new_tiku_structure_v3_art/structure/structure_main.py

#!/usr/bin/env/python
# -*- coding:utf-8 -*-

import sys
sys.path.append(r"F:\zwj\Text_Structure\new_tiku_structure_v3_art")

from pprint import pprint
# from utils.exam_type import get_exam_type
from structure.final_structure import one_item_structure
from utils.stem_ans_split import get_split_pos
from utils.washutil import *
from structure.three_parse_structure import *
from utils.pic_pos_judge import img_regroup
from structure.paper_text_structure import WordParseStructure
from func_timeout import func_set_timeout

from utils.xuanzuoti2slave import toslave_bef, toslave_aft


paper_types = ["第三种试卷格式：题目与答案分开",
               "第二种试卷格式: 不同时含有或都不含有{答案}和{解析}关键字",
               "第一种试卷格式：教师用卷，含答案和解析关键字"]


class StructureExporter(WordParseStructure):
    """
    基于wordbin出来的html结果进一步做 试卷类型 非模板结构化
    """

    def img_repl(self, one_dict):
        """
        初步拆分题目后，图片信息的替换
        :return:
        """
        imgs = {s: re.findall("<img.*?/>", one_dict[s]) for s in ['stem', 'key', 'parse', 'com_stem'] if s in one_dict}
        for k, imgs_seq in imgs.items():
            for img in imgs_seq:
                img = re.sub("(?<!\s)(w_h|data-latex)=", r" \1=", img)
                one_dict[k] = one_dict[k].replace(img, self.subs2src[img])
                # if type(self.img_url) == str and self.img_url:
                #     one_dict[k] = re.sub(r'<img src="files/', '<img src="' + str(self.img_url), str(one_dict[k]))
        if "analy" in one_dict:
            for img in re.findall("<img.*?/>", one_dict["analy"]):
                img = re.sub("(?<!\s)(w_h|data-latex)=", r" \1=", img)
                one_dict["analy"] = one_dict["analy"].replace(img, self.subs2src[img])
        return one_dict

    # @func_set_timeout(30)
    def export(self):
        """结构化入口"""
        if not self.row_list:
            return {"errcode": 1, "errmsgs": "题文没有有效信息", "data": {}}, ""
        # print(self.row_list)
        # 判断考试类型
        # paper_other_info = get_exam_type(row_list)

        # 第二步：寻找题目和答案的切分点，一定要有“答案”关键字
        split_res = get_split_pos(self.row_list)
        if type(split_res) == str:
            return {"errcode": 1, "errmsgs": split_res, "data": {}}, paper_types[0]
        row_list, items_list, ans_list, is_may_ans = split_res
        rd2_is_fail = 0
        rd1_may_fail = 0
        item_res, paper_type, item_no_type = "", "", 1
        item_groups, ans_groups = {}, {}
        if "【答案】" in "".join(items_list) or "【解析】" in "".join(items_list):
            rd1_may_fail = 1
        else:
            if items_list:
                paper_type = paper_types[0]
                reform_res = items_ans_reform(items_list, ans_list, self.subject)
                if type(reform_res) == str:
                    return {"errcode": 1, "errmsgs": reform_res, "data": {}}, paper_type
                else:
                    # item_res = reform_res
                    if len(reform_res) == 2:
                        item_res, item_no_type = reform_res
                    else:
                        item_res, item_no_type, rd2_is_fail, item_groups = reform_res

        if not items_list or rd1_may_fail or (is_may_ans and rd2_is_fail):
            ans_n = re.findall("【答案】", "\n".join(row_list))
            parse_n = len(re.findall("【解析】", "\n".join(row_list)))
            if self.subject not in ["地理", "语文"] and ans_n and len(ans_n) == parse_n > 10:  # 带相同个数的答案和解析
                paper_type = paper_types[2]
                item_res = split_by_keywords(row_list, self.subject)
                if type(item_res) == str and re.search("格式有误|没有换行|题型不明确|题型行格式有问题", item_res):
                    print("第一种试卷格式解析格式有误")
                    try:
                        paper_type = paper_types[1]
                        item_res = split_by_topicno(row_list, self.subject)
                    except:
                        return {"errcode": 1, "errmsgs": item_res, "data": {}}, paper_type
            else:
                paper_type = paper_types[1]
                item_res = split_by_topicno(row_list, self.subject)

        item_list = []
        if type(item_res) == str:
            return {"errcode": 1, "errmsgs": item_res, "data": {}}, paper_type
        else:
            if type(item_res) == tuple:
                if len(item_res) == 2:
                    item_list, item_no_type = item_res
                else:
                    item_list, item_no_type, item_groups, ans_groups = item_res
            elif type(item_res) == list:
                item_list = item_res

            # pprint(item_list)
            print('****************初步切分题目的个数*****************', len(item_list))
            res = []
            if item_list:
                item_list = img_regroup(item_list, row_list)   # 图片重组判断
                if self.subs2src:
                    item_list = list(map(self.img_repl, item_list))  # 图片信息替换还原
            # ---------初步拆分题目错误判断--------------------
            # ---------新题型进一步拆分--------------------
            #     new_item = [[k, i] for k, i in enumerate(item_list) if re.search("选[修学]", i["stem"][:10])]
            #     have_slave = 0
            #     to_slave = []
            #     if new_item:
            #         try:
            #             have_slave = 1
            #             for one in new_item:
            #                 new_res = toslave_bef(one[1])
            #                 if type(new_res) == list:
            #                     to_slave.extend(new_res)
            #                     item_list.remove(one[1])
            #                 else:
            #                     item_list[one[0]] = new_res
            #         except:
            #             pass
            #     if to_slave:
            #         item_list.extend(to_slave)
            # ==========小题结构化========
            #   from multiprocessing.dummy import Pool as ThreadPool
            #   pool = ThreadPool(2)  # 比# pool = multiprocessing.Pool(3)速度快
            #     pprint(item_list)
                consumer = ['toslave'] * len(item_list)  # noslave
                items_no_type = [item_no_type] * len(item_list)
                sj = [self.subject] * len(item_list)
                xyz = zip(item_list, consumer, items_no_type, sj, [0] * len(item_list))
                # res = list(pool.map(one_item_structure, xyz))
                res = list(map(one_item_structure, xyz))  # 和多进程相比，这样速度也很快
            # ==========最后的清洗=========
            #     pprint(res)
                res = wash_after(res, item_groups, ans_groups, self.subject)
                # if have_slave and not to_slave:
                #     res = list(map(toslave_aft, res))
            # 结果返回
            if self.is_reparse:
                return {"html": self.new_html, "items": res}, paper_type
            else:
                return {"items": res}, paper_type

    @staticmethod
    def _get_all_errors(res):
        """
        整套试卷结构化完成以后,把所有报错放在一个list里面：
        all_errors = [{"单选题第1题目":[]},{"解答题第2题":[]},{},{}]
        :param res:
        :return:
        """
        type_names = []
        errmgs = []
        spliterr_point = []
        for one_res in res:
            type_names.append(one_res["type"])
            if "text_errmsgs" in one_res:
                errmgs.append(one_res["text_errmsgs"])
            else:
                errmgs.append("")
            if 'spliterr_point' in one_res:
                spliterr_point.append(one_res['spliterr_point'])
        # 给同种题型的名字重新编码
        new_names = []
        for k, v in enumerate(type_names):
            if v:
                nums = str(type_names[:k]).count(v)
            else:
                nums = k
            if spliterr_point:
                add_n = insert_sort2get_idx(spliterr_point, k+1)
                new_names.append("{}第{}题(在整份word中的序号为{}题)".format(v, nums + 1 + add_n, k + 1 + add_n))
            else:
                new_names.append("{}第{}题(在整份word中的序号为{}题)".format(v, nums + 1, k + 1))
        all_errors = []
        for name, error in zip(new_names, errmgs):
            if len(error) > 0:
                all_errors.append({name: error})
        return all_errors


if __name__ == '__main__':
    # 单份试卷测试
    import json
    from bson.objectid import ObjectId

    # path1 = r"F:\zwj\parse_2021\data\fail\2\2.txt"
    # path = r"F:\zwj\parse_2021\res_folder\13.html"
    # images_url1 = ""  # "http://49.233.23.58:11086/ser_static/4439/files/"
    # html = "<p>"+"</p>\n<p>".join(html.split("\n"))+"</p>"

    # with open(r"F:\zwj\Text_Structure\fail_files3\c5e222c5fbded2a2264ae002907fc92c__2021_04_16_18_43_23.json", 'r') as load_f:
    #     html = json.load(load_f)
        # print(load_dict)

    # path2 = r"G:\zwj\WL\Text_Structure\new_tiku_structure_v3_art\data\地理\3\安徽高三地理.html"
    # path2 = r"G:\zwj\WL\Text_Structure\new_tiku_structure_v3_art\data\地理\2\gd1.html"
    # path2 = r"G:\zwj\WL\Text_Structure\new_tiku_structure_v3_art\data\地理\shuguang.html"
    # path2 = r"G:\zwj\WL\Text_Structure\new_tiku_structure_v3_art\data\huaxue\huexue2.html"
    # path2 = r"F:\zwj\new_word_text_extract_2021\data\phy_clean.html"
    # path2 = r"G:\zwj\Word2Html\data\yuwen\yuwen1.html"
    # path2 = r"F:\zwj\Text_Structure\new_tiku_structure_v3_art\data\语文\bj.html"
    # path2 = r"G:\zwj\WL\Text_Structure\new_tiku_structure_v3_art\data\语文\2\tianjin.html"
    # path2 = r"G:\zwj\WL\Text_Structure\new_tiku_structure_v3_art\data\语文\2\61c5380666e78ea2a20b4ff0.html"
    # path2 = r"G:\zwj\WL\Text_Structure\new_tiku_structure_v3_art\data\政治\jiexi_bj.html"
    # path2 = r"F:\zwj\Text_Structure\accept_files\62a2f9b9765759d85567a3e4.html"
    #    6239991e6ca622396925f66b     624cf82d12cd45a7836f3430  626b4b1f81b582c0470d01b0
    # 627b64b0814132f0d7b12589    627b622981b582c0470d020e
    # 6294326cf84c0e279ac6484e.html   62903acaf84c0e279ac647fb
    path2 = r"C:\Users\Administrator\Desktop\666a67fec3c4da9e7009b531.html"
    path2 = r"C:\Users\Administrator\Desktop\66753958c3c4da9e7009b7ae.html"
    path2 = r"F:\zwj\Text_Structure\accept_files\66799166c3c4da9e7009b84f_2.html"
    html = open(path2, "r", encoding="utf-8").read()
    # html = json.loads(html)  621845626ca622396925f55c
    html2 = """
1. I’m anxious___________ your injury.Are you feeling any better now?
2. After he was back on his feet, he was anxious___________ (return) to school as soon as possible.
3. Helen was ___________ to death when she saw the ___________scene.She hid herself in the corner, shaking with___________(fright).
4. The music outside___________ (annoy) Tom soon. He couldn’t keep his___________ (concentrate) with such ___________ (annoy) music going on yesterday.
5. With so many people talking around, he still concentrated ___________ doing his homework,which left a deep impression on me.
6. The result was far beyond ___________ we had expected, which brought great joy to every one of us.
7. If the dress doesn’t fit you, take it back and you can exchange it ___________ another one of the same price.
8. The dictionary is out of date：many words have been added ___________ the language since it came out.
9. This vacation I went to an island on the Pacific and  ___________ by its scenery. The island has left a lasting ___________ on me.
10. We are confident about the future and will never lose our confidence  ___________ the achievements we will make.
11. He has worked for nearly 20 years, so he is senior ___________ most of his workmates.
12. Although he is three years junior ___________ me, he has more work experience.
    """
    res1 = StructureExporter(html, "202406251733", "语文", 0).export()
    # new_fpath = os.path.join(r"G:\zwj\WL\Text_Structure\fail_files", "res_政治.json")
    # re_f = open(new_fpath, 'w', encoding='utf-8')
    # json.dump(res1[0]["items"], re_f, ensure_ascii=False)
    # for i in res1[0]["items"]:
    #     re_f.write(str(i))
    pprint(res1[0]["items"])
    # pprint(res1[0]["html"])
    print('题目数量：', len(res1[0]["items"]))

    # new_fpath = r"F:\zwj\Text_Structure\new_tiku_structure_2021\res_folder\10-28.json"
    # re_f = open(new_fpath, 'w', encoding='utf-8')
    # json.dump(res1, re_f, ensure_ascii=False)
    # mongo = Mongo()
    # data = mongo.get_data_info({"_id": ObjectId("5fc64c9c4994183dda7e75b2")})
    # # pprint(data["item_ocr"])
    # res1 = WordParseStructure(data["item_ocr"], images_url1).structure()

    # print(res1)
    # print('题目数量：', len(res1[0]["items"]))

    # 6837 序号有些乱  6836 图片位置和格式有问题
    # 6822  16A、和16B、类型的序号怎么处理    'item_id'有int和 str 型，须统一处理下
    # 6820 答案页没有明显标识
    # 14.html  只有答案，没有题干
    # 21.html  多套题目在一起，多个从1开始的序号，最后一道题，把后面题目都放在一起了，需要判断一下吗？
    # import json
    # re_f = open("207.txt", 'w', encoding='utf-8')
    # json.dump(res1[0], re_f)

    # json文件
    # for file in os.listdir(r"F:\zwj\Text_Structure\fail_files"):
    #     path1 = os.path.join(r"F:\zwj\Text_Structure\fail_files", file)
    #     # path1 = r"F:\zwj\Text_Structure\fail_files\89a6911f57bf89aba898651b27d2a2fc__2021_04_09_18_50_19.json"
    #     with open(path1,'r',encoding='utf-8') as f:
    #         html= json.load(f)
    #         pprint(html)
    #     # try:
    #     #     res1 = WordParseStructure(html, "").structure()
    #         # os.remove(path1)
    #     # except:
    #     #     pass
    #     res1 = WordParseStructure(html, "").structure()
    #     pprint(res1)
    #     print('题目数量：', len(res1[0]["items"]))