:
    #   # return self.id2type[id], prob
    #   return [{'docchannel':self.id2type[id]}]

  def predict_rule(self, title, content, channel_dic, prem_dic):
      '''2022/2/10加入规则去除某些数据源及内容过短且不包含类别关键词的公告不做预测'''
      hetong = '(合同|验收|履约)(公告|公示)|合同号?$'  # 合同标题正则
      zhongbiao_t = '(中标|中选|成交|入选|入围|结果|确认)(公告|公示|结果)|(遴选|采购|招标|竞价|议价|比选|询价|评选|谈判|邀标|邀请|洽谈|约谈|评标|发包|遴选)结果|开标(记录|信息|情况)|单一来源|直接(选取|选定)|中标通知书|中标$'
      zhongbiao_c = '(中标|中选|成交|拟选用|拟邀请|最终选定的?|拟定)(供应商|供货商|服务商|企业|公司|单位|(候选)?人)(名称)?[:：]|[，。：.](供应商|供货商|服务商)(名称)?：|指定的中介服务机构：|建设服务单位：'
      zhaobiao_t = '(遴选|采购|招标|竞价|议价|比选|询价|评选|谈判|邀标|邀请|洽谈|约谈)(公告|公示|$)'
      title_cn = re.sub('[^\u4e00-\u9fa5]', '', title)
      if len(re.sub('[^\u4e00-\u9fa5]', "", content))<50 and channel_dic['docchannel']['doctype'] != '新闻资讯':
          if re.search(hetong, title_cn) != None:
              channel_dic['docchannel']['docchannel'] = '合同公告'
          elif re.search(zhongbiao_t, title_cn):
              channel_dic['docchannel']['docchannel'] = '中标信息'
          elif re.search(zhaobiao_t, title_cn):
              channel_dic['docchannel']['docchannel'] = '招标公告'
          else:
              channel_dic['docchannel']['docchannel'] = ''
      elif channel_dic['docchannel'].get('docchannel', '') == '招标公告' and 'win_tenderer' in json.dumps(prem_dic,
                                                                                              ensure_ascii=False):
          if re.search(hetong, title_cn) != None:
              channel_dic['docchannel']['docchannel'] = '合同公告'
              log('正则把招标公告修改为合同公告')
          elif re.search(zhongbiao_t, title_cn) or re.search(zhongbiao_t, content[:200]) or re.search(zhongbiao_c,
                                                                                                      content):
              channel_dic['docchannel']['docchannel'] = '中标信息'
              log('正则把招标公告修改为中标信息')
      elif channel_dic['docchannel'].get('docchannel', '') == '中标信息' and 'win_tenderer' not in json.dumps(prem_dic,
                                                                                                    ensure_ascii=False):
          if re.search(hetong, title_cn):
              channel_dic['docchannel']['docchannel'] = '合同公告'
              log('正则把中标信息修改为合同公告')
          elif re.search(zhongbiao_t, title_cn) or re.search(zhongbiao_t, content[:200]) or re.search(zhongbiao_c,
                                                                                                      content):
              pass
          elif re.search(zhaobiao_t, title_cn):
              channel_dic['docchannel']['docchannel'] = '招标公告'
              log('正则把中标信息修改为招标公告')
          elif re.search('中标|成交|中选|入选|入围|结果|供应商|供货商|候选人', title_cn+content)==None:
              channel_dic['docchannel']['docchannel'] = ''
              log('正则把中标信息修改为空')
      return channel_dic

  def predict_merge(self, title, list_sentence, html, bidway, prem, original_docchannel='', web_source_no=''):
      '''
      正则，模型混合预测，返回公告类型及生命周期
      :param title:  公告标题
      :param content: 预处理后的返回的句子实体列表 list_sentence
      :param html: 公告原文 html 内容
      :param bidway: 招标方式
      :param prem: 提取的prem 字典
      :return: {'docchannel': {'docchannel':'中标信息', 'doctype':'采招数据'}} 字典格式
      '''
      def cut_single_cn_space(text):
          new_text = ""
          for w in text.split():
              if len(w) == 1 or re.search('^[\u4e00-\u9fa5][:：]', w):
                  new_text += w
              else:
                  new_text += ' ' + w
          return new_text

      def html2text(html):
          ser = re.search('<div[^<>]*richTextFetch', html)
          # if ser and len(re.sub('[^\u4e00-\u9fa5]', '', html[:ser.start()]))>500:
          #     html = html[:ser.start()]+'##richTextFetch##'
          if ser:
              if len(re.sub('[^\u4e00-\u9fa5]', '', html[:ser.start()])) > 200:
                  html = html[:ser.start()] + '##richTextFetch##'
              else:
                  html = html[:ser.start() + 500]
          text = re.sub('<[^<]*?>', '', html).replace('&nbsp;', ' ')
          text = re.sub('http[0-9a-zA-Z-.:/]+|[0-9a-zA-Z-./@]+', '', text)
          text = re.sub('\s+', ' ', text)
          text = re.sub('[/|[()（）]', '', text)
          text = cut_single_cn_space(text)
          return text[:20000]

      def count_diffser(pattern, text):
          num = 0
          kw = []
          for p in pattern.split('；'):
              if re.search(p, text):
                  num += 1
                  kw.append(re.search(p, text).group(0))
          return num, '；'.join(kw)

      def is_contain_winner(extract_json):
          if re.search('win_tenderer', extract_json):
              return True
          else:
              return False

      def is_single_source(bidway, title):
          if re.search('单一来源|单一性采购', title):
              return True
          elif bidway == '单一来源':
              return True
          else:
              return False

      def get_type(title, text):
          if re.search(self.title_type_dic['土地矿产'], title) or re.search(self.type_dic['土地矿产'],
                                                                   text):  # and re.search('(土地|用地|宗地|地块)(经营权)?(流转|承包|出租|招租|租赁|确权)', text)==None
              if re.search(self.title_type_dic['采招数据'], title + text.strip().split(' ')[0]):
                  return '采招数据', re.search(self.title_type_dic['采招数据'], title + text.strip().split(' ')[0]).group(0)
              return '土地矿产', (re.search(self.title_type_dic['土地矿产'], title) or re.search(self.type_dic['土地矿产'], text)).group(0)
          elif (re.search(self.title_type_dic['拍卖出让'], title) or re.search(self.type_dic['拍卖出让'], text)):
              if re.search(self.title_type_dic['采招数据'], title + text.strip().split(' ')[0]):
                  return '采招数据', re.search(self.title_type_dic['采招数据'], title + text.strip().split(' ')[0]).group(0)
              return '拍卖出让', (re.search(self.title_type_dic['拍卖出让'], title) or re.search(self.type_dic['拍卖出让'], text)).group(0)
          elif re.search(self.title_type_dic['产权交易'], title) or re.search(self.type_dic['产权交易'], text):
              if re.search(self.title_type_dic['采招数据'], title + text.strip().split(' ')[0]):
                  return '采招数据', re.search(self.title_type_dic['采招数据'], title + text.strip().split(' ')[0]).group(0)
              return '产权交易', (re.search(self.title_type_dic['产权交易'], title) or re.search(self.type_dic['产权交易'], text)).group(0)
          elif re.search(self.title_type_dic['采招数据'], title) or re.search(self.type_dic['采招数据'], title + text):
              return '采招数据', (
                          re.search(self.title_type_dic['采招数据'], title) or re.search(self.type_dic['采招数据'], title + text)).group(
                  0)
          elif re.search(self.title_type_dic['新闻资讯'], title):
              if re.search(self.title_type_dic['采招数据'], title +text.strip().split(' ')[0]):
                  return '采招数据', re.search(self.title_type_dic['采招数据'], title +text.strip().split(' ')[0]).group(0)
              return '新闻资讯', re.search(self.title_type_dic['新闻资讯'], title).group(0)
          else:
              return '', '没有公告类型关键词,返回空'

      def get_life(title, text):
          title = re.sub('[-()（）0-9a-z]|第?[二三四]次公?告?', '', title)
          first_line = text.split()[0] if len(text.split()) > 2 else ''
          if title.strip()[-2:] not in ['公告', '公示'] and 5 < len(first_line) < 30 and first_line[-2:] in ['公告', '公示']:
              # print('title: ', title, first_line)
              title += first_line
              # print('title: ', title)

          def count_score(l):
              return len(l) + len(set(l)) * 2

          life_kw_title = {}
          life_kw_content = {}
          life_score = {}

          # msc = ""
          # 查找标题每个类别关键词
          for k, v in self.title_life_dic.items():
              k2 = re.sub('[\da-z]', '', k)
              if k2 not in life_kw_title:
                  life_kw_title[k2] = []
              for it in re.finditer(v, title):
                  life_kw_title[k2].append(it.group(0))

          # 查找正文每个类别关键词
          for k, v in self.life_dic.items():
              k2 = re.sub('[\da-z]', '', k)
              if k2 not in life_kw_content:
                  life_kw_content[k2] = {'pos': [], 'neg': []}
              for it in re.finditer(v, text):
                  if 'neg' not in k:
                      life_kw_content[k2]['pos'].append(it.group(0))
                  else:
                      life_kw_content[k2]['neg'].append(it.group(0))
          for k2 in life_kw_content:
              life_score[k2] = count_score(life_kw_content[k2]['pos']) - count_score(
                  life_kw_content[k2]['neg'])

          life_kw_title = {k: v for k, v in life_kw_title.items() if v != []}
          life_kw_content = {k: v for k, v in life_kw_content.items() if life_score[k] > 0}
          msc = [life_kw_title, life_kw_content, life_score]
          msc = json.dumps(msc, ensure_ascii=False)

          max_score = 0
          life_list = []
          for k in life_score.keys():
              if life_score[k] > max_score:
                  max_score = life_score[k]
                  life_list = [k]
              elif life_score[k] == max_score and life_score[k] > 0:
                  life_list.append(k)

          if '采购意向' in life_kw_title or '采购意向' in life_list:
              return '采购意向', msc
          elif '招标预告' in life_kw_title or '招标预告' in life_list:
              if set(['中标信息', '候选人公示', '合同公告']) & set(life_kw_content) != set():
                  return '', msc
              return '招标预告', msc
          elif '公告变更' in life_kw_title or '公告变更' in life_list:
              if life_score.get('候选人公示', 0) > 3 or '候选人公示' in life_kw_title:
                  return '候选人公示', msc
              elif life_score.get('合同公告', 0) > 3 or '合同公告' in life_kw_title:
                  return '合同公告', msc
              elif life_score.get('中标信息', 0) > 3 or '中标信息' in life_kw_title:
                  return '中标信息', msc
              elif '招标公告' in life_kw_title and life_score.get('公告变更', 0) < 4:
                  return '招标公告', msc
              return '公告变更', msc
          elif '招标答疑' in life_kw_title or '招标答疑' in life_list:
              if '招标公告' in life_kw_title and life_score.get('招标答疑', 0) < 4:
                  return '招标公告', msc
              elif life_score.get('招标答疑', 0) < max_score:
                  if max_score > 3 and len(life_list) == 1:
                      return life_list[0], msc
                  return '', msc
              return '招标答疑', msc
          elif '候选人公示' in life_kw_title or '候选人公示' in life_list:
              if '招标公告' in life_kw_title and life_score.get('招标公告', 0) > 3:
                  return '招标公告', msc
              return '候选人公示', msc
          elif '合同公告' in life_kw_title or '合同公告' in life_list:
              if '招标公告' in life_kw_title and life_score.get('招标公告', 0) > 3:
                  return '招标公告', msc
              return '合同公告', msc
          elif '中标信息' in life_kw_title or '中标信息' in life_list:
              if '招标公告' in life_kw_title and life_score.get('招标公告',
                                                            0) > 2:  # (life_score.get('招标公告', 0)>2 or life_score.get('中标信息', 0)<4) 0.7886409793924245
                  return '招标公告', msc
              elif '废标公告' in life_kw_title:
                  return '废标公告', msc
              elif life_score.get('候选人公示', 0) >= 3:
                  return '候选人公示', msc
              elif life_score.get('合同公告', 0) > 5:
                  return '合同公告', msc
              return '中标信息', msc
          elif '废标公告' in life_kw_title or '废标公告' in life_list:
              if life_score.get('招标公告', 0) > 3 and '废标公告' not in life_kw_title:
                  return '招标公告', msc
              return '废标公告', msc
          elif '资审结果' in life_kw_title or '资审结果' in life_list:
              return '资审结果', msc
          elif '招标公告' in life_kw_title or '招标公告' in life_list:
              return '招标公告', msc

          return '', msc

      def get_model_inputs(list_sentence):
          list_sentence = sorted(list_sentence, key=lambda x:x.sentence_index)
          token_l = [it.tokens for it in list_sentence]
          tokens = [it for l in token_l for it in l]
          content = ' '.join(tokens[:500])
          data_content, data_title = self.predict_process(docid='', doctitle=title[-50:],
                                                          dochtmlcon=content)  # 标题最多取50字
          text_len = len(data_content[0]) if len(data_content[0]) < self.sequen_len else self.sequen_len
          title_len = len(data_title[0]) if len(data_title[0]) < self.title_len else self.title_len

          array_content = embedding(data_content, shape=(len(data_content), self.sequen_len, 128))
          array_title = embedding(data_title, shape=(len(data_title), self.title_len, 128))
          return array_content, array_title ,text_len, title_len, content

      def type_model_predict():
           pred = self.type_sess.run(self.type_softmax,
                                     feed_dict={
                                         self.type_title: array_title,
                                         self.type_content: array_content,
                                         self.type_mask: [[0] * text_len + [1] * (self.sequen_len - text_len)],
                                         self.type_mask_title: [[0] * title_len + [1] * (self.title_len - title_len)],
                                         self.type_prob: 1}
                                     )
           id = np.argmax(pred, axis=1)[0]
           prob = pred[0][id]
           return id, prob

      def life_model_predict():
          pred = self.lift_sess.run(self.lift_softmax,
                                    feed_dict={
                                        self.lift_title: array_title,
                                        self.lift_content: array_content,
                                        self.mask: [[0] * text_len + [1] * (self.sequen_len - text_len)],
                                        self.mask_title: [[0] * title_len + [1] * (self.title_len - title_len)],
                                        self.lift_prob: 1}
                                    )
          id = np.argmax(pred, axis=1)[0]
          prob = pred[0][id]
          return id, prob

      def final_change(msc):
          '''
          修改逻辑：
          1、中标公告、合同公告无中标人且原始为非中标，返回原类型
          2、废标公告有中标人且标题无废标关键词，返回中标信息
          3、答疑公告标题无答疑关键且原始为招标，返回原始类别
          4、招标公告有中标人且原始为中标，返回中标信息
          5、预测及原始均在招标、预告、意向，返回原始类别
          6、预测及原始均在变更、答疑，返回原始类别
          7、预测为采招数据，原始为产权且有关键词，返回原始类别
          8、废标公告原始为招标、预告且标题无废标关键期，返回原始类别
          '''
          if result['docchannel']['docchannel'] in ['中标信息', '合同公告'] and origin_dic.get(
                  original_docchannel, '') in ['招标公告', '采购意向', '招标预告', '公告变更'] and is_contain_winner(prem_json)==False:
              result['docchannel']['docchannel'] = origin_dic.get(original_docchannel, '')
              msc += '最终规则修改：中标公告、合同公告无中标人且原始为非中标，返回原类型'
          elif result['docchannel']['docchannel'] == '废标公告' and is_contain_winner(prem_json) and re.search(
                  self.title_life_dic['废标公告'], title) == None:
              result['docchannel']['docchannel'] = '中标信息'
              msc += '最终规则修改：预测为废标却有中标人且标题无废标关键词改为中标信息;'
          elif result['docchannel']['docchannel'] in ['招标答疑'] and re.search(
                  self.title_life_dic['招标答疑'], title) == None and origin_dic.get(
                  original_docchannel, '') in ['招标公告', '采购意向', '招标预告']:
              result['docchannel']['docchannel'] = origin_dic.get(original_docchannel, '')
              msc += '最终规则修改：答疑公告标题无答疑关键且原始为招标，返回原始类别;'
          elif result['docchannel']['docchannel'] == '招标公告' and is_contain_winner(prem_json) and origin_dic.get(
                  original_docchannel, '') == '中标信息':
              result['docchannel']['docchannel'] = '中标信息'
              msc += '最终规则修改：预测为招标公告却有中标人且原始为中标改为中标信息;'
          elif result['docchannel']['docchannel'] in ['招标公告', '采购意向', '招标预告'] and origin_dic.get(
                  original_docchannel, '') in ['招标公告', '采购意向', '招标预告']:
              result['docchannel']['docchannel'] = origin_dic.get(original_docchannel, '')
              msc += '最终规则修改：预测及原始均在招标、预告、意向，返回原始类别'
          elif result['docchannel']['docchannel'] in ['招标答疑', '公告变更'] and origin_dic.get(
                  original_docchannel, '') in ['招标答疑', '公告变更']:
              result['docchannel']['docchannel'] = origin_dic.get(original_docchannel, '')
              msc += '最终规则修改：预测及原始均在答疑、变更，返回原始类别'
          elif result['docchannel']['doctype'] == '采招数据' and origin_dic.get(
                  original_docchannel, '') in ['产权交易', '土地矿产'] and re.search('产权|转让|受让|招租|出租|承租|竞价|资产', text):
              result['docchannel']['doctype'] = origin_dic.get(original_docchannel, '')
              msc += '最终规则修改：预测为采招数据，原始为产权且有关键词，返回原始类别'
          elif result['docchannel']['docchannel'] == '废标公告' and origin_dic.get(
                  original_docchannel, '') in ['招标公告', '采购意向', '招标预告'] and re.search(
                  self.title_life_dic['废标公告'], title) == None:
              result['docchannel']['docchannel'] = origin_dic.get(original_docchannel, '')
              msc += '最终规则修改：废标公告原始为招标、预告且标题无废标关键期，返回原始类别;'

          '''下面是新格式增加返回字段'''
          if result['docchannel']['docchannel'] != '':  # 预测到生命周期的复制到life_docchannel，否则用数据源结果
              result['docchannel']['life_docchannel'] = result['docchannel']['docchannel']
          else:
              result['docchannel']['life_docchannel'] = origin_dic.get(original_docchannel, '原始类别')
          return msc

      not_extract_dic = {
          104: '招标文件',
          106: '法律法规',
          107: '新闻资讯',
          108: '拟建项目',
          109: '展会推广',
          110: '企业名录',
          111: '企业资质',
          112: '全国工程人员',
          113: '业主采购'
      }

      origin_dic = {51: '公告变更',
       52: '招标公告',
       101: '中标信息',
       102: '招标预告',
       103: '招标答疑',
       104: '招标文件',
       105: '资审结果',
       106: '法律法规',
       107: '新闻资讯',
       108: '拟建项目',
       109: '展会推广',
       110: '企业名录',
       111: '企业资质',
       112: '全国工程',
       113: '业主采购',
       114: '采购意向',
       115: '拍卖出让',
       116: '土地矿产',
       117: '产权交易',
       118: '废标公告',
       119: '候选人公示',
       120: '合同公告'}
      if original_docchannel in not_extract_dic:
          return {'docchannel': {'docchannel': '', 'doctype': not_extract_dic[original_docchannel], 'life_docchannel': origin_dic.get(original_docchannel, '原始类别')}}, '公告类别不在提取范围'
      if web_source_no in ['02104-7', '04733', 'DX007628-6']: # 这些数据源无法识别
          return {'docchannel': {'docchannel': '', 'doctype': '采招数据', 'life_docchannel': origin_dic.get(original_docchannel, '原始类别')}}, '此数据源公告分类不明确，返回数据源类别'

      title = re.sub('[^\u4e00-\u9fa5]', '', title)
      if len(title) > 50:
          title = title[:20] + title[-30:]

      text = html2text(html)
      prem_json = json.dumps(prem, ensure_ascii=False)
      result = {'docchannel': {'docchannel': '', 'doctype': ''}}

      doc_type, type_kw = get_type(title, text)
      # doc_life, life_kw = get_life(title, text, prem_json, bidway, original_docchannel)
      doc_life, life_kw = get_life(title, text)
      if doc_type in self.title_type_dic:
          result['docchannel']['doctype'] = doc_type
      if doc_life in self.title_life_dic:
          result['docchannel']['docchannel'] = doc_life
      # print('channel正则预测结果：', result)
      msc = '正则结果：类型：%s, 关键词：%s, 周期：%s， 关键词：%s'%(doc_type, type_kw,doc_life, life_kw)+'\n'+'模型结果：'
      # print('类型：%s, 关键词：%s, 周期：%s， 关键词：%s'%(doc_type, type_kw,doc_life, life_kw))
      if doc_type == "" or doc_life == "":
          array_content, array_title, text_len, title_len, content = get_model_inputs(list_sentence)
          if doc_type =="":
              type_id, type_prob = type_model_predict()
              type_model = self.id2type[type_id]
              result['docchannel']['doctype'] = type_model
              msc += type_model + '；'
              # print('公告类别：', self.id2type[id], '概率：',prob)
              # if id == 0:
          if doc_life=="" and result['docchannel']['doctype'] not in ['', '新闻资讯']:
              if len(text)>150 and re.search(self.kws, content):
                  life_id, life_prob = life_model_predict()
                  life_model = self.id2life[life_id]
                  result['docchannel']['docchannel'] = life_model
                  msc += life_model + '；\n'

      msc = final_change(msc)
      # print('channel ', msc)
      return result, msc

# 保证金支付方式提取
class DepositPaymentWay():
    def __init__(self,):
        self.pt = '(保证金的?(交纳|缴纳|应按下列|入账|支付)方式)[:：]*([^，。]{,60})'
        self.pt2 = '保证金(必?须以|必?须?通过|以)(.{,8})方式'
        kws = ['银行转账', '公?对公方?式?转账', '对公转账', '柜台转账', '(线上|网上)自?行?(缴纳|交纳|缴退|收退)',
               '网上银行支付', '现金存入', '直接缴纳', '支票', '汇票', '本票', '电汇', '转账', '汇款', '随机码',
               '入账', '基本账户转出', '基本账户汇入', '诚信库中登记的账户转出',
               '银行保函', '电子保函', '担保函', '保证保险', '合法担保机构出具的担保', '金融机构、担保机构出具的保函']
        self.kws = sorted(kws, key=lambda x: len(x), reverse=True)

    def predict(self,content):
        pay_way = {'deposit_patment_way':''}
        result = []
        pay = re.search(self.pt, content)
        if pay:
            # print(pay.group(0))
            pay = pay.group(3)
            for it in re.finditer('|'.join(self.kws), pay):
                result.append(it.group(0))
            pay_way['deposit_patment_way'] = '；'.join(result)
            return pay_way
        pay = re.search(self.pt2, content)
        if pay:
            # print(pay.group(0))
            pay = pay.group(2)
            for it in re.finditer('|'.join(self.kws), pay):
                result.append(it.group(0))
            pay_way['deposit_patment_way'] = '；'.join(result)
            return pay_way
        else:
            return pay_way


# 总价单价提取
class TotalUnitMoney:
    def __init__(self):
        pass

    def predict(self, list_sentences, list_entitys):
        for i in range(len(list_entitys)):
            list_entity = list_entitys[i]

            # 总价单价
            for _entity in list_entity:
                if _entity.entity_type == 'money':
                    word_of_sentence = list_sentences[i][_entity.sentence_index].sentence_text
                    # 总价在中投标金额中
                    if _entity.label == 1:
                        result = extract_total_money(word_of_sentence,
                                                     _entity.entity_text,
                                                     [_entity.wordOffset_begin, _entity.wordOffset_end])
                        if result:
                            _entity.is_total_money = 1

                    # 单价在普通金额中
                    else:
                        result = extract_unit_money(word_of_sentence,
                                                    _entity.entity_text,
                                                    [_entity.wordOffset_begin, _entity.wordOffset_end])
                        if result:
                            _entity.is_unit_money = 1
                # print("total_unit_money", _entity.entity_text,
                #       _entity.is_total_money, _entity.is_unit_money)

# 行业分类
class IndustryPredictor():
    def __init__(self,):
        self.model_path = os.path.dirname(__file__)+ '/industry_model'
        self.id2lb = {0: '专业施工', 1: '专用仪器仪表', 2: '专用设备修理', 3: '互联网信息服务', 4: '互联网安全服务', 5: '互联网平台', 6: '互联网接入及相关服务', 7: '人力资源服务',
             8: '人造原油', 9: '仓储业', 10: '仪器仪表', 11: '仪器仪表修理', 12: '会计、审计及税务服务', 13: '会议、展览及相关服务', 14: '住宅、商业用房',
             15: '体育场地设施管理', 16: '体育组织', 17: '体育设备', 18: '保险服务', 19: '信息处理和存储支持服务', 20: '信息技术咨询服务',
             21: '信息系统集成和物联网技术服务', 22: '修缮工程', 23: '健康咨询', 24: '公路旅客运输', 25: '其他专业咨询与调查', 26: '其他专业技术服务',
             27: '其他交通运输设备', 28: '其他公共设施管理', 29: '其他土木工程建筑', 30: '其他工程服务', 31: '其他建筑建材', 32: '其他运输业', 33: '农业和林业机械',
             34: '农业服务', 35: '农产品', 36: '农副食品，动、植物油制品', 37: '出版业', 38: '办公消耗用品及类似物品', 39: '办公设备', 40: '化学原料及化学制品',
             41: '化学纤维', 42: '化学药品和中药专用设备', 43: '医疗设备', 44: '医药品', 45: '卫星传输服务', 46: '卫生', 47: '印刷服务', 48: '图书和档案',
             49: '图书档案设备', 50: '图书馆与档案馆', 51: '土地管理业', 52: '地质勘查', 53: '地震服务', 54: '场馆、站港用房', 55: '城市公共交通运输',
             56: '塑料制品、半成品及辅料', 57: '天然石料', 58: '娱乐设备', 59: '婚姻服务', 60: '安全保护服务', 61: '安全生产设备', 62: '家具用具',
             63: '家用电器修理', 64: '工业、生产用房', 65: '工业与专业设计及其他专业技术服务', 66: '工矿工程建筑', 67: '工程技术与设计服务', 68: '工程机械',
             69: '工程监理服务', 70: '工程评价服务', 71: '工程造价服务', 72: '市场调查', 73: '广告业', 74: '广播', 75: '广播、电视、电影设备',
             76: '广播电视传输服务', 77: '废弃资源综合利用业', 78: '建筑涂料', 79: '建筑物、构筑物附属结构', 80: '建筑物拆除和场地准备活动', 81: '建筑装饰和装修业',
             82: '录音制作', 83: '影视节目制作', 84: '房地产中介服务', 85: '房地产开发经营', 86: '房地产租赁经营', 87: '房屋租赁', 88: '招标代理',
             89: '探矿、采矿、选矿和造块设备', 90: '政法、检测专用设备', 91: '教育服务', 92: '教育设备', 93: '文物及非物质文化遗产保护', 94: '文物和陈列品',
             95: '文艺创作与表演', 96: '文艺设备', 97: '新闻业', 98: '旅行社及相关服务', 99: '日杂用品', 100: '有色金属冶炼及压延产品', 101: '有色金属矿',
             102: '木材、板材等', 103: '木材采集和加工设备', 104: '机械设备', 105: '机械设备经营租赁', 106: '林业产品', 107: '林业服务', 108: '架线和管道工程建筑',
             109: '核工业专用设备', 110: '橡胶制品', 111: '殡葬服务', 112: '殡葬设备及用品', 113: '气象服务', 114: '水上交通运输设备', 115: '水上运输业',
             116: '水利和水运工程建筑', 117: '水工机械', 118: '水文服务', 119: '水资源管理', 120: '污水处理及其再生利用', 121: '汽车、摩托车修理与维护',
             122: '法律服务', 123: '洗染服务', 124: '测绘地理信息服务', 125: '海洋仪器设备', 126: '海洋工程建筑', 127: '海洋服务', 128: '消防设备',
             129: '清洁服务', 130: '渔业产品', 131: '渔业服务', 132: '炼焦和金属冶炼轧制设备', 133: '烟草加工设备', 134: '热力生产和供应', 135: '焦炭及其副产品',
             136: '煤炭采选产品', 137: '燃气生产和供应业', 138: '物业管理', 139: '特种用途动、植物', 140: '环保咨询', 141: '环境与生态监测检测服务',
             142: '环境污染防治设备', 143: '环境治理业', 144: '玻璃及其制品', 145: '理发及美容服务', 146: '生态保护', 147: '电信',
             148: '电力、城市燃气、蒸汽和热水、水', 149: '电力供应', 150: '电力工业专用设备', 151: '电力工程施工', 152: '电力生产', 153: '电子和通信测量仪器',
             154: '电工、电子专用生产设备', 155: '电影放映', 156: '电气安装', 157: '电气设备', 158: '电气设备修理', 159: '畜牧业服务', 160: '监控设备',
             161: '石油制品', 162: '石油和化学工业专用设备', 163: '石油和天然气开采产品', 164: '石油天然气开采专用设备', 165: '研究和试验发展', 166: '社会工作',
             167: '社会经济咨询', 168: '科技推广和应用服务业', 169: '科研、医疗、教育用房', 170: '管道和设备安装', 171: '粮油作物和饲料加工设备', 172: '纸、纸制品及印刷品',
             173: '纺织原料、毛皮、被服装具', 174: '纺织设备', 175: '绿化管理', 176: '缝纫、服饰、制革和毛皮加工设备', 177: '航空器及其配套设备', 178: '航空客货运输',
             179: '航空航天工业专用设备', 180: '节能环保工程施工', 181: '装卸搬运', 182: '计算机和办公设备维修', 183: '计算机设备', 184: '计量标准器具及量具、衡器',
             185: '货币处理专用设备', 186: '货币金融服务', 187: '质检技术服务', 188: '资本市场服务', 189: '车辆', 190: '边界勘界和联检专用设备', 191: '运行维护服务',
             192: '通信设备', 193: '通用设备修理', 194: '道路货物运输', 195: '邮政专用设备', 196: '邮政业', 197: '采矿业和制造业服务',
             198: '铁路、船舶、航空航天等运输设备修理', 199: '铁路、道路、隧道和桥梁工程建筑', 200: '铁路运输设备', 201: '防洪除涝设施管理', 202: '陶瓷制品',
             203: '雷达、无线电和卫星导航设备', 204: '非金属矿', 205: '非金属矿物制品工业专用设备', 206: '非金属矿物材料', 207: '食品加工专用设备', 208: '食品及加工盐',
             209: '餐饮业', 210: '饮料、酒精及精制茶', 211: '饮料加工设备', 212: '饲养动物及其产品', 213: '黑色金属冶炼及压延产品', 214: '黑色金属矿'}
        self.industry_dic = {'专业施工': {'大类': '专业施工', '门类': '建筑业'},
                             '专用仪器仪表': {'大类': '专用设备', '门类': '零售批发'},
                             '专用设备修理': {'大类': '金属制品、机械和设备修理业', '门类': '金属制品、机械和设备修理业'},
                             '互联网信息服务': {'大类': '互联网和相关服务', '门类': '信息传输、软件和信息技术服务业'},
                             '互联网安全服务': {'大类': '互联网和相关服务', '门类': '信息传输、软件和信息技术服务业'},
                             '互联网平台': {'大类': '互联网和相关服务', '门类': '信息传输、软件和信息技术服务业'},
                             '互联网接入及相关服务': {'大类': '互联网和相关服务', '门类': '信息传输、软件和信息技术服务业'},
                             '人力资源服务': {'大类': '商务服务业', '门类': '租赁和商务服务业'},
                             '人造原油': {'大类': '炼焦产品、炼油产品', '门类': '零售批发'},
                             '仓储业': {'大类': '装卸搬运和运输代理业', '门类': '交通运输、仓储和邮政业'},
                             '仪器仪表': {'大类': '通用设备', '门类': '零售批发'},
                             '仪器仪表修理': {'大类': '金属制品、机械和设备修理业', '门类': '金属制品、机械和设备修理业'},
                             '会计、审计及税务服务': {'大类': '商务服务业', '门类': '租赁和商务服务业'},
                             '会议、展览及相关服务': {'大类': '商务服务业', '门类': '租赁和商务服务业'},
                             '住宅、商业用房': {'大类': '房屋建筑业', '门类': '建筑业'},
                             '体育场地设施管理': {'大类': '体育', '门类': '文化、体育和娱乐业'},
                             '体育组织': {'大类': '体育', '门类': '文化、体育和娱乐业'},
                             '体育设备': {'大类': '专用设备', '门类': '零售批发'},
                             '保险服务': {'大类': '保险业', '门类': '金融业'},
                             '信息处理和存储支持服务': {'大类': '软件和信息技术服务业', '门类': '信息传输、软件和信息技术服务业'},
                             '信息技术咨询服务': {'大类': '软件和信息技术服务业', '门类': '信息传输、软件和信息技术服务业'},
                             '信息系统集成和物联网技术服务': {'大类': '软件和信息技术服务业', '门类': '信息传输、软件和信息技术服务业'},
                             '修缮工程': {'大类': '修缮工程', '门类': '建筑业'},
                             '健康咨询': {'大类': '商务服务业', '门类': '租赁和商务服务业'},
                             '公路旅客运输': {'大类': '道路运输业', '门类': '交通运输、仓储和邮政业'},
                             '其他专业咨询与调查': {'大类': '商务服务业', '门类': '租赁和商务服务业'},
                             '其他专业技术服务': {'大类': '专业技术服务业', '门类': '科学研究和技术服务业'},
                             '其他交通运输设备': {'大类': '专用设备', '门类': '零售批发'},
                             '其他公共设施管理': {'大类': '公共设施管理业', '门类': '水利、环境和公共设施管理业'},
                             '其他土木工程建筑': {'大类': '土木工程建筑业', '门类': '建筑业'},
                             '其他工程服务': {'大类': '工程服务', '门类': '科学研究和技术服务业'},
                             '其他建筑建材': {'大类': '建筑建材', '门类': '零售批发'},
                             '其他运输业': {'大类': '其他运输业', '门类': '交通运输、仓储和邮政业'},
                             '农业和林业机械': {'大类': '专用设备', '门类': '零售批发'},
                             '农业服务': {'大类': '农林牧副渔服务', '门类': '农林牧副渔服务'},
                             '农产品': {'大类': '农林牧渔业产品', '门类': '零售批发'},
                             '农副食品，动、植物油制品': {'大类': '食品、饮料和烟草原料', '门类': '零售批发'},
                             '出版业': {'大类': '新闻和出版业', '门类': '文化、体育和娱乐业'},
                             '办公消耗用品及类似物品': {'大类': '办公消耗用品及类似物品', '门类': '零售批发'},
                             '办公设备': {'大类': '通用设备', '门类': '零售批发'},
                             '化学原料及化学制品': {'大类': '基础化学品及相关产品', '门类': '零售批发'},
                             '化学纤维': {'大类': '基础化学品及相关产品', '门类': '零售批发'},
                             '化学药品和中药专用设备': {'大类': '专用设备', '门类': '零售批发'},
                             '医疗设备': {'大类': '专用设备', '门类': '零售批发'},
                             '医药品': {'大类': '医药品', '门类': '零售批发'},
                             '卫星传输服务': {'大类': '电信、广播电视和卫星传输服务', '门类': '信息传输、软件和信息技术服务业'},
                             '卫生': {'大类': '卫生', '门类': '卫生和社会工作'},
                             '印刷服务': {'大类': '商务服务业', '门类': '租赁和商务服务业'},
                             '图书和档案': {'大类': '图书和档案', '门类': '零售批发'},
                             '图书档案设备': {'大类': '通用设备', '门类': '零售批发'},
                             '图书馆与档案馆': {'大类': '文化艺术业', '门类': '文化、体育和娱乐业'},
                             '土地管理业': {'大类': '土地管理业', '门类': '水利、环境和公共设施管理业'},
                             '地质勘查': {'大类': '专业技术服务业', '门类': '科学研究和技术服务业'},
                             '地震服务': {'大类': '专业技术服务业', '门类': '科学研究和技术服务业'},
                             '场馆、站港用房': {'大类': '房屋建筑业', '门类': '建筑业'},
                             '城市公共交通运输': {'大类': '道路运输业', '门类': '交通运输、仓储和邮政业'},
                             '塑料制品、半成品及辅料': {'大类': '橡胶、塑料、玻璃和陶瓷制品', '门类': '零售批发'},
                             '天然石料': {'大类': '建筑建材', '门类': '零售批发'},
                             '娱乐设备': {'大类': '专用设备', '门类': '零售批发'},
                             '婚姻服务': {'大类': '居民服务业', '门类': '居民服务、修理和其他服务业'},
                             '安全保护服务': {'大类': '商务服务业', '门类': '租赁和商务服务业'},
                             '安全生产设备': {'大类': '专用设备', '门类': '零售批发'},
                             '家具用具': {'大类': '家具用具', '门类': '零售批发'},
                             '家用电器修理': {'大类': '机动车、电子产品和日用产品修理业', '门类': '居民服务、修理和其他服务业'},
                             '工业、生产用房': {'大类': '房屋建筑业', '门类': '建筑业'},
                             '工业与专业设计及其他专业技术服务': {'大类': '专业技术服务业', '门类': '科学研究和技术服务业'},
                             '工矿工程建筑': {'大类': '土木工程建筑业', '门类': '建筑业'},
                             '工程技术与设计服务': {'大类': '专业技术服务业', '门类': '科学研究和技术服务业'},
                             '工程机械': {'大类': '专用设备', '门类': '零售批发'},
                             '工程监理服务': {'大类': '工程服务', '门类': '科学研究和技术服务业'},
                             '工程评价服务': {'大类': '专业技术服务业', '门类': '科学研究和技术服务业'},
                             '工程造价服务': {'大类': '工程服务', '门类': '科学研究和技术服务业'},
                             '市场调查': {'大类': '商务服务业', '门类': '租赁和商务服务业'},
                             '广告业': {'大类': '商务服务业', '门类': '租赁和商务服务业'},
                             '广播': {'大类': '广播、电视、电影和影视录音制作业', '门类': '文化、体育和娱乐业'},
                             '广播、电视、电影设备': {'大类': '通用设备', '门类': '零售批发'},
                             '广播电视传输服务': {'大类': '电信、广播电视和卫星传输服务', '门类': '信息传输、软件和信息技术服务业'},
                             '废弃资源综合利用业': {'大类': '废弃资源综合利用业', '门类': '废弃资源综合利用业'},
                             '建筑涂料': {'大类': '建筑建材', '门类': '零售批发'},
                             '建筑物、构筑物附属结构': {'大类': '建筑建材', '门类': '零售批发'},
                             '建筑物拆除和场地准备活动': {'大类': '建筑装饰和其他建筑业', '门类': '建筑业'},
                             '建筑装饰和装修业': {'大类': '建筑装饰和其他建筑业', '门类': '建筑业'},
                             '录音制作': {'大类': '广播、电视、电影和影视录音制作业', '门类': '文化、体育和娱乐业'},
                             '影视节目制作': {'大类': '广播、电视、电影和影视录音制作业', '门类': '文化、体育和娱乐业'},
                             '房地产中介服务': {'大类': '房地产业', '门类': '房地产业'},
                             '房地产开发经营': {'大类': '房地产业', '门类': '房地产业'},
                             '房地产租赁经营': {'大类': '房地产业', '门类': '房地产业'},
                             '房屋租赁': {'大类': '租赁业', '门类': '租赁和商务服务业'},
                             '招标代理': {'大类': '专业技术服务业', '门类': '科学研究和技术服务业'},
                             '探矿、采矿、选矿和造块设备': {'大类': '专用设备', '门类': '零售批发'},
                             '政法、检测专用设备': {'大类': '专用设备', '门类': '零售批发'},
                             '教育服务': {'大类': '教育服务', '门类': '教育'},
                             '教育设备': {'大类': '专用设备', '门类': '零售批发'},
                             '文体设备和用品出租': {'大类': '租赁业', '门类': '租赁和商务服务业'},
                             '文物及非物质文化遗产保护': {'大类': '文化艺术业', '门类': '文化、体育和娱乐业'},
                             '文物和陈列品': {'大类': '文物和陈列品', '门类': '零售批发'},
                             '文艺创作与表演': {'大类': '文化艺术业', '门类': '文化、体育和娱乐业'},
                             '文艺设备': {'大类': '专用设备', '门类': '零售批发'},
                             '新闻业': {'大类': '新闻和出版业', '门类': '文化、体育和娱乐业'},
                             '旅行社及相关服务': {'大类': '商务服务业', '门类': '租赁和商务服务业'},
                             '日杂用品': {'大类': '日杂用品', '门类': '零售批发'},
                             '有色金属冶炼及压延产品': {'大类': '建筑建材', '门类': '零售批发'},
                             '有色金属矿': {'大类': '矿与矿物', '门类': '零售批发'},
                             '木材、板材等': {'大类': '建筑建材', '门类': '零售批发'},
                             '木材采集和加工设备': {'大类': '专用设备', '门类': '零售批发'},
                             '机械设备': {'大类': '通用设备', '门类': '零售批发'},
                             '机械设备经营租赁': {'大类': '租赁业', '门类': '租赁和商务服务业'},
                             '林业产品': {'大类': '农林牧渔业产品', '门类': '零售批发'},
                             '林业服务': {'大类': '农林牧副渔服务', '门类': '农林牧副渔服务'},
                             '架线和管道工程建筑': {'大类': '土木工程建筑业', '门类': '建筑业'},
                             '核工业专用设备': {'大类': '专用设备', '门类': '零售批发'},
                             '橡胶制品': {'大类': '橡胶、塑料、玻璃和陶瓷制品', '门类': '零售批发'},
                             '殡葬服务': {'大类': '居民服务业', '门类': '居民服务、修理和其他服务业'},
                             '殡葬设备及用品': {'大类': '专用设备', '门类': '零售批发'},
                             '气象服务': {'大类': '专业技术服务业', '门类': '科学研究和技术服务业'},
                             '水上交通运输设备': {'大类': '专用设备', '门类': '零售批发'},
                             '水上运输业': {'大类': '水上运输业', '门类': '交通运输、仓储和邮政业'},
                             '水利和水运工程建筑': {'大类': '土木工程建筑业', '门类': '建筑业'},
                             '水工机械': {'大类': '专用设备', '门类': '零售批发'},
                             '水文服务': {'大类': '水利管理业', '门类': '水利、环境和公共设施管理业'},
                             '水资源管理': {'大类': '水利管理业', '门类': '水利、环境和公共设施管理业'},
                             '污水处理及其再生利用': {'大类': '水的生产和供应业', '门类': '电力、热力、燃气及水生产和供应业'},
                             '汽车、摩托车修理与维护': {'大类': '机动车、电子产品和日用产品修理业', '门类': '居民服务、修理和其他服务业'},
                             '法律服务': {'大类': '商务服务业', '门类': '租赁和商务服务业'},
                             '洗染服务': {'大类': '居民服务业', '门类': '居民服务、修理和其他服务业'},
                             '测绘地理信息服务': {'大类': '专业技术服务业', '门类': '科学研究和技术服务业'},
                             '海洋仪器设备': {'大类': '专用设备', '门类': '零售批发'},
                             '海洋工程建筑': {'大类': '土木工程建筑业', '门类': '建筑业'},
                             '海洋服务': {'大类': '专业技术服务业', '门类': '科学研究和技术服务业'},
                             '消防设备': {'大类': '专用设备', '门类': '零售批发'},
                             '清洁服务': {'大类': '其他服务业', '门类': '居民服务、修理和其他服务业'},
                             '渔业产品': {'大类': '农林牧渔业产品', '门类': '零售批发'},
                             '渔业服务': {'大类': '农林牧副渔服务', '门类': '农林牧副渔服务'},
                             '炼焦和金属冶炼轧制设备': {'大类': '专用设备', '门类': '零售批发'},
                             '烟草加工设备': {'大类': '专用设备', '门类': '零售批发'},
                             '热力生产和供应': {'大类': '电力、热力生产和供应业', '门类': '电力、热力、燃气及水生产和供应业'},
                             '焦炭及其副产品': {'大类': '炼焦产品、炼油产品', '门类': '零售批发'},
                             '煤炭采选产品': {'大类': '矿与矿物', '门类': '零售批发'},
                             '燃气生产和供应业': {'大类': '燃气生产和供应业', '门类': '电力、热力、燃气及水生产和供应业'},
                             '物业管理': {'大类': '房地产业', '门类': '房地产业'},
                             '特种用途动、植物': {'大类': '农林牧渔业产品', '门类': '零售批发'},
                             '环保咨询': {'大类': '商务服务业', '门类': '租赁和商务服务业'},
                             '环境与生态监测检测服务': {'大类': '专业技术服务业', '门类': '科学研究和技术服务业'},
                             '环境污染防治设备': {'大类': '专用设备', '门类': '零售批发'},
                             '环境治理业': {'大类': '生态保护和环境治理业', '门类': '水利、环境和公共设施管理业'},
                             '玻璃及其制品': {'大类': '橡胶、塑料、玻璃和陶瓷制品', '门类': '零售批发'},
                             '理发及美容服务': {'大类': '居民服务业', '门类': '居民服务、修理和其他服务业'},
                             '生态保护': {'大类': '生态保护和环境治理业', '门类': '水利、环境和公共设施管理业'},
                             '电信': {'大类': '电信、广播电视和卫星传输服务', '门类': '信息传输、软件和信息技术服务业'},
                             '电力、城市燃气、蒸汽和热水、水': {'大类': '电力、城市燃气、蒸汽和热水、水', '门类': '零售批发'},
                             '电力供应': {'大类': '电力、热力生产和供应业', '门类': '电力、热力、燃气及水生产和供应业'},
                             '电力工业专用设备': {'大类': '专用设备', '门类': '零售批发'},
                             '电力工程施工': {'大类': '土木工程建筑业', '门类': '建筑业'},
                             '电力生产': {'大类': '电力、热力生产和供应业', '门类': '电力、热力、燃气及水生产和供应业'},
                             '电子和通信测量仪器': {'大类': '通用设备', '门类': '零售批发'},
                             '电工、电子专用生产设备': {'大类': '专用设备', '门类': '零售批发'},
                             '电影放映': {'大类': '广播、电视、电影和影视录音制作业', '门类': '文化、体育和娱乐业'},
                             '电气安装': {'大类': '建筑安装业', '门类': '建筑业'},
                             '电气设备': {'大类': '通用设备', '门类': '零售批发'},
                             '电气设备修理': {'大类': '金属制品、机械和设备修理业', '门类': '金属制品、机械和设备修理业'},
                             '畜牧业服务': {'大类': '农林牧副渔服务', '门类': '农林牧副渔服务'},
                             '监控设备': {'大类': '通用设备', '门类': '零售批发'},
                             '石油制品': {'大类': '炼焦产品、炼油产品', '门类': '零售批发'},
                             '石油和化学工业专用设备': {'大类': '专用设备', '门类': '零售批发'},
                             '石油和天然气开采产品': {'大类': '矿与矿物', '门类': '零售批发'},
                             '石油天然气开采专用设备': {'大类': '专用设备', '门类': '零售批发'},
                             '研究和试验发展': {'大类': '研究和试验发展', '门类': '科学研究和技术服务业'},
                             '社会工作': {'大类': '社会工作', '门类': '卫生和社会工作'},
                             '社会经济咨询': {'大类': '商务服务业', '门类': '租赁和商务服务业'},
                             '科技推广和应用服务业': {'大类': '科技推广和应用服务业', '门类': '科学研究和技术服务业'},
                             '科研、医疗、教育用房': {'大类': '房屋建筑业', '门类': '建筑业'},
                             '管道和设备安装': {'大类': '建筑安装业', '门类': '建筑业'},
                             '粮油作物和饲料加工设备': {'大类': '专用设备', '门类': '零售批发'},
                             '纸、纸制品及印刷品': {'大类': '纸、纸制品及印刷品', '门类': '零售批发'},
                             '纺织原料、毛皮、被服装具': {'大类': '纺织原料、毛皮、被服装具', '门类': '零售批发'},
                             '纺织设备': {'大类': '专用设备', '门类': '零售批发'},
                             '绿化管理': {'大类': '公共设施管理业', '门类': '水利、环境和公共设施管理业'},
                             '缝纫、服饰、制革和毛皮加工设备': {'大类': '专用设备', '门类': '零售批发'},
                             '航空器及其配套设备': {'大类': '专用设备', '门类': '零售批发'},
                             '航空客货运输': {'大类': '航空运输业', '门类': '交通运输、仓储和邮政业'},
                             '航空航天工业专用设备': {'大类': '专用设备', '门类': '零售批发'},
                             '节能环保工程施工': {'大类': '土木工程建筑业', '门类': '建筑业'},
                             '装卸搬运': {'大类': '装卸搬运和运输代理业', '门类': '交通运输、仓储和邮政业'},
                             '计算机和办公设备维修': {'大类': '机动车、电子产品和日用产品修理业', '门类': '居民服务、修理和其他服务业'},
                             '计算机设备': {'大类': '通用设备', '门类': '零售批发'},
                             '计量标准器具及量具、衡器': {'大类': '通用设备', '门类': '零售批发'},
                             '货币处理专用设备': {'大类': '专用设备', '门类': '零售批发'},
                             '货币金融服务': {'大类': '货币金融服务', '门类': '金融业'},
                             '质检技术服务': {'大类': '专业技术服务业', '门类': '科学研究和技术服务业'},
                             '资本市场服务': {'大类': '资本市场服务', '门类': '金融业'},
                             '车辆': {'大类': '通用设备', '门类': '零售批发'},
                             '边界勘界和联检专用设备': {'大类': '专用设备', '门类': '零售批发'},
                             '运行维护服务': {'大类': '软件和信息技术服务业', '门类': '信息传输、软件和信息技术服务业'},
                             '通信设备': {'大类': '通用设备', '门类': '零售批发'},
                             '通用设备修理': {'大类': '金属制品、机械和设备修理业', '门类': '金属制品、机械和设备修理业'},
                             '道路货物运输': {'大类': '道路运输业', '门类': '交通运输、仓储和邮政业'},
                             '邮政专用设备': {'大类': '专用设备', '门类': '零售批发'},
                             '邮政业': {'大类': '邮政业', '门类': '交通运输、仓储和邮政业'},
                             '采矿业和制造业服务': {'大类': '采矿业和制造业服务', '门类': '农林牧副渔服务'},
                             '铁路、船舶、航空航天等运输设备修理': {'大类': '金属制品、机械和设备修理业', '门类': '金属制品、机械和设备修理业'},
                             '铁路、道路、隧道和桥梁工程建筑': {'大类': '土木工程建筑业', '门类': '建筑业'},
                             '铁路运输设备': {'大类': '专用设备', '门类': '零售批发'},
                             '防洪除涝设施管理': {'大类': '水利管理业', '门类': '水利、环境和公共设施管理业'},
                             '陶瓷制品': {'大类': '橡胶、塑料、玻璃和陶瓷制品', '门类': '零售批发'},
                             '雷达、无线电和卫星导航设备': {'大类': '通用设备', '门类': '零售批发'},
                             '非金属矿': {'大类': '矿与矿物', '门类': '零售批发'},
                             '非金属矿物制品工业专用设备': {'大类': '专用设备', '门类': '零售批发'},
                             '非金属矿物材料': {'大类': '建筑建材', '门类': '零售批发'},
                             '食品加工专用设备': {'大类': '专用设备', '门类': '零售批发'},
                             '食品及加工盐': {'大类': '食品、饮料和烟草原料', '门类': '零售批发'},
                             '餐饮业': {'大类': '餐饮业', '门类': '住宿和餐饮业'},
                             '饮料、酒精及精制茶': {'大类': '食品、饮料和烟草原料', '门类': '零售批发'},
                             '饮料加工设备': {'大类': '专用设备', '门类': '零售批发'},
                             '饲养动物及其产品': {'大类': '农林牧渔业产品', '门类': '零售批发'},
                             '黑色金属冶炼及压延产品': {'大类': '建筑建材', '门类': '零售批发'},
                             '黑色金属矿': {'大类': '矿与矿物', '门类': '零售批发'}}
        self.sess = tf.Session(graph=tf.Graph())
        self.get_model()

        with open(os.path.dirname(__file__)+'/industry_rule_kw_json/tw_industry_keyword_org/tw_industry_keyword_org.json', 'r',
                  encoding='utf-8') as fp1:
            self.json_data_industry = json.load(fp1)
        with open(os.path.dirname(__file__)+'/industry_rule_kw_json/tw_company_classification_keyword/tw_company_classification_keyword.json', 'r',
                  encoding='utf-8') as fp2:
            self.json_data_company = json.load(fp2)
        with open(os.path.dirname(__file__)+'/industry_rule_kw_json/tw_custom_keyword/tw_custom_keyword.json', 'r', encoding='utf-8') as fp3:
            self.json_data_custom = json.load(fp3)


    def get_model(self):
        with self.sess.as_default() as sess:
            with self.sess.graph.as_default():
                meta_graph_def = tf.saved_model.loader.load(sess,
                                                            tags=['serve'],
                                                            export_dir=os.path.dirname(__file__)+'/industry_model')
                signature_key = tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
                signature_def = meta_graph_def.signature_def

                self.title = sess.graph.get_tensor_by_name(signature_def[signature_key].inputs['title'].name)
                self.project = sess.graph.get_tensor_by_name(signature_def[signature_key].inputs['project'].name)
                self.product = sess.graph.get_tensor_by_name(signature_def[signature_key].inputs['product'].name)
                self.outputs = sess.graph.get_tensor_by_name(signature_def[signature_key].outputs['outputs'].name)

    def text2array(self, text, tenderee='', maxSententLen=20):
        tenderee = tenderee.replace('(', '（').replace(')', '）')
        text = text.replace('(', '（').replace(')', '）')
        text = re.sub(
            '(废标|终止|综?合?评审|评标|开标|资审|履约|验收|成交|中标人?|中选人?|单一来源|合同|候选人|结果|变更|更正|答疑|澄清|意向|需求|采购|招标|询比?价|磋商|谈判|比选|比价|竞价|议价)的?(公告|预告|公示)?|关于为?|选取|定点|直接|邀请函?|通知书?|备案|公开|公示|公告|记录|竞争性',
            '', text)
        text = text.replace(tenderee, '')
        text = ' ' if text=="" else text
        words_docs_list = selffool.cut(text)
        words_docs_list = [[it for it in l if re.search('^[\u4e00-\u9fa5]+$', it)][-maxSententLen:] for l in words_docs_list]
        array = embedding(words_docs_list, shape=(len(words_docs_list), maxSententLen, 128))
        return array

    def process(self, title, project, product, tenderee):
        return self.text2array(title, tenderee), self.text2array(project, tenderee), self.text2array(product)

    def predict_model(self, title, project, product, tenderee=''):
        title_array, project_array, product_array = self.process(title, project, product, tenderee)
        rs = self.sess.run(self.outputs,
                           feed_dict={
                               self.title:title_array,
                               self.project:project_array,
                               self.product:product_array
                            }
                           )
        pred = np.argmax(rs[0])
        return self.id2lb[pred], rs[0][pred]
        # # 返回top2 结果
        # pred_list = np.argsort(-rs[0])
        # return self.id2lb[pred_list[0]], self.id2lb[pred_list[1]], rs[0][pred_list[0]], rs[0][pred_list[1]]

    def predict_rule(self, doctitle, tenderee, win_tenderer, project_name, product):
        doctitle = doctitle if doctitle else ''
        tenderee = tenderee if tenderee else ''
        win_tenderer = win_tenderer if win_tenderer else ''
        project_name = project_name if project_name else ''
        product = product if product else ''

        text_ind = (doctitle + project_name + product).replace(tenderee, '')
        text_com = win_tenderer

        length_ind_text = len(text_ind) + 1
        length_com_text = len(text_com) + 1
        # print(text)

        dic_res = {}  # 行业分类字典
        score_lst = []  # 得分列表
        word_lst = []  # 关键词列表

        # 主要内容关键词
        if text_ind:
            # logging.info("data_ind%s"%str(_json_data_industry[0]))
            for data_industry in self.json_data_industry:
                industry = data_industry['xiaolei']
                key_word = data_industry['key_word']
                key_word_2 = data_industry['key_word2']
                power = float(data_industry['power']) if data_industry['power'] else 0
                this_score = power * (text_ind.count(key_word) * len(key_word) / length_ind_text)

                if key_word_2:
                    # key_word_compose = key_word + "+" + key_word_2
                    if text_ind.count(key_word_2) == 0:
                        this_score = 0

                if this_score > 0:
                    # print(industry,key_word,this_score)
                    if industry in dic_res.keys():
                        dic_res[industry] += this_score
                    else:
                        dic_res[industry] = this_score

                    if key_word not in word_lst:
                        word_lst.append(key_word)

        # 供应商关键词
        if text_com:

            for data_company in self.json_data_company:
                industry = data_company['industry_type']
                key_word = data_company['company_word']
                power = float(data_company['industry_rate']) if data_company['industry_rate'] else 0
                this_score = power * (text_com.count(key_word) * len(key_word) / length_com_text)

                if this_score > 0:
                    # print(industry,key_word,this_score)
                    if industry in dic_res.keys():
                        dic_res[industry] += this_score
                    else:
                        dic_res[industry] = this_score

                    if key_word not in word_lst:
                        word_lst.append(key_word)

        # 自定义关键词
        if text_ind:

            custom_ind = [
                ['tenderee', '医院|疾病预防', ['设备', '系统', '器'], '医疗设备'],
                ['tenderee', '学校|大学|小学|中学|学院|幼儿园', ['设备', '器'], '教育设备'],
                ['tenderee', '学校|大学|小学|中学|学院|幼儿园|医院', ['工程'], '科研、医疗、教育用房'],
                ['tenderee', '供电局|电网|国网|电力|电厂|粤电', ['设备', '器', '物资'], '电力工业专用设备'],
                ['tenderee', '公安|法院|检察院', ['设备', '器'], '政法、检测专用设备'],
                ['tenderee', '^中铁|^中交|^中建|中国建筑', ['材料'], '其他建筑建材'],
                ['doctextcon', '信息技术服务|系统开发|信息化|信息系统', ['监理'], '信息技术咨询服务'],
                ['doctextcon', '工程', ['消防'], '专业施工'],
                ['doctextcon', '铁路|航空|船舶|航天|广铁', ['维修'], '铁路、船舶、航空航天等运输设备修理'],
                ['doctextcon', '设备|仪|器', ['租赁'], '机械设备经营租赁'],
                ['doctextcon', '交通|铁路|公路|道路|桥梁', ['工程'], '铁路、道路、隧道和桥梁工程建筑'],
                ['win_tenderer', '电力', ['设备', '器'], '电力工业专用设备'],
                ['win_tenderer', '信息|网络科技', ['系统'], '信息系统集成和物联网技术服务'],
                ['tenderee,doctextcon', '铁路|广铁|铁道', ['设备', '器', '物资', '材料', '铁路'], '铁路运输设备'],
            ]

            for data_custom in self.json_data_custom:
                industry_custom = data_custom['industry']
                key_word = data_custom['company_word']
                power = float(data_custom['industry_rate'])
                for k in range(len(custom_ind)):
                    subject = ''
                    if 'tenderee' in custom_ind[k][0]:
                        subject += tenderee
                    if 'win_tenderer' in custom_ind[k][0]:
                        subject += win_tenderer
                    if 'doctextcon' in custom_ind[k][0]:
                        subject += text_ind

                    ptn = custom_ind[k][1]
                    # print('ptn',ptn)
                    if re.search(ptn, subject) and industry_custom in custom_ind[k][2]:
                        industry = custom_ind[k][3]
                    else:
                        continue

                    this_score = power * (text_ind.count(key_word) * len(key_word) / len(subject))

                    if this_score > 0:
                        # print(industry,key_word,this_score)
                        if industry in dic_res.keys():
                            dic_res[industry] += this_score
                        else:
                            dic_res[industry] = this_score

                        if key_word not in word_lst:
                            word_lst.append(key_word)
        sort_res = sorted(dic_res.items(), key=lambda x: x[1], reverse=True)
        lst_res = [s[0] for s in sort_res]
        score_lst = [str(round(float(s[1]), 2)) for s in sort_res]
        if len(lst_res) > 0:
            return lst_res, score_lst, word_lst
        else:
            return [""], [], []

    def predict_merge(self, pinmu_type, industry_lst):
        '''
        通过一系列规则最终决定使用模型还是规则的结果
        :param pinmu_type: 模型预测类别
        :param industry_lst: 规则预测类别列表
        :return:
        '''
        industry_type = industry_lst[0]
        if industry_type == "":
            return pinmu_type
        if industry_type == '专用设备修理' and re.search('修理|维修|装修|修缮', pinmu_type):
            final_type = pinmu_type
        elif industry_type == '其他土木工程建筑' and re.search('工程|建筑|用房|施工|安装|质检|其他专业咨询与调查', pinmu_type):
            final_type = pinmu_type
        elif pinmu_type == '专用设备修理' and re.search('工程|修理', industry_type):
            final_type = industry_type
        elif pinmu_type == '信息系统集成和物联网技术服务' and re.search('卫星传输|信息处理和存储支持服务|信息技术咨询服务|运行维护服务|其他专业技术服务|医疗设备|医药品',
                                                          industry_type):
            final_type = industry_type
        elif industry_type == '仪器仪表' and re.search('仪器|器具|医疗设备', pinmu_type):
            final_type = pinmu_type
        elif industry_type == '医药品' and re.search('医疗设备', pinmu_type):
            final_type = pinmu_type
        elif industry_type == '医药品' and re.search('医疗设备', pinmu_type):
            final_type = pinmu_type
        elif re.search('设备', industry_type) and re.search('修理|维修', pinmu_type):
            final_type = pinmu_type
        elif industry_type == '社会工作' and re.search('工程', pinmu_type):
            final_type = pinmu_type
        elif industry_type == '信息系统集成和物联网技术服务' and re.search('信息处理|设备', pinmu_type):
            final_type = pinmu_type
        elif industry_type == '研究和试验发展' and re.search('其他专业咨询与调查|质检技术服务|信息系统集成|其他工程服务', pinmu_type):
            final_type = pinmu_type
        elif industry_type == '其他专业咨询与调查' and re.search('工程造价服务', pinmu_type):
            final_type = pinmu_type
        elif industry_type == '广告业' and re.search('印刷服务|影视节目制作|信息系统', pinmu_type):
            final_type = pinmu_type
        elif industry_type == '清洁服务' and re.search('工程|环境污染防治设备|修理', pinmu_type):
            final_type = pinmu_type
        elif industry_type == '其他公共设施管理' and re.search('信息系统', pinmu_type):
            final_type = pinmu_type
        elif industry_type == '其他专业技术服务' and re.search('工程技术与设计服务|质检技术服务|环境与生态监测检测服务', pinmu_type):
            final_type = pinmu_type
        elif industry_type == '机械设备经营租赁' and re.search('电信', pinmu_type):
            final_type = pinmu_type
        elif industry_type == '货币金融服务' and re.search('信息系统集成和物联网技术服务', pinmu_type):
            final_type = pinmu_type
        elif industry_type == '体育场地设施管理' and re.search('体育设备', pinmu_type):
            final_type = pinmu_type
        elif industry_type == '安全保护服务' and re.search('信息系统|监控设备|互联网安全服务', pinmu_type):
            final_type = pinmu_type
        elif industry_type == '互联网接入及相关服务' and re.search('通信设备', pinmu_type):
            final_type = pinmu_type
        elif industry_type == '卫生' and re.search('医疗设备|信息系统', pinmu_type):
            final_type = pinmu_type
        elif pinmu_type == '研究和试验发展' and re.search('其他工程服务', industry_type):
            final_type = industry_type
        elif pinmu_type == '办公设备' and re.search('教育设备', industry_type):
            final_type = industry_type
        elif re.search('车辆|机械设备经营租赁', pinmu_type) and re.search('公路旅客运输', industry_type):
            final_type = industry_type
        elif len(industry_lst) > 1 and pinmu_type == industry_lst[1] and re.search('会计|法律|物业|家具|印刷|互联网安全',
                                                                                   industry_type) == None \
                and re.search('其他|人力资源服务', pinmu_type) == None:
            final_type = pinmu_type
        elif industry_type != "":
            final_type = industry_type
        else:
            final_type = pinmu_type
        return final_type

    def predict(self, title, project, product, prem):
        def get_ree_win(prem):
            tenderee = ""
            win_tenderer = ""
            try:
                for v in prem[0]['prem'].values():
                    for link in v['roleList']:
                        if link['role_name'] == 'tenderee' and tenderee == "":
                            tenderee = link['role_text']
                        elif link['role_name'] == 'win_tenderer' and win_tenderer == "":
                            win_tenderer = link['role_text']
            except Exception as e:
                print('解析prem 获取招标人、中标人出错')
            return tenderee, win_tenderer
        tenderee, win_tenderer = get_ree_win(prem)
        result_model, prob = self.predict_model(title, project, product, tenderee)
        industry_lst, score_lst, word_lst = self.predict_rule(title, tenderee, win_tenderer, project, product)
        final_type = self.predict_merge(result_model, industry_lst)
        # print('模型：%s；规则：%s；最终：%s'%(result_model, industry_lst[0], final_type))
        # return {'industry': final_type}
        return {'industry': {
                            'class_name': final_type,
                            'subclass': self.industry_dic[final_type]['大类'],
                            'class': self.industry_dic[final_type]['门类']
                            }
                }



def getSavedModel():
    #predictor = FormPredictor()
    graph = tf.Graph()
    with graph.as_default():
        model = tf.keras.models.load_model("../form/model/model_form.model_item.hdf5",custom_objects={"precision":precision,"recall":recall,"f1_score":f1_score})
        
        #print(tf.graph_util.remove_training_nodes(model))
        tf.saved_model.simple_save(
          tf.keras.backend.get_session(),
          "./h5_savedmodel/",
          inputs={"image": model.input},
          outputs={"scores": model.output}
        )
        
def getBiLSTMCRFModel(MAX_LEN,vocab,EMBED_DIM,BiRNN_UNITS,chunk_tags,weights):
    '''
    model = models.Sequential()
    model.add(layers.Embedding(len(vocab), EMBED_DIM, mask_zero=True))  # Random embedding
    model.add(layers.Bidirectional(layers.LSTM(BiRNN_UNITS // 2, return_sequences=True)))
    crf = CRF(len(chunk_tags), sparse_target=True)
    model.add(crf)
    model.summary()
    model.compile('adam', loss=crf.loss_function, metrics=[crf.accuracy])
    return model
    '''
    input = layers.Input(shape=(None,),dtype="int32")
    if weights is not None:
        embedding = layers.embeddings.Embedding(len(vocab),EMBED_DIM,mask_zero=True,weights=[weights],trainable=True)(input)
    else:
        embedding = layers.embeddings.Embedding(len(vocab),EMBED_DIM,mask_zero=True)(input)
    bilstm = layers.Bidirectional(layers.LSTM(BiRNN_UNITS//2,return_sequences=True))(embedding)
    bilstm_dense = layers.TimeDistributed(layers.Dense(len(chunk_tags)))(bilstm)
    crf = CRF(len(chunk_tags),sparse_target=True)
    crf_out = crf(bilstm_dense)
    model = models.Model(input=[input],output = [crf_out])
    model.summary()
    model.compile(optimizer = 'adam', loss = crf.loss_function, metrics = [crf.accuracy])
    return model


import h5py
def h5_to_graph(sess,graph,h5file):
    
    f = h5py.File(h5file,'r')   #打开h5文件 
    def getValue(v):
        _value = f["model_weights"]
        list_names = str(v.name).split("/")
        for _index in range(len(list_names)):
            print(v.name)
            if _index==1:
                _value = _value[list_names[0]]
            _value = _value[list_names[_index]]
        return _value.value
            
    def _load_attributes_from_hdf5_group(group, name):
        """Loads attributes of the specified name from the HDF5 group.
    
        This method deals with an inherent problem
        of HDF5 file which is not able to store
        data larger than HDF5_OBJECT_HEADER_LIMIT bytes.
    
        # Arguments
            group: A pointer to a HDF5 group.
            name: A name of the attributes to load.
    
        # Returns
            data: Attributes data.
        """
        if name in group.attrs:
            data = [n.decode('utf8') for n in group.attrs[name]]
        else:
            data = []
            chunk_id = 0
            while ('%s%d' % (name, chunk_id)) in group.attrs:
                data.extend([n.decode('utf8')
                            for n in group.attrs['%s%d' % (name, chunk_id)]])
                chunk_id += 1
        return data
    
    def readGroup(gr,parent_name,data):
        for subkey in gr:
            print(subkey)
            if parent_name!=subkey:
                if parent_name=="":
                    _name = subkey
                else:
                    _name = parent_name+"/"+subkey
            else:
                _name = parent_name
            if str(type(gr[subkey]))=="<class 'h5py._hl.group.Group'>":
                readGroup(gr[subkey],_name,data)
            else:
                data.append([_name,gr[subkey].value])
                print(_name,gr[subkey].shape)
                
    
    layer_names = _load_attributes_from_hdf5_group(f["model_weights"], 'layer_names')
    list_name_value = []
    readGroup(f["model_weights"], "", list_name_value)
    '''
    for k, name in enumerate(layer_names):
        g = f["model_weights"][name]
        weight_names = _load_attributes_from_hdf5_group(g, 'weight_names')
        #weight_values = [np.asarray(g[weight_name]) for weight_name in weight_names]
        for weight_name in weight_names:
            list_name_value.append([weight_name,np.asarray(g[weight_name])])
    '''
    for name_value in list_name_value:
        name = name_value[0]
        '''
        if re.search("dense",name) is not None:
            name = name[:7]+"_1"+name[7:]
        '''
        value = name_value[1]
        print(name,graph.get_tensor_by_name(name),np.shape(value))
        sess.run(tf.assign(graph.get_tensor_by_name(name),value))


def initialize_uninitialized(sess):
    global_vars          = tf.global_variables()
    is_not_initialized   = sess.run([tf.is_variable_initialized(var) for var in global_vars])
    not_initialized_vars = [v for (v, f) in zip(global_vars, is_not_initialized) if not f]
    
    adam_vars = []
    for _vars in not_initialized_vars:
        if re.search("Adam",_vars.name) is not None:
            adam_vars.append(_vars)
 
    print([str(i.name) for i in adam_vars]) # only for testing
    if len(adam_vars):
        sess.run(tf.variables_initializer(adam_vars))
    
      
def save_codename_model():
    # filepath = "../projectCode/models/model_project_"+str(60)+"_"+str(200)+".hdf5"
    filepath = "../../dl_dev/projectCode/models_tf/59-L0.471516189943-F0.8802154826344823-P0.8789179683459191-R0.8815168335321886/model.ckpt"
    vocabpath = "../projectCode/models/vocab.pk"
    classlabelspath = "../projectCode/models/classlabels.pk"
    # vocab = load(vocabpath)
    # class_labels = load(classlabelspath)
    w2v_matrix = load('codename_w2v_matrix.pk')
    graph = tf.get_default_graph()
    with graph.as_default() as g:
        ''''''
        # model = getBiLSTMCRFModel(None, vocab, 60, 200, class_labels,weights=None)
        #model = models.load_model(filepath,custom_objects={'precision':precision,'recall':recall,'f1_score':f1_score,"CRF":CRF,"loss":CRF.loss_function})
        
        sess = tf.Session(graph=g)
        # sess = tf.keras.backend.get_session()
        char_input, logits, target, keepprob, length, crf_loss, trans, train_op = BiLSTM_CRF_tfmodel(sess, w2v_matrix)
        #with sess.as_default():
        sess.run(tf.global_variables_initializer())
        # print(sess.run("time_distributed_1/kernel:0"))
        # model.load_weights(filepath)
        saver = tf.train.Saver()
        saver.restore(sess, filepath)

        # print("logits",sess.run(logits))
        
        # print("#",sess.run("time_distributed_1/kernel:0"))

        # x = load("codename_x.pk")
        #y = model.predict(x)
        # y = sess.run(model.output,feed_dict={model.input:x})
        
        # for item in np.argmax(y,-1):
        #     print(item)
        tf.saved_model.simple_save(
                                    sess,
                                    "./codename_savedmodel_tf/",
                                    inputs={"inputs": char_input,
                                            "inputs_length":length,
                                            'keepprob':keepprob},
                                    outputs={"logits": logits,
                                             "trans":trans}
        )
        
    
def save_role_model():
    '''
    @summary: 保存model为savedModel，部署到PAI平台上调用
    '''
    model_role = PREMPredict().model_role
    with model_role.graph.as_default():
        model = model_role.getModel()
        sess = tf.Session(graph=model_role.graph)
        print(type(model.input))
        
        sess.run(tf.global_variables_initializer())
        h5_to_graph(sess, model_role.graph, model_role.model_role_file)
        model = model_role.getModel()
        
        tf.saved_model.simple_save(sess,
                                   "./role_savedmodel/",
                                   inputs={"input0":model.input[0],
                                           "input1":model.input[1],
                                           "input2":model.input[2]},
                                   outputs={"outputs":model.output}
                                   )


def save_money_model():
    model_file = os.path.dirname(__file__)+"/../money/models/model_money_word.h5"
    graph = tf.Graph()
    with graph.as_default():

        sess = tf.Session(graph=graph)

        with sess.as_default():
            # model = model_money.getModel()
            # model.summary()
            # sess.run(tf.global_variables_initializer())
            # h5_to_graph(sess, model_money.graph, model_money.model_money_file)

            model = models.load_model(model_file,custom_objects={'precision':precision,'recall':recall,'f1_score':f1_score})
            model.summary()
            print(model.weights)
            tf.saved_model.simple_save(sess,
                                       "./money_savedmodel2/",
                                       inputs = {"input0":model.input[0],
                                                 "input1":model.input[1],
                                                 "input2":model.input[2]},
                                       outputs = {"outputs":model.output}
                                       )
    

def save_person_model():
    model_person = EPCPredict().model_person
    with model_person.graph.as_default():
        
        x = load("person_x.pk")
        _data = np.transpose(np.array(x),(1,0,2,3))
        model = model_person.getModel()
        
        sess = tf.Session(graph=model_person.graph)
        with sess.as_default():
            
            sess.run(tf.global_variables_initializer())
            model_person.load_weights()
        
        
        #h5_to_graph(sess, model_person.graph, model_person.model_person_file)
        
        predict_y = sess.run(model.output,feed_dict={model.input[0]:_data[0],model.input[1]:_data[1]})
        #predict_y = model.predict([_data[0],_data[1]])
        print(np.argmax(predict_y,-1))
        
        tf.saved_model.simple_save(sess,
                                   "./person_savedmodel/",
                                   inputs={"input0":model.input[0],
                                           "input1":model.input[1]},
                                   outputs = {"outputs":model.output})
    
def save_form_model():
    model_form = FormPredictor()
    with model_form.graph.as_default():
        model = model_form.getModel("item")
        sess = tf.Session(graph=model_form.graph)
        sess.run(tf.global_variables_initializer())
        h5_to_graph(sess, model_form.graph, model_form.model_file_item)
        tf.saved_model.simple_save(sess,
                                   "./form_savedmodel/",
                                   inputs={"inputs":model.input},
                                   outputs = {"outputs":model.output})
    
def save_codesplit_model():
    filepath_code = "../../dl_dev/projectCode/models/model_code.hdf5"
    
    
    graph = tf.Graph()
    with graph.as_default():
        model_code = models.load_model(filepath_code, custom_objects={'precision':precision,'recall':recall,'f1_score':f1_score})
        sess = tf.Session()
        sess.run(tf.global_variables_initializer())
        h5_to_graph(sess, graph, filepath_code)
        tf.saved_model.simple_save(sess,
                                   "./codesplit_savedmodel/",
                                   inputs={"input0":model_code.input[0],
                                           "input1":model_code.input[1],
                                           "input2":model_code.input[2]},
                                   outputs={"outputs":model_code.output})

def save_timesplit_model():
    filepath = '../time/model_label_time_classify.model.hdf5'
    with tf.Graph().as_default() as graph:
        time_model = models.load_model(filepath, custom_objects={'precision': precision, 'recall': recall, 'f1_score': f1_score})
        with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())
            h5_to_graph(sess, graph, filepath)
            tf.saved_model.simple_save(sess,
                                       "./timesplit_model/",
                                       inputs={"input0":time_model.input[0],
                                               "input1":time_model.input[1]},
                                       outputs={"outputs":time_model.output})


if __name__=="__main__":
    #save_role_model()
    # save_codename_model()
    # save_money_model()
    #save_person_model()
    #save_form_model()
    #save_codesplit_model()
    # save_timesplit_model()
    '''
    # with tf.Session(graph=tf.Graph()) as sess:
    #     from tensorflow.python.saved_model import tag_constants
    #     meta_graph_def = tf.saved_model.loader.load(sess, [tag_constants.SERVING], "./person_savedModel")
    #     graph = tf.get_default_graph()
    #     signature_key = tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
    #     signature = meta_graph_def.signature_def
    #     input0 = sess.graph.get_tensor_by_name(signature[signature_key].inputs["input0"].name)
    #     input1 = sess.graph.get_tensor_by_name(signature[signature_key].inputs["input1"].name)
    #     outputs = sess.graph.get_tensor_by_name(signature[signature_key].outputs["outputs"].name)
    #     x = load("person_x.pk")
    #     _data = np.transpose(x,[1,0,2,3])
    #     y = sess.run(outputs,feed_dict={input0:_data[0],input1:_data[1]})
    #     print(np.argmax(y,-1))
    '''

    MAX_LEN = 1000
    vocabpath = os.path.dirname(__file__) + "/codename_vocab.pk"
    vocab = load(vocabpath)
    word2index = dict((w, i) for i, w in enumerate(np.array(vocab)))
    index_unk = word2index.get("<unk>")
    sentence = "招标人：广州市重点公共建设项目管理中心，联系人：李工，联系方式：020-22905689，招标代理：广东重工建设监理有限公司，" \
               "代理联系人：薛家伟，代理联系方式：13535014481，招标监督机构：广州市重点公共建设项目管理中心，监督电话：020-22905690，" \
               "备注：以上为招标公告简要描述，招标公告详细信息请查看“招标公告”附件，"
    sentence = sentence*5
    list_sentence = [sentence]*200
    # print(list_sentence)
    x = [[word2index.get(word, index_unk) for word in sentence] for sentence in
         list_sentence]
    x_len = [len(_x) if len(_x) < MAX_LEN else MAX_LEN for _x in x]
    # print(x_len)
    x = pad_sequences(x, maxlen=MAX_LEN, padding="post", truncating="post")

    requests_result = requests.post(API_URL + "/predict_codeName", json={"inouts": x.tolist(), "inouts_len": x_len},
                                    verify=True)
    # predict_y = json.loads(requests_result.text)['result']
    print("cost_time:", json.loads(requests_result.text)['cost_time'])
    print(MAX_LEN, len(sentence), len(list_sentence))
    requests_result = requests.post(API_URL + "/predict_codeName", json={"inouts": x.tolist(), "inouts_len": x_len},
                                    verify=True)
    # predict_y = json.loads(requests_result.text)['result']
    print("cost_time:", json.loads(requests_result.text)['cost_time'])
    print(MAX_LEN, len(sentence), len(list_sentence))