306 lines
16 KiB
Python
306 lines
16 KiB
Python
# -*- coding: utf-8 -*-
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import json
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import os.path
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import time
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import re
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from flask_app.main.json_utils import combine_json_results, nest_json_under_key
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from flask_app.main.通义千问long import upload_file, qianwen_long
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from concurrent.futures import ThreadPoolExecutor
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from flask_app.main.禁止投标情形 import find_forbidden
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from 禁止投标情形 import process_string_list
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#如果当前段落有序号，则向下匹配直接遇到相同的序号样式
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#如果当前段落无序号，则向下匹配序号，把若干同类的序号都摘出来。
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def extract_text_with_keywords(doc_path, keywords, follow_up_keywords):
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from collections import OrderedDict
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from docx import Document
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import re
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doc = Document(doc_path)
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extracted_paragraphs = OrderedDict() # 使用OrderedDict保持顺序
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continue_collecting = False
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current_section_pattern = None
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active_key = None # 用来标记当前正在收集的文本块的键
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def match_keywords(text, patterns):
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"""使用正则表达式匹配关键词。"""
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return any(re.search(pattern, text, re.IGNORECASE) for pattern in patterns)
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def extract_from_text(text, index):
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nonlocal continue_collecting, current_section_pattern, active_key
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if text == "": # Skip empty lines
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return
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if continue_collecting:
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if current_section_pattern and re.match(current_section_pattern, text):
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continue_collecting = False
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active_key = None # 结束当前的收集
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else:
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if active_key is not None:
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extracted_paragraphs[active_key].append(text)
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if match_keywords(text, keywords):
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active_key = text # 设置当前的关键词块
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extracted_paragraphs[active_key] = [text] # 初始化列表以收集文本，包括当前匹配的文本
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# 检查是否也匹配后续关键词
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if match_keywords(text, follow_up_keywords):
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continue_collecting = True
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# 设置跟踪模式
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section_number = re.match(r'(\d+(\s*\.\s*\d+)*)', text)
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if section_number:
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current_section_number = section_number.group(1)
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level_count = current_section_number.count('.')
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pattern = r'\b' + (r'\d+\s*\.\s*') * level_count + r'\d+\b'
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current_section_pattern = re.compile(pattern)
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else:
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found_next_number = False
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current_section_pattern = None
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for next_index in range(index + 1, len(doc.paragraphs)):
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next_text = doc.paragraphs[next_index].text.strip()
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if not found_next_number:
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next_section_number = re.match(r'^([A-Za-z0-9]+(?:\.[A-Za-z0-9]+)*)', next_text)
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if next_section_number:
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found_next_number = True
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section_parts = next_section_number.group(1).split('.')
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dynamic_pattern = r'^' + r'\.'.join([r'[A-Za-z0-9]+' for _ in section_parts]) + r'\b'
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current_section_pattern = re.compile(dynamic_pattern)
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if current_section_pattern and re.match(current_section_pattern, next_text):
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extracted_paragraphs[active_key].append(next_text) # 持续收集
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for index, para in enumerate(doc.paragraphs):
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extract_from_text(para.text.strip(), index)
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return extracted_paragraphs # 返回字典，键是关键词，值是相关文本列表
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def clean_dict_datas(extracted_contents, keywords,excludes): #让正则表达式提取到的东西格式化
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all_texts1 = []
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all_texts2=[]
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# 定义用于分割句子的正则表达式，包括中文和西文的结束标点
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split_pattern = r'(?<=[。！？\!\?])'
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for key, text_list in extracted_contents.items():
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if len(text_list) == 1:
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for data in text_list:
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# 检查是否包含任何需要排除的字符串
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if any(exclude in data for exclude in excludes):
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continue # 如果包含任何排除字符串，跳过这个数据
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# 去掉开头的序号，包括字母+数字的格式 以及括号+数字
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pattern = r'^\s*([（(]\d+[)）]|[A-Za-z]?\d+\s*(\.\s*\d+)*(\s|\.|、)?)'
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data = re.sub(pattern, '', data).strip()
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keyword_match = re.search(keywords, data)
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if keyword_match:
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# 从关键词位置开始查找结束标点符号
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start_pos = keyword_match.start()
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# 截取从关键词开始到后面的内容
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substring = data[start_pos:]
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# 按定义的结束标点分割
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sentences = re.split(split_pattern, substring, 1)
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if len(sentences) > 0 and sentences[0]:
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# 只取第一句，保留标点
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cleaned_text = data[:start_pos] + sentences[0]
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else:
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cleaned_text = data # 如果没有标点，使用整个字符串
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else:
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# 如果没有找到关键词，保留原文本
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cleaned_text = data
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all_texts1.append(cleaned_text) # 将处理后的文本添加到结果列表
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else:
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pattern = r'^\s*([（(]\d+[)）]|[A-Za-z]?\d+\s*(\.\s*\d+)*(\s|\.|、)?)'
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data = re.sub(pattern, '', text_list[0]).strip()
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# 将修改后的第一个元素和剩余的元素连接起来
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text_list[0] = data # 更新列表中的第一个元素
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joined_text = "\n".join(text_list) # 如果列表中有多个元素，则连接它们
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all_texts2.append(joined_text) # 将每个列表的内容添加到 all_texts 中
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return all_texts1,all_texts2 #all_texts1要额外用gpt all_text2直接返回结果
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def find_sentences_with_keywords(data, keywords, follow_up_keywords):
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"""递归查找并返回包含关键词的句子列表，并根据是否存在后续关键词分别存储到两个列表中。"""
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sentences1 = [] # 保存没有后续关键词的情况
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sentences2 = [] # 保存有后续关键词的情况
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if isinstance(data, dict):
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for value in data.values():
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result1, result2 = find_sentences_with_keywords(value, keywords, follow_up_keywords)
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sentences1.extend(result1)
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sentences2.extend(result2)
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elif isinstance(data, list):
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for item in data:
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result1, result2 = find_sentences_with_keywords(item, keywords, follow_up_keywords)
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sentences1.extend(result1)
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sentences2.extend(result2)
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elif isinstance(data, str):
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# 分割句子，保证句子完整性
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split_sentences = re.split(r'(?<=[。！？\!\?])', data)
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i = 0
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while i < len(split_sentences):
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sentence = split_sentences[i]
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if re.search(keywords, sentence, re.IGNORECASE):
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follow_up_present = any(
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re.search(follow_up, sentence, re.IGNORECASE) for follow_up in follow_up_keywords)
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if follow_up_present:
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# 如果存在后续关键词，则从当前位置开始截取
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start_index = i
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end_index = start_index
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found_next_section = False
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for j in range(start_index + 1, len(split_sentences)):
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if re.match(r'\d+\.\d+(\.\d+)?', split_sentences[j].strip()):
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end_index = j
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found_next_section = True
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break
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if found_next_section:
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full_text = ' '.join(split_sentences[start_index:end_index]).strip()
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else:
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full_text = ' '.join(split_sentences[start_index:]).strip()
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pattern = r'^\s*([（(]\d+[)）]|[A-Za-z]?\d+(\.\d+)*(\s|\.|、)?)'
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data=re.sub(pattern,'',full_text)
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sentences2.append(data) # 存储有后续关键词的情况
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i = end_index if found_next_section else len(split_sentences)
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else:
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pattern = r'^\s*([（(]\d+[)）]|[A-Za-z]?\d+(\.\d+)*(\s|\.|、)?)'
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data = re.sub(pattern, '', sentence).replace('\n','').strip()
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sentences1.append(data) # 存储没有后续关键词的情况
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i += 1
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else:
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i += 1
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return sentences1, sentences2 # 返回两个列表
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def extract_sentences_from_json(json_path, keywords,follow_up_keywords):
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with open(json_path, 'r', encoding='utf-8') as file:
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data = json.load(file)
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"""从JSON数据中提取包含关键词的句子。"""
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return find_sentences_with_keywords(data, keywords,follow_up_keywords)
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#处理无效投标
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def extract_values_if_contains(data, includes):
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"""
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递归检查字典中的值是否包含列表 'includes' 中的内容。
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如果包含，将这些值添加到一个列表中并返回。
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参数:
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data (dict): 字典或从 JSON 解析得到的数据。
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includes (list): 包含要检查的关键词的列表。
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返回:
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list: 包含满足条件的值的列表。
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"""
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included_values = [] # 初始化结果列表
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# 定义递归函数来处理嵌套字典
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def recursive_search(current_data):
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if isinstance(current_data, dict):
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for key, value in current_data.items():
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if isinstance(value, dict):
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# 如果值是字典，递归搜索
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recursive_search(value)
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elif isinstance(value, str):
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# 如果值是字符串，检查是否包含任何 includes 中的关键词
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if any(include in value for include in includes):
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included_values.append(value)
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elif isinstance(current_data, list):
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for item in current_data:
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# 如果是列表，递归每个元素
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recursive_search(item)
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# 开始递归搜索
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recursive_search(data)
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return included_values
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#你是一个文本助手，文本内的信息以'...............'分割，你负责准确筛选所需的信息并返回，每块信息要求完整，不遗漏，你不得擅自进行总结或删减。
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#以上是从招标文件中摘取的内容，文本内之间的信息以'...............'分割，请你根据该内容回答：否决投标或拒绝投标或无效投标或使投标失效的情况有哪些？文本中可能存在无关的信息，请你准确筛选符合的信息并将它的序号返回。请以[x,x,x]格式返回给我结果，x为符合的信息的序号。
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#以上是原文内容，文本内的信息以'...............'分割，请你根据该信息回答：否决投标或拒绝投标或无效投标或使投标失效的情况有哪些？文本中可能存在无关的信息，请你准确筛选所需的信息并返回。最终结果以json列表格式返回给我，键名为'否决和无效投标情形'，你的回答完全忠于原文内容，且回答内容与原文内容一致，要求完整与准确，不能擅自总结或者概括。",
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def handle_query(file_path, user_query, output_file, result_key, keywords, truncate_json_path):
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excludes = ["说明表", "重新招标", "否决所有", "否决投标的条件", "备注：", "本人保证："]
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follow_up_keywords = [r'情\s*形\s*之\s*一', r'情\s*况\s*之\s*一', r'下\s*列', r'以\s*下']
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extracted_contents = extract_text_with_keywords(file_path, [keywords], follow_up_keywords) #字典结果
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all_texts1, all_texts2 = clean_dict_datas(extracted_contents, keywords, excludes) # 列表
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all_tables1, all_tables2 = extract_sentences_from_json(truncate_json_path, keywords, follow_up_keywords)
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qianwen_txt = all_texts1 + all_tables1
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# Proceed only if there is content to write
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if qianwen_txt:
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with open(output_file, 'w', encoding='utf-8') as file:
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# 初始化一个计数器
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counter = 1
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for content in qianwen_txt:
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file.write("..............."+'\n')
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# 写入内容前加上序号，后面接一个点和空格，然后是内容
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file.write(f"{counter}. {content}\n")
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# 更新计数器，每次循环递增
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counter += 1
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file_id = upload_file(output_file)
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print("starting qianwen-long...")
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qianwen_ans = qianwen_long(file_id, user_query)
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selected_contents = []
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num_list = process_string_list(qianwen_ans)
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print(num_list)
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for index in num_list:
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if index - 1 < len(qianwen_txt):
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content = qianwen_txt[index - 1] # 转换序号为索引（假设序号从1开始）
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selected_contents.append(content)
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selected_contents += all_texts2
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selected_contents += all_tables2
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# 创建一个字典来保存结果
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res = {result_key: selected_contents}
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# 将结果转换为JSON字符串
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# os.remove(output_file) # Remove the file after use
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# print(f"Deleted temporary file: {output_file}")
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else:
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res = {result_key: ""} # Set the response to empty if no contents were extracted
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return res
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def combine_find_invalid(file_path, output_dir, truncate_json_path,clause_path,truncate3):
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print("starting无效标与废标...")
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queries = [
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(r'否\s*决|无\s*效\s*投\s*标|被\s*拒\s*绝|予\s*以\s*拒\s*绝|投\s*标\s*失\s*效|投\s*标\s*无\s*效',
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"以上是从招标文件中摘取的内容，文本内之间的信息以'...............'分割，请你根据该内容回答：否决投标或拒绝投标或无效投标或投标失效的情况有哪些？文本中可能存在无关的信息，请你准确筛选符合的信息并将它的序号返回。请以[x,x,x]格式返回给我结果，x为符合的信息的序号，若情况不存在，返回[]。",
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os.path.join(output_dir, "temp1.txt"), "否决和无效投标情形"),
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(r'废\s*标',
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"以上是从招标文件中摘取的内容，文本内之间的信息以'...............'分割，请你根据该内容回答：废标项的情况有哪些？文本中可能存在无关的信息，请你准确筛选符合的信息并将它的序号返回。请以[x,x,x]格式返回给我结果，x为符合的信息的序号，若情况不存在，返回[]。",
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os.path.join(output_dir, "temp2.txt"), "废标项")
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]
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results = []
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# 使用线程池来并行处理查询
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with ThreadPoolExecutor() as executor:
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futures = []
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for keywords, user_query, output_file, result_key in queries:
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future = executor.submit(handle_query, file_path, user_query, output_file, result_key, keywords,
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truncate_json_path)
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futures.append(future)
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time.sleep(1) # 暂停1秒后再提交下一个任务
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for future in futures:
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results.append(future.result())
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#禁止投标
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print("starting不得存在的情形...")
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forbidden_res = find_forbidden(truncate_json_path, clause_path, truncate3)
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results.append(forbidden_res)
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combined_dict = {}
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for d in results:
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combined_dict.update(d)
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print("无效标与废标done...")
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return nest_json_under_key(combined_dict, "无效标与废标项")
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if __name__ == '__main__':
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start_time = time.time()
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truncate_json_path = "C:\\Users\\Administrator\\Desktop\\招标文件\\truncate_output.json"
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clause_path="C:\\Users\\Administrator\\Desktop\\招标文件\\clause.json"
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truncate3="C:\\Users\\Administrator\\Desktop\\招标文件\\zbtest12_qualification.pdf"
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output_dir = "C:\\Users\\Administrator\\Desktop\\货物标\\output"
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doc_path = 'C:\\Users\\Administrator\\Desktop\\货物标\\output\\zbtest11_invalid.docx'
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results = combine_find_invalid(doc_path, output_dir,truncate_json_path,clause_path,truncate3)
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end_time = time.time()
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print("Elapsed time:", str(end_time - start_time))
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print("Results:", results)
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