241 lines
10 KiB
Python
241 lines
10 KiB
Python
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import json
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import re
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from functools import cmp_to_key
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from flask_app.general.投标人须知正文提取指定内容 import extract_between_sections
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#生成结构化的数据，即根据序号1 1.1 1.1.1生成有层级的JSON，已废弃！
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#工程标版
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def compare_headings(a, b):
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a_nums = [int(num) for num in a[0].rstrip('.').split('.') if num.isdigit()]
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b_nums = [int(num) for num in b[0].rstrip('.').split('.') if num.isdigit()]
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return (a_nums > b_nums) - (a_nums < b_nums)
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# 主要是处理键值中若存在若干序号且每个序号块的内容>=50字符的时候，用列表表示。
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def post_process(value):
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# 如果传入的是非字符串值，直接返回原值
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if not isinstance(value, str):
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return value
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# 定义可能的分割模式及其正则表达式
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patterns = [
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(r'\d+、', r'(?=\d+、)'), # 匹配 '1、'
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(r'[（(]\d+[）)]', r'(?=[（(]\d+[）)])'), # 匹配 '(1)' 或 '（1）'
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(r'\d+\.', r'(?=\d+\.)'), # 匹配 '1.'
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(r'[一二三四五六七八九十]、', r'(?=[一二三四五六七八九十]、)'), # 匹配 '一、'、'二、' 等
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(r'[一二三四五六七八九十]\.', r'(?=[一二三四五六七八九十]\.)') # 匹配 '一.'、'二.' 等
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]
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# 初始化用于保存最早匹配到的模式及其位置
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first_match = None
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first_match_position = len(value) # 初始值设为文本长度，确保任何匹配都会更新它
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# 遍历所有模式，找到第一个出现的位置
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for search_pattern, split_pattern_candidate in patterns:
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match = re.search(search_pattern, value)
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if match:
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# 如果这个匹配的位置比当前记录的更靠前，更新匹配信息
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if match.start() < first_match_position:
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first_match = split_pattern_candidate
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first_match_position = match.start()
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# 如果找到了最早出现的匹配模式，使用它来分割文本
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if first_match:
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blocks = re.split(first_match, value)
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else:
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# 如果没有匹配的模式，保留原文本
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blocks = [value]
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processed_blocks = []
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for block in blocks:
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if not block:
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continue
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# 计算中英文字符总数，如果大于50，则加入列表
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if block and len(re.findall(r'[\u4e00-\u9fff\w]', block)) >= 50:
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processed_blocks.append(block.strip())
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else:
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# 如果发现有块长度小于50，返回原数据
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return value
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# 如果所有的块都符合条件，返回分割后的列表
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return processed_blocks
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def preprocess_data(data):
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"""
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预处理数据，自动添加缺失的父层级键，并按数字顺序排序。
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"""
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keys_to_add = set()
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for key in data.keys():
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parts = key.split('.')
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if len(parts) > 1:
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parent_key = parts[0] + '.'
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if parent_key not in data:
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keys_to_add.add(parent_key)
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# 添加缺失的父层级键
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for parent_key in keys_to_add:
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data[parent_key] = parent_key.rstrip('.')
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# 对键进行排序
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sorted_data = dict(sorted(data.items(), key=cmp_to_key(compare_headings)))
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return sorted_data
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def transform_json(data):
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result = {}
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temp = {0: result} # 初始化根字典
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data=preprocess_data(data)
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# 首先,创建一个临时字典用于检查是否存在三级标题
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has_subkey = {}
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for key in data.keys():
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parts = key.split('.')
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if len(parts) > 2 and parts[1]:
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parent_key = parts[0] + '.' + parts[1]
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has_subkey[parent_key] = True
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for key, value in data.items():
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match = re.match(r'(\d+)(?:\.(\d+))?(?:\.(\d+))?', key)
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if match:
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levels = [int(l) for l in match.groups() if l is not None]
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if (len(levels) - 1) in temp:
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parent = temp[len(levels) - 1]
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else:
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print(f"No parent found at level {len(levels) - 1} for key '{key}'. Check the data structure.")
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continue
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if len(levels) == 1: # 一级标题
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# 新增逻辑：判断值中是否有 ':' 或 '：'，并进行拆分
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# 优先按 '\n' 拆分
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if '\n' in value:
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new_key, *new_value = value.split('\n', 1)
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new_key = new_key.strip()
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new_value = new_value[0].strip() if new_value else ""
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# 如果没有 '\n'，再检查 ':' 或 '：'，并进行拆分
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elif ':' in value or '：' in value:
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delimiter = ':' if ':' in value else '：'
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new_key, new_value = value.split(delimiter, 1)
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new_key = new_key.strip()
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new_value = new_value.strip()
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else:
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new_key = value.strip()
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new_value = ""
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parent[new_key] = {}
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if new_value:
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parent[new_key][new_key] = new_value # 使用 new_key 作为键名，而不是固定的 "content"
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temp[len(levels)] = parent[new_key]
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elif len(levels) == 2: # 二级标题
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new_key, *new_value = value.split('\n', 1)
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new_key = new_key.strip()
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new_value = new_value[0].strip() if new_value else ""
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if f"{levels[0]}.{levels[1]}" in has_subkey:
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parent[new_key] = [new_value] if new_value else []
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else:
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parent[new_key] = new_value
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temp[len(levels)] = parent[new_key]
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else: # 三级标题
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if isinstance(parent, dict):
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parent_key = list(parent.keys())[-1]
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if isinstance(parent[parent_key], list):
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parent[parent_key].append(value)
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elif parent[parent_key]:
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parent[parent_key] = [parent[parent_key], value]
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else:
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parent[parent_key] = [value]
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elif isinstance(parent, list):
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parent.append(value)
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def remove_single_item_lists(node):
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if isinstance(node, dict):
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for key in list(node.keys()):
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node[key] = remove_single_item_lists(node[key])
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if isinstance(node[key], list) and len(node[key]) == 1:
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node[key] = node[key][0]
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return node
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return remove_single_item_lists(result)
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def sort_clean_data_keys(data):
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# 预处理：删除键名中的空格
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def preprocess_key(key):
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return re.sub(r'\s+', '', key)
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# 将键转换成由整数构成的元组，作为排序依据
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def key_func(key):
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return tuple(int(part) for part in re.split(r'\D+', key) if part)
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# 创建一个新的字典，键名经过预处理
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preprocessed_data = {preprocess_key(key): value for key, value in data.items()}
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# 对预处理后的字典键进行排序
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sorted_keys = sorted(preprocessed_data.keys(), key=key_func)
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# 创建一个新的字典，按照排序后的键添加键值对
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sorted_data = {key: preprocessed_data[key] for key in sorted_keys}
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return sorted_data
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#---------------------------------
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"""
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递归处理嵌套的数据结构（字典和列表）。
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对最内层的字符串值应用 post_process 函数。
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post_process 函数尝试将长字符串按特定模式分割成块，每块至少包含50个中英文字符。
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如果字典中所有值都是 ""、"/" 或空列表，则返回'键'的列表。
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"""
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def process_nested_data(data):
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# 先检查是否所有值都是 ""、"/" 或空列表
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if isinstance(data, dict) and all(v == "" or v == "/" or (isinstance(v, list) and not v) for v in data.values()):
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return list(data.keys())
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# 递归遍历字典，处理最内层的字符串
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if isinstance(data, dict):
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# 如果当前项是字典，继续递归遍历其键值对
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result = {}
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for key, value in data.items():
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processed_value = process_nested_data(value)
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# 如果处理后的值是只有一个元素的列表，就直接使用该元素
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if isinstance(processed_value, list) and len(processed_value) == 1:
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result[key] = processed_value[0]
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else:
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result[key] = processed_value
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return result
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elif isinstance(data, list):
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# 如果是列表，直接返回列表，保持原样
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return data
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else:
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# 到达最内层，处理非字典和非列表的元素（字符串）
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return post_process(data)
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def process_with_outer_key(data):
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processed_data = {}
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# 遍历外层的键值对
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for outer_key, inner_data in data.items():
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# 调用 transform_json 函数对内层数据进行处理
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processed_inner_data = transform_json(inner_data)
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# 将处理后的数据保留在外层键下
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processed_data[outer_key] = processed_inner_data
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return processed_data
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if __name__ == "__main__":
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clause_path=r'C:\Users\Administrator\Desktop\货物\test\test\clause1.json'
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type_target_map = {
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1: ["投标文件", "响应文件", "响应性文件"],
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2: ["开标", "评标", "定标", "评审", "成交", "合同", "磋商", "谈判", "中标", "程序", "步骤"],
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3: ["重新招标、不再招标和终止招标", "重新招标", "重新采购", "不再招标", "不再采购", "终止招标", "终止采购"],
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4: ["评标"] # 测试
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}
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type=1
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target_values = type_target_map.get(type)
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with open(clause_path, 'r', encoding='utf-8') as file:
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data = json.load(file)
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extracted_data = extract_between_sections(data, target_values) # 读取json，截取大标题之间的内容
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#旧版逻辑，大标题：
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transformed_data = process_with_outer_key(extracted_data)
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final_result = process_nested_data(transformed_data)
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#旧版逻辑，序号提取
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sorted_data = sort_clean_data_keys(extracted_data) # 对输入的字典 data 的键进行预处理和排序
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transformed_data = transform_json(sorted_data)
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print(json.dumps(transformed_data,ensure_ascii=False,indent=4))
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