import os
import numpy as np
from numpy.linalg import norm
import pandas as pd
# from rouge import Rouge
from rouge_chinese import Rouge
from Rouge_w import Rouge_w,Rouge_l
import json
import pymysql

import requests
from flask import Flask, jsonify
from flask import request
import uuid
app = Flask(__name__)
app.config["JSON_AS_ASCII"] = False


nums_cpus = 16
rouge = Rouge()
rouge_model = Rouge_w()
rouge_l_model = Rouge_l()


def rouge_value_self(data_1, data_2):
    data_1 = [' '.join(i) for i in data_1]
    data_2 = [' '.join(i) for i in data_2]
    rouge_l_list = []

    for sen_1, sen_2 in zip(data_1, data_2):
        sen_1 = sen_1.split(" ")
        sen_2 = sen_2.split(" ")
        rouge_l_score = rouge_l_model.score(sen_1, sen_2)
        rouge_l_list.append(rouge_l_score)

    return "", "", rouge_l_list


def rouge_pre(text, df_train_nuoche):

    return_list = []
    index_rouge_list = []
    text_list = [text] * len(df_train_nuoche)

    data_list = []
    for data_dan in df_train_nuoche:
        data_list.append(data_dan[0])
    rouge_1, rouge_2, rouge_l = rouge_value_self(text_list, data_list)
    index_rouge_list.extend(rouge_l)

    re1 = [(i[0], i[1]) for i in sorted(list(enumerate(index_rouge_list)), key=lambda x: x[1], reverse=True)]

    return_list.append(re1[0][1])
    return_list.append(re1[0][0])

    return return_list


def accurate_check_rouge(text_paper, recall_data_list):
    '''
    精确查重出相似句子
    :param text:
    :param recall_data_list: list [[sentence, filename],[sentence, filename],[sentence, filename]]
    :return:
    '''
    # 文本处理
    # with open(text_paper_path, encoding="gbk") as f:
    #     text_paper = f.read()
    centent_list = []
    text_paper = str(text_paper).replace("。\n", "。")
    centent_list.extend(text_paper.split("。"))
    data_zong = []

    # rouge算法查重
    for text in centent_list:
        rouge_pre_list = rouge_pre(text, recall_data_list)
        data_zong.append(rouge_pre_list)

    original_dict = []


    # 找出相似的句子序号
    bool_check_sentense = []
    for i in range(len(data_zong)):
        if data_zong[i][0] > 0.47:
            bool_check_sentense.append([i,data_zong[i][1]])
    biao_red = biaohong(bool_check_sentense, data_zong, recall_data_list) # [[[0, 1, 2], [479, 480, 481]], [[3, 4, 5], [481, 482, 483]], [[6, 7, 8], [484, 485, 486]]]

    sentence_0_list = []
    sentence_1_list = []
    sim_paper_name = []

    for i in biao_red:
        if recall_data_list[i[1][0]][1] == recall_data_list[i[1][1]][1] == recall_data_list[i[1][2]][1]:
            sentence_0_list.append("。".join([centent_list[i[0][0]], centent_list[i[0][1]], centent_list[i[0][2]]]))
            sentence_1_list.append("".join([recall_data_list[i[1][0]][0], recall_data_list[i[1][1]][0], recall_data_list[i[1][2]][0]]))
            sim_paper_name.append(recall_data_list[i[1][0]][1])
        else:
            continue

    sentence_0_list_new = []
    sentence_1_list_new = []

    for i in zip(sentence_0_list, sentence_1_list):
        if len(i[0]) + len(i[1]) < 1200:
            sentence_0_list_new.append(i[0])
            sentence_1_list_new.append(i[1])
        else:
            print(len(i[0]) + len(i[1]))
            continue
    for i in zip(sentence_0_list_new, sentence_1_list_new):
        print("超过字数", len(i[0]))
        print("超过字数", len(i[1]))

    paper_dict = biaohong_bert_predict(sentence_0_list_new, sentence_1_list_new)

    # paper_dict
    # print("原文：".format(i), paper_dict[i][0])
    # print("原文标红：".format(i), paper_dict[i][1])
    # print("相似：".format(i), paper_dict[i][2])
    # print("相似标红：".format(i), paper_dict[i][3])

    # original_text
    original_text = []
    original_text_contrast = []


    for paper_dict_dan_id, sentence_0_dan, sentence_1_dan, sim_paper_name_dan in zip(range(len(paper_dict)), sentence_0_list_new, sentence_1_list_new, sim_paper_name):

        print([sentence_0_dan, sentence_1_dan])
        original_text_contrast_dict = {}
        similar_content = {"author": ""}
        try:
            sentence_0_bool, sentence_0_dan_red = original_text_marked_red(sentence_0_dan, paper_dict[paper_dict_dan_id][0], paper_dict[paper_dict_dan_id][1]) # text_original, bert_text, bert_text_pre
        except:
            print([sentence_0_dan,sentence_1_dan])
            9/0
        sentence_1_bool, sentence_1_dan_red = original_text_marked_red(sentence_1_dan, paper_dict[paper_dict_dan_id][2], paper_dict[paper_dict_dan_id][3]) # text_original, bert_text, bert_text_pre

        if sentence_0_bool == False or sentence_1_bool == False:
            continue
        original_text.append(sentence_0_dan_red)
        original_text_contrast_dict["original_text"] = "此处有 {} 字相似\n".format(
            len(paper_dict[paper_dict_dan_id][1])) + sentence_0_dan_red

        similar_content["content"] = sentence_1_dan_red
        similar_content["title"] = sim_paper_name_dan
        original_text_contrast_dict["similar_content"] = similar_content

        original_text_contrast.append(original_text_contrast_dict)

    original_text = "。".join(original_text)

    return {
        "author": "",
        "check_time": "",
        "section_data": "",
        "section_details": [
            {
                "end_page_index": 0,
                "name": "",
                "original_text": original_text,
                "original_text_contrast": original_text_contrast
            }
        ]
    }





def biaohong(bool_check_sentense, data_zong, df_train_nuoche):
    '''
    标红的序号 [[0,1,2],[3,4,5]]
    :param bool_check_sentense:
    :return: list
    '''
    biao_red = []
    i = 0
    start = -1
    end = -1
    while True:
        if i >= len(bool_check_sentense) or bool_check_sentense[i][0] +1 >= len(data_zong) or bool_check_sentense[i][1]+1 >= len(df_train_nuoche):
            break
        elif bool_check_sentense[i][0]-1 == start:
            i += 1
            continue
        elif bool_check_sentense[i][0] == end:
            i += 1
            continue
        elif bool_check_sentense[i][0]-1 == end:
            i += 1
            continue
        else:
            biao_red_dan = []
            biao_red_dan.append([bool_check_sentense[i][0] - 1, bool_check_sentense[i][1] - 1])
            biao_red_dan.append([bool_check_sentense[i][0], bool_check_sentense[i][1]])
            biao_red_dan.append([bool_check_sentense[i][0] + 1, bool_check_sentense[i][1] + 1])
            biao_red.append([[bool_check_sentense[i][0]-1, bool_check_sentense[i][0], bool_check_sentense[i][0]+1],
                             [bool_check_sentense[i][1]-1, bool_check_sentense[i][1], bool_check_sentense[i][1]+1]])
            start = bool_check_sentense[i][0]-1
            end = bool_check_sentense[i][0]+1
            i += 1

    return biao_red


def dialog_line_parse(url, text):
    """
    将数据输入模型进行分析并输出结果
    :param url: 模型url
    :param text: 进入模型的数据
    :return: 模型返回结果
    """

    response = requests.post(
        url,
        json=text,
        timeout=100000
    )
    if response.status_code == 200:
        return response.json()
    else:
        # logger.error(
        #     "【{}】 Failed to get a proper response from remote "
        #     "server. Status Code: {}. Response: {}"
        #     "".format(url, response.status_code, response.text)
        # )
        print("【{}】 Failed to get a proper response from remote "
            "server. Status Code: {}. Response: {}"
            "".format(url, response.status_code, response.text))
        print(text)
        return []


def original_text_marked_red(text_original, bert_text, bert_text_pre):
    '''
    把原文标红字段找到
    :param text_original:
    :param bert_text:
    :param bert_text_pre:
    :return:
    '''

    fuhao = ["\n"]
    up_pointer = 0
    down_pointer = 0

    pointer_list = []

    if len(bert_text_pre) > len(bert_text):
        return False, ""

    while True:
        if down_pointer >= len(bert_text_pre):
            break
        elif down_pointer == len(bert_text_pre)-1:
            if bert_text[up_pointer] == bert_text_pre[down_pointer]:
                pointer_list.append(up_pointer)
                break
            else:
                up_pointer += 1
                down_pointer = 0
                pointer_list = []

        elif bert_text[up_pointer] in fuhao:
            up_pointer += 1

        else:
            if bert_text[up_pointer] == bert_text_pre[down_pointer]:
                pointer_list.append(up_pointer)
                up_pointer += 1
                down_pointer += 1
            else:
                if bert_text_pre[down_pointer:down_pointer+5] == "[UNK]":
                    up_pointer += 1
                    down_pointer += 5
                    pointer_list.append(up_pointer)
                else:
                    up_pointer += 1
                    down_pointer = 0
                    pointer_list = []


    start = pointer_list[0]
    end = pointer_list[-1]
    bert_text_list = list(bert_text)
    bert_text_list.insert(start, "<red>")
    bert_text_list.insert(end + 2 , "</red>")

    text_original_list = list(text_original)

    up = 0
    down = 0

    while True:
        if up == len(text_original_list):
            break

        if text_original_list[up] == bert_text_list[down]:
            up += 1
            down += 1

        else:
            if bert_text_list[down] == "<red>":
                down += 1
            elif bert_text_list[down] == "</red>":
                down += 1
            else:
                bert_text_list.insert(down, text_original_list[up])
                up += 1
                down += 1

    bert_text = "".join(bert_text_list)
    return True, bert_text


def biaohong_bert_predict(sentence_0_list, sentence_1_list):
    '''
    找出标红字符
    :param bool_check_sentense:
    :return:
    '''

    # sentence_0_list = []
    # sentence_1_list = []
    # sim_paper_name = []
    #
    # for i in biaohong_list:
    #     sentence_0_list.append("。".join([paper_list[i[0][0]], paper_list[i[0][1]], paper_list[i[0][2]]]))
    #     sentence_1_list.append("。".join([recall_data_list[i[1][1]], recall_data_list[i[1][1]], recall_data_list[i[1][2]]]))

    paper_dict = dialog_line_parse("http://192.168.31.74:16003/", {"sentence_0": sentence_0_list, "sentence_1": sentence_1_list})["resilt"]

    # paper_dict
    # print("原文：".format(i), paper_dict[i][0])
    # print("原文标红：".format(i), paper_dict[i][1])
    # print("相似：".format(i), paper_dict[i][2])
    # print("相似标红：".format(i), paper_dict[i][3])

    # original_text
    #
    #
    # for paper_dict_dan, sentence_0_dan, sentence_1_dan in zip(paper_dict, sentence_0_list, sentence_1_list):
    #     original_text_marked_red

    return paper_dict

def ulit_text(title, text):
    data = []
    try:
        text = json.loads(text)["content"]
    except:
        pass

    text = text.strip().replace("\n", "").replace(" ", "").replace("。", "。\n")
    text_list = text.split("\n")

    for i in text_list:
        data.append([i, title])
    return data

def run_query(conn, sql, params):
    with conn.cursor() as cursor:
        cursor.execute(sql, params)
        result = cursor.fetchall()
    return result


def processing_one_text(paper_id):
    conn = pymysql.connect(
        host='192.168.31.145',
        port=3306,
        user='root',
        password='123456',
        db='zhiwang_db',
        charset='utf8mb4',
        cursorclass=pymysql.cursors.DictCursor
    )

    sql = 'SELECT * FROM main_table_paper_detail_message WHERE doc_id=%s'
    params = (paper_id,)

    result = run_query(conn, sql, params)

    conn.close()
    print(result)
    title = result[0]['title']
    author = result[0]['author']
    content_path = result[0]['content']

    try:
        with open(content_path, encoding="utf-8") as f:
            text = f.read()
    except:
        with open(content_path, encoding="gbk") as f:
            text = f.read()

    data = ulit_text(title, text)
    return data


def ulit_recall_paper(recall_data_list_dict):
    '''
    对返回的十篇文章路径读取并解析
    :param recall_data_list_path:
    :return data: list [[sentence, filename],[sentence, filename],[sentence, filename]]
    '''

    # data = []
    # for path in recall_data_list_path:
    #     filename = path.split("/")[-1]
    #     with open(path, encoding="gbk") as f:
    #         text = f.read()
    #     text_list = text.split("\n")
    #     for sentence in text_list:
    #         if sentence != "":
    #             data.append([sentence, filename])
    # return data


    data = []
    for i in list(recall_data_list_dict.items())[:5]:
        data_one = processing_one_text(i[0])
        data.extend(data_one)


    return data


def recall_10(title, abst_zh, content) -> list:
    '''
    宇鹏召回接口
    :param paper_name:
    :return:
    '''

    request_json = {
        "title": title,
        "abst_zh": abst_zh,
        "content": content
    }
    paper_dict = dialog_line_parse("http://192.168.31.145:50002/check", request_json)

    return paper_dict

@app.route("/", methods=["POST"])
def handle_query():

    title = request.json["title"]
    abst_zh = request.json["abst_zh"] # txt
    content = request.json["content"]

    # 调用宇鹏查询相似十篇
    # recall_data_list_dict = recall_10(title, abst_zh, content)

    with open("data/rell_json.txt") as f:
        recall_data_list_dict = eval(f.read())

    # 读取文章转化成格式数据
    recall_data_list = ulit_recall_paper(recall_data_list_dict)


    # recall_data_list = pd.read_csv("data/10235513_大型商业建筑人员疏散设计研究_沈福禹/查重.csv", encoding="utf-8").values.tolist()

    # 进入精确查重系统
    return_list = accurate_check_rouge(content, recall_data_list)

    return_text = {"resilt": return_list, "probabilities": None, "status_code": 200}
    return jsonify(return_text)  # 返回结果


if __name__ == "__main__":
    app.run(host="0.0.0.0", port=16001, threaded=True, debug=True)