# -*- coding: utf-8 -*-
# @Time    : 2024/2/6 
# @Author  : lkj
# @description :
from collections import Counter
from numpy import dot
from numpy.linalg import norm
from utils.milvus_hlper import Milvus
from utils.request_fun import text_to_vector
import numpy as np


class JyCode(object):
    def __init__(self, db_name, config):
        self.m = Milvus(table=db_name, **config)

    @staticmethod
    def cosine_similarity(vector1, vector2):
        """
        余弦相似计算
        """
        dot_product = np.dot(vector1, vector2)
        norm1 = np.linalg.norm(vector1)
        norm2 = np.linalg.norm(vector2)
        similarity = dot_product / (norm1 * norm2)
        return round(similarity, 4)

    def check(self, text_vec, data_es: list, offset_value=0.025, min_value=0.9, max_value=0.965):
        '''
        规则打可信度
        :param text_vec:
        :param data_es: 通过向量查询后的数据
        :param offset_value topk之间的差异值
        :param min_value  可信度最低阈值，低于该阈值不可信
        :param max_value 最大阈值，大于该值默认直接可信
        :return:
        '''
        try:
            sim_list = [i[2] for i in data_es]  # 相似度列表
            sim_mean = sum(sim_list) / len(sim_list)  # 平均相似度
            data = [item for item in data_es if item[2] >= sim_mean - offset_value ]  # 删除es结果中差异性大且相似度低的值
            count = 3  # 统计个数阈值
            if len(data) <= 4:
                count = 2
            if data[0][2] > max_value:  # 查询结果为正序如果满足极大值或者较大差异直接返回第一个数据
                return data[0][1], 'mode01'
            if data[0][2] - data[1][2] > offset_value and data[0][2] > min_value:  # 如果第一个可信度大于第二个0.025并且第一个相似度大于0.9
                return data[0][1], 'mode1'
            # 新判断当出现多个大于0.9判断每个上一级分类的选取最高的
            score_list = [i[1] for i in data_es if i[2] > min_value]
            if len(score_list) >= 2:
                best_code = ''
                best_sim = 0
                for end_code in score_list:
                    father_code = end_code[:-2]  # 父级的id
                    father_code_name = self.m.get_name(father_code)  # 父级的name
                    father_vec = text_to_vector(father_code_name)
                    similarity = round(dot(text_vec, father_vec) / (norm(text_vec) * norm(father_vec)), 4)  # 相似度计算
                    if similarity > best_sim:  # 循环查找父级相似度最大的
                        best_code = end_code
                        best_sim = similarity
                return best_code, 'mode1'

            #  第三档的可信度规则
            elif data[0][2] - data[1][2] > (offset_value-0.01) and data[0][2] > min_value:
                pcode = data[0][1][:-2]
                pname = self.m.get_name(pcode)
                pvec = text_to_vector(pname)
                similarity = round(dot(text_vec, pvec) / (norm(text_vec) * norm(pvec)), 4)  # 相似度计算
                if similarity > (min_value-0.1):  # 第一个值的父级相似度大于0.8直接返回该值
                    return data[0][1], 'mode1'
            else:
                # 统计整个向量库中返回的数据的出现的频率做规则
                code_list = []
                for row in data:
                    if len(row[1][:-2]) > 1:
                        code_list.append(row[1][:-2])
                    code_list.append(row[1])
                word_count = dict(Counter(code_list))  # 统计对应分类及其父类的词频，如果某个词的父类频率高则定位到该类
                max_word = [(k, v) for k, v in word_count.items() if v >= count]
                if len(max_word) == 1:
                    return max_word[0][0], 'mode2'
                else:  # 如果存在多个值进行对比
                    code = ''
                    code_sim = 0
                    for word in max_word:
                        code_ = word[0]
                        code_name = self.m.get_name(code_)
                        vec = text_to_vector(code_name)
                        sim_ = round(dot(text_vec, vec) / (norm(text_vec) * norm(vec)), 4)  # 相似度计算
                        if sim_ > code_sim:
                            code_sim = sim_
                            code = code_
                    return code, 'mode3'
            return '', 'mode0'
        except Exception as e:
            print('check_error', e)
            return '', 'error'

    def run_mode1(self,text, baseclass=None):
        """
        标的物数字化主函数
        :param text:
        :param baseclass
        :return: result_name:结果名称, similarity:结果与输入文本相似度, mode:流程模式, code:结果编码, credibility:结果可信度
        """
        vec = text_to_vector(text)  # 转成向量
        search_result = self.m.search_china(vec, baseclass)  # 查询结果
        if not search_result:
            return '', 0, 'mode0', '', 0
        similarity = 0  # 文本与结果相似度
        result_name = ''
        mode = 'mode0'
        credibility = 0
        code = ''
        if search_result:
            check_result = self.check(vec, search_result)  # 结果筛选
            if check_result[0]:
                code = check_result[0]
                result_name = self.m.get_name(check_result[0])  # 名称映射
                mode = check_result[1]
                if result_name:
                    res_vec = text_to_vector(result_name)
                    similarity = round(dot(vec, res_vec) / (norm(vec) * norm(res_vec)), 4)  # 相似度计算
                    if mode == 'mode01':
                        credibility = 0.99
                    if mode == 'mode1':
                        credibility = 0.95
                    if mode == 'mode3' and similarity > 0.85:
                        credibility = 0.90
                    if mode == 'mode2' and similarity > 0.8:
                        credibility = 0.85
        if mode in ['mode2', 'mode3']:
            pcode = code[:-2]
            if not pcode:
                pcode = code
            p_name = self.m.get_name(pcode)  # 父类名称
            p_name_vec = text_to_vector(p_name)  # 父类向量
            p_similarity = round(dot(p_name_vec, vec) / (norm(p_name_vec) * norm(vec)), 4)  # 文本与父类计算
            if p_similarity > 0.85 and similarity > 0.9 or similarity == 0.99:
                mode = 'mode4'
                credibility = 0.99
        if credibility == 0:
            result_name = search_result[0][0]
            similarity = search_result[0][2]
            mode = ''
            code = search_result[0][1]
        return result_name, similarity, mode, code, credibility

    def run_mode1_main(self,text, baseclass=None):
        """

        """
        try:
            result = list(self.run_mode1(text, baseclass))
            if not result[0]:
                return ['', '', '', 0, '']
            result.pop(2)
            code = result[2]
            route = self.m.get_root_zc(code)
            result.append(route)
            if result[1] > 0.9 and result[-2] == 0:
                result[-2] = 0.85
            if result[1] == 1.0:
                result[-2] = 0.99
            return result
        except Exception as e:
            print('政采分类错误--->', e)
            return ['', '', '', '', '']