#!/usr/bin/python3
# -*- coding: utf-8 -*-
# @Author  : bidikeji
# @Time    : 2020/12/24 0024 15:23
import re
import os
import time
import tensorflow as tf
from BiddingKG.dl.common.Utils import *
from tensorflow.contrib.crf import crf_log_likelihood
from tensorflow.contrib.layers.python.layers import initializers
from keras.preprocessing.sequence import pad_sequences
import BiddingKG.dl.interface.Preprocessing as Preprocessing
from BiddingKG.dl.interface.Preprocessing import *

def BiLSTM_CRF_tfmodel(sess,weights):
    BiRNN_Units = 140
    chunk_tags = {
        'O': 0,
        'PN_B': 1,
        'PN_M': 2,
        'PN_E': 3
    }

    def embedding_layer(input):
        embedding = tf.get_variable("embedding",initializer=np.array(weights,dtype=np.float32) if weights is not None else None,dtype=tf.float32)
        return tf.nn.embedding_lookup(params=embedding,ids=input)

    def BiLSTM_Layer(input,length):
        with tf.variable_scope("BiLSTM"):
            forward_cell = tf.contrib.rnn.BasicLSTMCell(BiRNN_Units//2,state_is_tuple=True)
            backward_cell = tf.contrib.rnn.BasicLSTMCell(BiRNN_Units//2,state_is_tuple=True)
        output, _ = tf.nn.bidirectional_dynamic_rnn(forward_cell,backward_cell,input,dtype=tf.float32,sequence_length=length)
        output = tf.concat(output,2)
        return output

    def CRF_layer(input,num_tags,BiRNN_Units,time_step):
        with tf.variable_scope("CRF"):
            with tf.variable_scope("hidden"):
                w_hidden = tf.get_variable(name='w_hidden',shape=(BiRNN_Units,BiRNN_Units//2),dtype=tf.float32,
                                           initializer=initializers.xavier_initializer(),regularizer=tf.contrib.layers.l2_regularizer(0.001))
                b_hidden = tf.get_variable(name='b_hidden',shape=(BiRNN_Units//2),dtype=tf.float32,initializer=tf.zeros_initializer())
                # print(input)
                input_reshape = tf.reshape(input,shape=(-1,BiRNN_Units))
                hidden = tf.tanh(tf.nn.xw_plus_b(input_reshape,w_hidden,b_hidden))
            with tf.variable_scope("output"):
                w_output = tf.get_variable(name='w_output',shape=(BiRNN_Units//2,num_tags),dtype=tf.float32,initializer=initializers.xavier_initializer(),regularizer=tf.contrib.layers.l2_regularizer(0.001))
                b_output = tf.get_variable(name='b_output',shape=(num_tags),dtype=tf.float32,initializer=tf.zeros_initializer())
                pred = tf.nn.xw_plus_b(hidden,w_output,b_output)
                logits_ = tf.reshape(pred,shape=(-1,time_step,num_tags),name='logits')
        return logits_

    def layer_loss(input,true_target,num_tags,length):
        with tf.variable_scope("crf_loss"):
            trans = tf.get_variable(name='transitons',shape=(num_tags,num_tags),dtype=tf.float32,initializer=initializers.xavier_initializer())
            log_likelihood,trans = crf_log_likelihood(inputs=input,tag_indices=true_target,transition_params=trans,sequence_lengths=length)
            return tf.reduce_mean(-log_likelihood),trans

    with sess.graph.as_default():
        char_input = tf.placeholder(name='char_input',shape=(None,None),dtype=tf.int32)
        target = tf.placeholder(name='target',shape=(None,None),dtype=tf.int32)
        length = tf.placeholder(name='length',shape=(None,),dtype=tf.int32)
        # keepprob = tf.placeholder(name='keepprob',dtype=tf.float32)

        _embedding = embedding_layer(char_input)
        _shape = tf.shape(char_input)
        batch_size = _shape[0]
        step_size = _shape[-1]
        bilstm = BiLSTM_Layer(_embedding,length)
        _logits = CRF_layer(bilstm,num_tags=len(chunk_tags),BiRNN_Units=BiRNN_Units,time_step=step_size)
        crf_loss,trans = layer_loss(_logits,true_target=target,num_tags=len(chunk_tags),length=length)
        global_step = tf.Variable(0,trainable=False)
        with tf.variable_scope("optimizer"):
            opt = tf.train.AdamOptimizer(0.002)
            grads_vars = opt.compute_gradients(crf_loss)
            capped_grads_vars = [[tf.clip_by_value(g,-5,5),v] for g,v in grads_vars]
            train_op = opt.apply_gradients(capped_grads_vars,global_step)
            return char_input,_logits,target,length,crf_loss,trans,train_op

def decode(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

class Punish_Extract():
    def __init__(self, model_file = os.path.dirname(__file__)+"/models/21-0.9990081295021194-0.3647936/model.ckpt"):
        print('model_file_path:',model_file)
        self.sess = tf.Session(graph=tf.Graph())
        self.code = ""
        self.punish_dicition = ""
        self.model_file = model_file #预测编号模型
        self.load_model()

    # 加载处罚编号预测模型
    def load_model(self):
        with self.sess.as_default() as sess:
            with sess.graph.as_default():
                vocab_model = getModel_word()
                vocab, w2v_matrix = getVocabAndMatrix(vocab_model, Embedding_size=60)
                self.char_input, self.logits, self.target, self.length, self.crf_loss, self.trans, self.train_op = BiLSTM_CRF_tfmodel(sess, w2v_matrix)
                sess.run(tf.global_variables_initializer())
                saver = tf.train.Saver()
                saver.restore(sess, self.model_file)

    # 处罚编号预测
    def predict_punishCode(self,list_sentences):
        re_ner = re.compile("12+?3")
        article_ner_list = []
        count = 0
        with self.sess.as_default():
            with self.sess.graph.as_default():
                for sentences in list_sentences:
                    count += 1
                    # print(count)
                    sentence_len = [len(sentence.sentence_text) for sentence in sentences]
                    maxlen = max(sentence_len)
                    sentences_x = []
                    for sentence in sentences:
                        sentence = sentence.sentence_text
                        sentence = list(sentence)
                        sentence2id = [getIndexOfWord(word) for word in sentence]
                        sentences_x.append(sentence2id)
                    sentences_x = pad_sequences(sentences_x, maxlen=maxlen, padding="post", truncating="post")
                    sentences_x = [np.array(x) for x in sentences_x]
                    _logits, _trans = self.sess.run([self.logits, self.trans],
                                               feed_dict={self.char_input: np.array(sentences_x), self.length: sentence_len})
                    viterbi_sequence = decode(logits=_logits, trans=_trans, sequence_lengths=sentence_len, tag_num=4)

                    ner_list = []
                    for _seq, sentence in zip(viterbi_sequence, sentences):
                        sentence = sentence.sentence_text
                        seq_id = ''.join([str(s) for s in _seq])
                        if re_ner.search(seq_id):
                            # print("sentence: ",sentence)
                            for _ner in re_ner.finditer(seq_id):
                                start = _ner.start()
                                end = _ner.end()
                                n = sentence[start:end]
                                # print(n,'<==>',start,end)
                                # ner_list.append((n, start, end))
                                ner_list.append(n)  # 改为只返回实体字符
                    # article_ner_list.append(ner_list)
                    article_ner_list.append('；'.join(set(ner_list)))
        return article_ner_list[0]

    # 处罚类型
    def get_punishType(self, x1, x2):
        '''通过文章标题及内容判断文章类别
        x1: 标题
        x2: 内容
        return 类别'''
        # x1 = x1.replace('(','（').replace(')', '）').replace(' ','')
        # x2 = x2.replace('(', '（').replace(')', '）').replace(' ', '')
        '''标题正则'''
        # 未知公告
        unknow = re.compile('采购方式|采购公告|磋商公告|谈判公告|交易公告$|征集|征求|招标公告|竞标公告|中标公告|'
                            '成交公告|成交信息|流标公告|废标公告|城市管理考评|决算表|决算|预算|资格考试|招聘|选聘'
                            '|聘请|拟录用|无违规违法|无此项信息|暂无工程投标违法|管理办法|指导意见|无投诉|投诉办法'
                            '公共资源交易情况|绩效评价|考试成绩|付息公告|不动产|办证|印发|转发')  #|结果公示 部分是
        # 投诉处理
        tscl = re.compile('投诉不予[处受]理|投诉不成立|终止投诉|投诉终止|不予受理|投诉事?项?的?处理')
        # 行政处罚
        xzcf = re.compile('行政处罚|行政处理|政处罚|行政裁决|防罚|公罚|医罚|环罚|政罚|文罚|局罚|旅罚|财罚|运罚')
        # 监督检查
        jdjc = re.compile('(监督检查的?问?题?(处理|整改|记分|结果|决定|处罚))|监督处罚|调查处理|监督处理')
        # 严重违法
        yzwf = re.compile('严重违法失信|黑名单|失信名单')
        # 不良行为
        blxw = re.compile('((不良|失信|不诚信|差错|不规范|违规|违约|处罚|违法)(行为|记录|信息))|((违约|违规|违法)(处理|操作|情况|问题))'
                          '|通报批评|记分管理|迟到|早退|缺席|虚假材料|弄虚作假|履职不到位|诚信考核扣分|串通投标'
                          '|审核不通过|码一致|地址一致|扣分处理|扣分通知|扣[0-9]+分|责令整改|信用信息认定书$'
                          '|关于.{,30}的处罚|关于.{,10}的?考评通报|关于.{,30}扣分情况|不规范代理行为'
                          '|(取消|暂停|限制).{,50}((专家|评标|评委|投标|竞价|被抽取|中标|供应商|候选人)资格)'
                          '|(代理服?务?机构).{,10}(扣分)|(专家).{,30}(扣分|记分|处罚)|对.{,30}处理|冻结.{,30}账号')
        # 其他不良行为
        other = re.compile('质疑|代理机构进场交易情况|网上投诉办理|信用奖惩|信用奖罚|进场工作.{,5}考核'
                           '|举报处理|结果无效|成交无效|行政复议')

        '''正文内容正则'''
        # 投诉处理
        tscl_c = re.compile('(投诉(人|单位)[1-9]?(名称)?[:：])|(投诉事项[1-5一二三四五、]*部?分?(成立|予以受理))'
                            '|((驳回|撤回|撤销|终止)[^，。]{,60}(投诉|质疑))')
        # 行政处罚
        xzcf_c = re.compile('((处理依据及结果|处理结果|处罚结果)).*行政处罚|如下行政处罚|行政处罚决定')
        # 诚信加分
        cxjf_c = re.compile('处罚结果.*诚信加分')
        # 严重违法失信
        yzwf_c = re.compile('工商部门严重违法失信起名单|严重违法失信的具体情形') #|严重违法失信的具体情形
        # 不良行为
        blxw_c = re.compile('(取消|暂停|限制).{,30}((专家|评标|评委|投标|采购|竞价|被抽取|中标|供应商)的?资格)'
                            '|(处罚结果|处罚情况).*(扣[1-9]*分|记分|不良行为|不良记录|不良信用|不诚信|扣除信用'
                            '|诚信档案|信用信息|取消.*资格|口头警告|处罚机关|责令改正|罚款|限制投标|暂扣|禁止'
                            '|暂停|封禁|暂无|行政处罚)|处罚结果'
                            '|处罚主题|禁止参与.{,10}政府采购活动|列入不良行为|处罚如下|如下处罚|违规处罚|处罚违规'
                            '|责令改正|责令整改|处罚依据|进行以下处理|处理依据及结果|处理结果|处罚决定书|'
                            '(不规范|不良|不诚信)行为记录')
        # 其他不良行为
        other_c = re.compile('质疑(人|单位)[1-9]?(名称)?：|公告期内受质疑')

        if re.search(unknow, x1):
            return re.search(unknow, x1).group(0), '未知类别'
        elif re.search(yzwf, x1):
            return re.search(yzwf, x1).group(0), '严重违法'
        elif re.search(yzwf_c, x2):
            return re.search(yzwf_c, x2).group(0), '严重违法'

        elif re.search(tscl, x1):
            return re.search(tscl, x1).group(0), '投诉处理'
        elif re.search(xzcf, x1):
            return re.search(xzcf, x1).group(0), '行政处罚'
        elif re.search(jdjc, x1):
            return re.search(jdjc, x1).group(0), '监督检查'
        elif re.search(blxw, x1):
            return re.search(blxw, x1).group(0), '不良行为'
        elif re.search(other, x1):
            return re.search(other, x1).group(0), '其他不良行为'

        elif re.search(tscl_c, x2):
            return re.search(tscl_c, x2).group(0), '投诉处理'
        elif re.search(xzcf_c, x2):
            return re.search(xzcf_c, x2).group(0), '行政处罚'
        elif re.search(cxjf_c, x2):
            return re.search(cxjf_c, x2).group(0), '诚信加分'

        elif re.search(blxw_c, x2):
            return re.search(blxw_c, x2).group(0), '不良行为'
        elif re.search(other_c, x2):
            return re.search(other_c, x2).group(0), '其他不良行为'

        return ' ', '未知类别'

    # 处罚决定
    def get_punishDecision(self, x, x2):
        '''通过正则匹配文章内容中的处理决定
        x:正文内容
        x2: 处罚类别
        return 处理决定字符串'''
        rule1 = re.compile(
            '(((如下|以下|处理|研究|本机关|我机关|本局|我局)决定)|((决定|处理|处理意见|行政处罚|处罚)(如下|如下))'
            '|((以下|如下)(决定|处理|处理意见|行政处罚|处罚))|处理依据及结果|处理结果|处罚结果|处罚情况|限制行为'
            '|整改意见)[:：].{5,}')
        rule2 = re.compile(
            '(((如下|以下|处理|研究|本机关|我机关|本局|我局)决定)|((决定|处理|处罚|处理意见)(如下|如下))'
            '|((以下|如下)(决定|处理|处理意见|处罚))|处理依据及结果|处理结果|处罚结果|处罚情况|限制行为'
            '|处罚内容)[：，,].{10,}')
        rule3 = re.compile('考评结果：?.*')
        rule4 = re.compile('(依据|根据)《.*》.*')
        if x2 == '未知类别':
            return ' '
        elif re.search(rule1, x[-int(len(x)*0.4):]):
            return re.search(rule1, x[-int(len(x)*0.4):]).group(0)
        elif re.search(rule1, x[-int(len(x)*0.6):]):
            return re.search(rule1, x[-int(len(x)*0.6):]).group(0)
        elif re.search(rule2, x[-int(len(x)*0.7):]):
            return re.search(rule2, x[-int(len(x)*0.7):]).group(0)
        elif re.search(rule3, x[-int(len(x)*0.6):]):
            return re.search(rule3, x[-int(len(x)*0.6):]).group(0)
        elif re.search(rule4, x[-int(len(x)*0.4):]):
            return re.search(rule4, x[-int(len(x)*0.4):]).group(0)
        else:
            return ' '

    # 投诉是否成立
    def get_punishWhether(self, x1, x2, x3):
        '''通过正则匹配处理决定判断投诉是否成立
        x1: 处理决定字符串
        x2: 正文内容
        x3: 处罚类别
        return 投诉是否成立'''
        p1 = re.compile('(投诉|投拆|质疑|举报)(事项|内容|事实)?[^不，。]{,10}(成立|属实|予以受理|予以支持)|责令|废标|(中标|成交)[^，。]{,10}无效'
                        '|取消[^，。]{,60}资格|罚款|重新(组织|开展)?(招标|采购)|投诉成立|被投诉人存在违法违规行为'
                        '|采购活动违法|(中标|评标|成交)结果无效')
        p2 = re.compile('投诉不予[处受]理|((投诉|投拆|质疑|举报)(事项|内容|事实)?[^，。]{,10}(不成立|情?况?不属实|不予支持|缺乏事实依据))'
                        '|((驳回|撤回|撤销|终止)[^，。]*(投诉|质疑|诉求))|终止[^，。]{,20}(行政裁决|投诉处理|采购活动)|投诉终止|投诉无效'
                        '|予以驳回|不予受理|继续开展采购|被投诉人不存在违法违规行为|中标结果有效|投诉[^，。]{,10}不成立'
                        '|维持被投诉人|不支持[^，。]{,20}投诉|无确凿证据')
        if x3 != '投诉处理':
            return ' '
        elif re.search(p1, x1):
            return '投诉成立'
        elif re.search(p2, x1):
            return '投诉无效'
        elif re.search(p1, x2):
            return '投诉成立'
        elif re.search(p2, x2):
            return '投诉无效'
        return ' '

    # 执法机构、处罚时间
    def get_institution(self, title, sentences_l, entity_l):
        '''
        通过判断实体前信息判断改实体是否为执法机构
        :param title: 文章标题
        :param sentences_l: 单篇公告句子列表
        :param entity_l: 单篇公告实体列表
        :return: 执法机构及处罚时间字符串，多个的用；号隔开
        '''
        institutions = []
        punishTimes = []
        institution_1 = re.compile("(?:处罚执行部门|认定部门|执法机关名称|执法单位|通报部门|处罚机关|处罚部门)[:：]")
        punishTimes_1 = re.compile("(?:处罚日期|限制行为开始时间|曝光开始日期|处罚决定日期|处罚期限|处罚时间|处理日期|公告开始时间)[:：]")
        # 通过实体前面关键词判断是否为执法机构或处罚时间
        for ner in entity_l:
            if ner.entity_type == 'org':
                left = sentences_l[ner.sentence_index].sentence_text[
                       max(0, ner.wordOffset_begin - 15):ner.wordOffset_begin]
                if institution_1.search(left):
                    institutions.append(ner)
                elif institutions != [] and ner.sentence_index == institutions[-1].sentence_index and \
                        ner.wordOffset_begin - institutions[-1].wordOffset_end < 2 and \
                        sentences_l[ner.sentence_index].sentence_text[
                        ner.wordOffset_begin:institutions[-1].wordOffset_end] \
                        in ['', '、', '和', '及']:
                    institutions.append(ner)
            elif ner.entity_type == 'time':
                left = sentences_l[ner.sentence_index].sentence_text[
                       max(0, ner.wordOffset_begin - 15):ner.wordOffset_begin]
                if punishTimes_1.search(left):
                    punishTimes.append(ner)

        institution_title = re.compile("财政局|财政厅|监督管理局|公管局|公共资源局|委员会")
        institution_time = re.compile(
            "(^，?[\d一二三四五六七八九十]{4}，?[/年-][\d一二三四五六七八九十]{1,2}，?[/月-][\d一二三四五六七八九十]{1,2}，?[/日-]?)")
        ins = ""
        ptime = ""
        # 如果前面步骤找不到处罚机构则在标题找实体，并正则检查是否有关键词
        if institutions == [] and len(title)>10:
            title_ners = getNers([title], useselffool=True)
            if title_ners[0]:
                for title_ner in title_ners[0]:
                    if title_ner[2] == 'org' and institution_title.search(title_ner[3]):
                        ins = title_ner[3]
                        break
        if punishTimes == [] or institutions == []:
            # 如果前面步骤还没找到要素，则通过公司实体后面是否有日期关键词，有则作为处罚机构和处罚时间
            for ner in [ner for ner in entity_l if ner.entity_type == 'org'][-5:][::-1]:
                right = sentences_l[ner.sentence_index].sentence_text[ner.wordOffset_end:ner.wordOffset_end + 16]
                if institution_time.search(right):
                    if ins == '':
                        ins = ner.entity_text
                    if ptime == '':
                        ptime = institution_time.search(right).group(1)
                    break
            # 前面步骤都没找到则判断最后一个时间实体是否在文章末尾，是则作为处罚时间
            if ptime == '':
                n_time = [ner for ner in entity_l if ner.entity_type == 'time']
                if len(n_time) != 0:
                    ner = n_time[-1]
                    if ner.sentence_index == len(sentences_l) - 1:
                        textLong = len(sentences_l[ner.sentence_index].sentence_text)
                        if ner.wordOffset_end > textLong - 3 and len(ner.entity_text) > 3:
                            ptime = ner.entity_text
        institutions = [ner.entity_text for ner in institutions]
        punishTimes = [ner.entity_text for ner in punishTimes]
        if institutions == [] and ins != "":
            institutions.append(ins)
        if punishTimes == [] and ptime != "":
            punishTimes.append(ptime)
        return "；".join(institutions), "；".join(punishTimes)

    # 投诉人、被投诉人、被处罚人
    def get_complainant(self, punishType, sentences_l, entity_l):
        '''
        通过对公告类别、句子列表、实体列表正则寻找投诉人、被投诉人、处罚人
        :param punishType: 公告处罚类别
        :param sentences_l: 单篇公告句子列表
        :param entity_l: 单篇公告实体列表
        :return: 投诉人、被投诉人
        '''
        complainants = []  # 投诉人
        punishPeople = []  # 被投诉人、被处罚人
        size = 16
        # 投诉人、质疑人
        complainants_rule1 = re.compile(
            "(?:[^被]|^)(?:投[诉拆][人方]|质疑[人方]|质疑供应商|质疑单位|疑问[人方]|检举[人方]|举报[人方])[\d一二三四五六七八九十]?(\(.+?\))?(:?，?名称[\d一二三四五六七八九十]?)?(?:[:：，]+.{0,3}$|$)")
        # 被处罚人，被投诉人
        punishPeople_rule1 = re.compile(
            "(被投[诉拆][人方]|被检举[人方]|被举报[人方]|被处罚人|被处罚单位|行政相对人|单位名称|不良行为单位或个人|被查单位|处罚主题|企业|主体|违规对象|违规单位|当事人)[\d一二三四五六七八九十]?(\(.+?\))?(:?，?名称[\d一二三四五六七八九十]?)?(?:[:：，]+.{0,3}$|$)")
        punishPeople_rule2_1 = re.compile("，$")
        punishPeople_rule2_2 = re.compile("^[:：]")
        punishPeople_rule3_1 = re.compile("(?:关于|对)[^，。]*$")
        punishPeople_rule3_2 = re.compile("^[^，。]*(?:通报|处罚|披露|处理|信用奖惩|不良行为|不良记录)")

        punish_l = []  # 处罚实体列表
        tmp = []
        for ner in [ner for ner in entity_l if ner.entity_type in ['org', 'company', 'person']]:
            if tmp == []:
                tmp.append(ner)
            elif ner.entity_type == tmp[-1].entity_type and ner.sentence_index == tmp[-1].sentence_index and \
                    ner.wordOffset_begin - tmp[-1].wordOffset_end < 2 \
                    and sentences_l[ner.sentence_index].sentence_text[ner.wordOffset_begin:tmp[-1].wordOffset_end] in [
                '',
                '、',
                '和',
                '及']:
                tmp.append(ner)
            elif ner.entity_type in ['org', 'company'] and tmp[-1].entity_type in ['org', 'company'] and \
                    ner.sentence_index == tmp[-1].sentence_index and ner.wordOffset_begin - tmp[-1].wordOffset_end < 2 \
                    and sentences_l[ner.sentence_index].sentence_text[ner.wordOffset_begin:tmp[-1].wordOffset_end] in [
                '',
                '、',
                '和',
                '及']:
                tmp.append(ner)
            else:
                punish_l.append(tmp)
                tmp = [ner]
        for ner_l in punish_l:
            begin_index = ner_l[0].wordOffset_begin
            end_index = ner_l[-1].wordOffset_end
            left = sentences_l[ner_l[0].sentence_index].sentence_text[max(0, begin_index - size):begin_index]
            right = sentences_l[ner_l[0].sentence_index].sentence_text[end_index:end_index + size]
            if complainants_rule1.search(left):
                complainants.append(ner_l)
            elif punishPeople_rule1.search(left):
                punishPeople.append(ner_l)
            elif punishPeople_rule2_1.search(left) and punishPeople_rule2_2.search(right):
                if punishType == '投诉处理':
                    complainants.append(ner_l)
                else:
                    punishPeople.append(ner_l)
            elif punishPeople_rule3_1.search(left) and punishPeople_rule3_2.search(right):
                punishPeople.append(ner_l)
        complainants = set([it.entity_text for l in complainants for it in l])
        punishPeople = set([it.entity_text for l in punishPeople for it in l])
        return '；'.join(complainants), '；'.join(punishPeople)

    def get_punish_extracts_backup(self, doc_id=' ', title=' ', text=' '):
        list_articles, list_sentences, list_entitys, _ = Preprocessing.get_preprocessed([[doc_id, text, "", "", ""]],
                                                                                        useselffool=True)
        punish_code = punish.predict_punishCode(list_sentences)
        # print('处罚编号： ',punish_code)
        institutions, punishTimes = punish.get_institution(title, list_sentences[0], list_entitys[0])
        # print('执法机构:',institutions, '\n 处罚时间：', punishTimes)
        keyword, punishType = punish.get_punishType(title, text)
        # print('处罚类型：',punishType)
        punishDecision = punish.get_punishDecision(text, punishType)
        # print('处罚决定：',punishDecision)
        punishWhether= punish.get_punishWhether(punishDecision, text, punishType)
        # print('投诉是否成立：',punishWhether)
        complainants, punishPeople = punish.get_complainant(punishType, list_sentences[0], list_entitys[0])
        # print('投诉人：%s  被投诉人：%s'%(complainants, punishPeople))
        punish_dic = {'punish_code':punish_code,
                      'punishType':punishType,
                      'punishDecision':punishDecision,
                     'complainants':complainants,
                     'punishPeople':punishPeople,
                     'punishWhether':punishWhether,
                     'institutions':institutions,
                     'punishTimes':punishTimes}
        return punish_dic
        # return punish_code, punishType, punishDecision, complainants, punishPeople, punishWhether,institutions, punishTimes

    def get_punish_extracts(self,list_sentences, list_entitys, title=' ', text=' '):
        keyword, punishType = self.get_punishType(title, text)
        if punishType == "未知类别":
            return {'punishType':punishType}
        # print('处罚类型：',punishType)
        punish_code = self.predict_punishCode(list_sentences)
        # print('处罚编号： ',punish_code)
        institutions, punishTimes = self.get_institution(title, list_sentences[0], list_entitys[0])
        # print('执法机构:',institutions, '\n 处罚时间：', punishTimes)
        punishDecision = self.get_punishDecision(text, punishType)
        # print('处罚决定：',punishDecision)
        punishWhether= self.get_punishWhether(punishDecision, text, punishType)
        # print('投诉是否成立：',punishWhether)
        complainants, punishPeople = self.get_complainant(punishType, list_sentences[0], list_entitys[0])
        # print('投诉人：%s  被投诉人：%s'%(complainants, punishPeople))
        punish_dic = {'punish_code':punish_code,
                      'punishType':punishType,
                      'punishDecision':punishDecision,
                     'complainants':complainants,
                     'punishPeople':punishPeople,
                     'punishWhether':punishWhether,
                     'institutions':institutions,
                     'punishTimes':punishTimes}
        return punish_dic

if __name__ == "__main__":
    punish = Punish_Extract(model_file = "models/21-0.9990081295021194-0.3647936/model.ckpt")

    import pandas as pd
    # with open('G:/失信数据/ALLDATA_re2-3.xlsx') as f:
    df = pd.read_excel('G:/失信数据/ALLDATA_re2-3.xlsx', index=0)[2:10]
    # i = 89
    # predict('2', df.loc[i, 'PAGE_TITLE'],df.loc[i, 'PAGE_CONTENT'])
    # i = 92
    # predict('2', df.loc[i, 'PAGE_TITLE'],df.loc[i, 'PAGE_CONTENT'])

    # t1 = time.time()
    # for i in df.index:
    #     punish_code, punishType, punishDecision, complainants, punishPeople, punishWhether, institutions, punishTimes = \
    #         get_punish_extracts(i, df.loc[i, 'PAGE_TITLE'], df.loc[i, 'PAGE_CONTENT'])
    #     df.loc[i, '投诉人'] = complainants
    #     df.loc[i, '被投诉人'] = punishPeople
    #     df.loc[i, '执法机构'] = institutions
    #     df.loc[i, '处罚时间'] = punishTimes
    #     df.loc[i, '处罚编号'] = punish_code
    #     print('完成第%d篇'%i)
    # # df.to_excel('G:/失信数据/ALLDATA_re2-4.xlsx', encoding='utf-8',columns=[['PAGE_TITLE', 'PAGE_CONTENT',
    # #     '关键词', '类别', '处理决定', '投诉是否成立',
    # #    'DETAILLINK', 'sentences', 'PAGE_TIME', 'complainant', 'punishPeople',
    # #    'institution', 'punishTime', 'ner_test']])
    # t2 = time.time()
    # # df.to_excel('G:/失信数据/ALLDATA_re2-4.xlsx', encoding='utf-8',columns=['PAGE_TITLE', 'PAGE_CONTENT',
    # #     '关键词', '类别', '处理决定', '投诉是否成立',
    # #    'DETAILLINK', 'sentences', 'PAGE_TIME', 'complainant', '投诉人', 'punishPeople', '被投诉人',
    # #    'institution', '执法机构', 'punishTime', '处罚时间', 'ner_test', '处罚编号'])
    # df.to_excel('G:/失信数据/ALLDATA_re2-4.xlsx', encoding='utf-8',columns=['PAGE_TITLE', 'PAGE_CONTENT',
    #     '关键词', '类别', '处理决定', '投诉是否成立', '投诉人', '被投诉人','执法机构', '处罚时间', '处罚编号',
    #    'DETAILLINK', 'sentences', 'PAGE_TIME'])
    # t3 = time.time()
    # print('处理耗时：%.4f, 保存耗时：%.4f'%(t2-t1, t3-t2))
    s = '编号：厦财企〔2020〕12号，各有关单位：341号。处罚编号：厦财企〔2020〕12号，文章编号：京财采投字(2018)第42号。公告编号：闽建筑招〔2018〕5号。处罚编号：松公管监[2020]2号,'
    # list_sentences = [s.split('。')]
    # punish_code= punish.predict_punishCode( list_sentences)
    # print(punish_code)

    # punish_code, punishType, punishDecision, complainants, punishPeople, punishWhether, institutions, punishTimes = \
    #             get_punish_extracts(text=s)
    punish_dic = punish.get_punish_extracts_backup(text=s)
    print(punish_dic)