356 lines
14 KiB
Python
356 lines
14 KiB
Python
import camelot
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import re
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import os
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import json
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import numpy as np
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from datetime import datetime
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# 读取PDF
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import PyPDF2
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# 分析PDF的layout，提取文本
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from pdfminer.high_level import extract_pages
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from pdfminer.layout import LTTextContainer, LTRect
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import pdfplumber
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import mysql.connector
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# 数据处理流程
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# 1. 解析pdf标题，获取多级标题名称及页码范围
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# 2. 基于规则选出需要解析的标题下内容
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# 3. 根据需要解析的标题及页码，获取所有表格内容，并转化成带语义的指标
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# 4. 根据需要解析的标题及页码，获取所有非表格类的正文
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# 5. 文本和表格指标调用大模型抽取原始指标
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# 6. 根据规则讲原始指标转化为最终显示指标
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STR_PATTERN = '营业收入|净利润|变动比例|损益|现金流量净额|现金流|每股收益|总资产|资产总额|收益率'
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def get_md5(str):
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import hashlib
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m = hashlib.md5()
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m.update(str.encode('utf-8'))
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return m.hexdigest()
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#获取指标的表头信息
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def get_num_info(array,row_num,col_num,x,y):
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num_info=""
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for j in range(col_num):
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if len(str(array[x][j])) > 50:
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continue
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num_info += str(array[x][j])
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for i in range(row_num):
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if len(str(array[i][y])) > 50:
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continue
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num_info += str(array[i][y])
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return num_info.replace('%','')
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def get_parse_pages(page_dict):
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"""
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:return: 返回一个存储需要解析的页码文本
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"""
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return "all"
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# 读取pdf文件中文本内容，不包括表格
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def get_text_content(pdf_path):
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"""
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:return: 返回pdf文件中文本内容，不包括表格
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"""
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page_obj = []
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# 我们从PDF中提取页面
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for pagenum, page in enumerate(extract_pages(pdf_path)):
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page_text = ''
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text_obj = {}
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# 初始化检查表的数量
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table_num = 0
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first_element= True
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table_extraction_flag= False
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# # 打开pdf文件
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pdf = pdfplumber.open(pdf_path)
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# 查找已检查的页面
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page_tables = pdf.pages[pagenum]
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# 找出本页上的表格数目
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tables = page_tables.find_tables()
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# 找到所有的元素
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page_elements = [(element.y1, element) for element in page._objs]
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# 对页面中出现的所有元素进行排序
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page_elements.sort(key=lambda a: a[0], reverse=True)
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# 查找组成页面的元素
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for i,component in enumerate(page_elements):
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# 提取PDF中元素顶部的位置
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pos= component[0]
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# 提取页面布局的元素
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element = component[1]
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# 检查该元素是否为文本元素
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if isinstance(element, LTTextContainer):
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# 检查文本是否出现在表中
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if table_extraction_flag == False:
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# 使用该函数提取每个文本元素的文本和格式
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line_text = element.get_text().replace('\s+', '').replace('\n', '').replace('\r', '')
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# 将每行的文本追加到页文本
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if len(line_text) > 5:
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page_text += line_text
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# 附加每一行包含文本的格式
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else:
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# 省略表中出现的文本
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pass
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# 检查表的元素
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if isinstance(element, LTRect):
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# 如果第一个矩形元素
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if first_element == True and (table_num+1) <= len(tables):
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# 找到表格的边界框
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lower_side = page.bbox[3] - tables[table_num].bbox[3]
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upper_side = element.y1
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# 将标志设置为True以再次避免该内容
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table_extraction_flag = True
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# 让它成为另一个元素
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first_element = False
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# 检查我们是否已经从页面中提取了表
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if element.y0 >= lower_side and element.y1 <= upper_side:
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pass
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elif not isinstance(page_elements[i+1][1], LTRect):
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table_extraction_flag = False
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first_element = True
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table_num+=1
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text_obj['page_num'] = pagenum
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text_obj['text'] = page_text
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page_obj.append(text_obj)
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# 打印提取的文本
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# print(page_obj)
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return page_obj
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# 读取pdf中的表格,并将表格中指标和表头合并，eg: 2022年1季度营业收入为xxxxx
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def get_table_measure(file_path, page_num="all"):
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"""
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:return: pdf中的表格,并将表格中指标和表头合并，eg: 2022年1季度营业收入为xxxxx
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"""
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measure_obj = []
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tables = camelot.read_pdf(file_path, pages=page_num, strip_text=' ,\n', copy_text=['h'])
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for t in tables:
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data_dict = {}
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measure_list = []
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arr = np.array(t.data)
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rows, cols = arr.shape
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if rows == 1 and cols == 1:
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continue
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arr_str = ''.join([''.join(map(str, row)) for row in arr])
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matches = re.findall(STR_PATTERN, arr_str)
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if len(matches) > 0:
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arr = np.array(t.data)
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rows, cols = arr.shape
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row_num , col_num = -1 , -1
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# 使用嵌套循环遍历数组，获取第一个数值位置
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for i in range(rows):
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for j in range(cols):
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if re.match(r'^[+-]?(\d+(\.\d*)?|\.\d+)(%?)$', str(arr[i, j])):
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if j == cols-1:
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row_num , col_num = i , j
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break
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elif (re.match(r'^[+-]?(\d+(\.\d*)?|\.\d+)(%?)$', str(arr[i, j+1]))
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or str(arr[i, j+1]) == '-'):
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row_num , col_num = i , j
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break
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else:
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continue
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break
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# 遍历数值二维数组，转成带语义的指标
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if row_num != -1 and col_num != -1:
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for i in range(row_num,arr.shape[0]):
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for j in range(col_num,arr.shape[1]):
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if arr[i, j] == '-' or arr[i, j] == '' or len(arr[i, j]) > 20:
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continue
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else:
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num_info = get_num_info(arr,row_num,col_num,i,j)
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measure_list.append(f"{num_info}为{arr[i, j]}")
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# print(f"{num_info}为{arr[i, j]}")
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else:
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pass
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if len(measure_list) > 0:
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data_dict["measure_list"] = measure_list
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data_dict["page_num"] = f"{str(t.page)}_{str(t.order)}"
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measure_obj.append(data_dict)
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# print(measure_obj)
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return measure_obj
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# 文本和表格数据给大模型，返回大模型抽取原始指标列表
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def get_measure_from_llm(user_prompt):
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"""
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:return: 文本和表格数据给大模型，返回大模型抽取原始指标列表
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"""
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import random
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from http import HTTPStatus
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from dashscope import Generation
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llm_measure_list = []
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system_prompt = '''
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你是一个优秀的金融分析师，从给定的数据报告中自动提取以下10个关键财务指标。指标包括：
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2023年营业收入
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2023年合计营业收入
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2023年调整后营业收入
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2022年营业收入
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2022年合计营业收入
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2022年调整后营业收入
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2023年营业收入变动比例
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2023年营业收入比上年同期增减
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2023年归属母公司净利润
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2023年归属于上市公司股东的净利润
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2023年归属母公司净利润变动比例
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请确保只抽取这些指标，并且每个指标的输出格式为：指标名:指标值
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所有的指标值必须从用户提供的信息中抽取，不允许自己生成，如果找不到相关指标，指标值显示为-
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<数据报告>
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<user_prompt>
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</数据报告>
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'''
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system_prompt = system_prompt.replace('<user_prompt>', user_prompt)
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response = Generation.call(
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model='qwen-turbo',
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prompt = system_prompt,
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seed=random.randint(1, 10000),
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top_p=0.1,
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result_format='message',
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enable_search=False,
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max_tokens=1500,
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temperature=0.85,
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repetition_penalty=1.0
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)
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if response.status_code == HTTPStatus.OK:
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result = response['output']['choices'][0]['message']['content']
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llm_measure_list = result.split('\n')
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return llm_measure_list
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else:
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print('Request id: %s, Status code: %s, error code: %s, error message: %s' % (
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response.request_id, response.status_code,
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response.code, response.message
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))
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return "llm_error"
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# 解析大模型抽取的指标，并插入到数据库
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def parse_llm_measure_to_db(measure_info,type,conn,cursor):
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create_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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# 执行SQL语句，插入数据
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insert_query = '''
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INSERT INTO ori_measure_list
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(file_id, file_name, type, page_number, table_index, ori_measure_id, ori_measure_name, ori_measure_value, create_time, update_time)
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VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
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'''
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file_id = '1111111'
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file_name = '科润智控.pdf'
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llm_measure = measure_info['llm_measure']
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page_num = measure_info['page_num']
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table_index = '0'
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if type == 'table':
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table_index = measure_info['table_index']
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for measure_obj in llm_measure:
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measure_obj = measure_obj.replace('\n', '').replace('\r', '').replace(' ', '')
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if ':' in measure_obj:
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ori_measure_name = measure_obj.split(':')[0]
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ori_measure_value = measure_obj.split(':')[1].replace('+', '')
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if '-' in ori_measure_value:
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ori_measure_value = "-"
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if '.' in ori_measure_name:
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ori_measure_name = ori_measure_name.split('.')[1]
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ori_measure_id = get_md5(ori_measure_name)
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data_to_insert = (file_id, file_name, type, int(page_num), int(table_index), ori_measure_id, ori_measure_name, ori_measure_value, create_time, create_time)
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cursor.execute(insert_query, data_to_insert)
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print(f"{type},{page_num},{table_index},{ori_measure_name},{ori_measure_value}")
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# 提交事务
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conn.commit()
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return ""
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# 根据measure_config中的规则，更新原始指标的显示指标
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def update_ori_measure(conn,cursor):
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# 执行SQL语句，更新数据
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update_query = '''
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UPDATE ori_measure_list
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SET measure_id = %s, measure_name = %s
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WHERE ori_measure_id = %s and ori_measure_value !='-'
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'''
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# 执行SQL语句，更新数据
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select_query = '''
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SELECT measure_id,measure_name,ori_measure_id FROM measure_config
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'''
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cursor.execute(select_query)
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records = cursor.fetchall()
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for record in records:
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data_to_update = (record[0], record[1], record[2])
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cursor.execute(update_query, data_to_update)
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conn.commit()
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if __name__ == "__main__":
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start_time = datetime.now()
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print("开始时间：", start_time.strftime("%Y-%m-%d %H:%M:%S"))
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path = "/Users/zhengfei/Desktop/科润智控1.pdf"
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table_info = get_table_measure(path)
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# text_info = get_text_content(path)
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# # 数据库连接对象
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# # 连接到MySQL数据库
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# conn = mysql.connector.connect(
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# host="121.37.185.246",
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# user="financial",
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# password="financial_8000",
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# database="financial_report"
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# )
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# 创建一个cursor对象来执行SQL语句
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# cursor = conn.cursor()
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for table_obj in table_info:
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table_measure_obj = {}
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table_page_num = table_obj['page_num'].split("_")[0]
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table_index = table_obj['page_num'].split("_")[1]
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table_measure = ','.join(table_obj['measure_list'])
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if table_page_num == '3':
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print(f"第{table_page_num}页表格指标为：{table_measure}")
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table_llm_measure = get_measure_from_llm(table_measure)
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if table_page_num == '3':
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print(f"第{table_page_num}页表格llm指标为：{table_llm_measure}")
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# table_measure_obj['page_num'] = table_page_num
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# table_measure_obj['table_index'] = table_index
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# table_measure_obj['llm_measure'] = table_llm_measure
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# parse_llm_measure_to_db(table_measure_obj,'table',conn,cursor)
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# for text_obj in text_info:
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# text_measure_obj = {}
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# text_page_num = text_obj['page_num']
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# text = text_obj['text']
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# if len(text) > 10:
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# text_llm_measure = get_measure_from_llm(text)
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# text_measure_obj['page_num'] = text_page_num
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# text_measure_obj['llm_measure'] = text_llm_measure
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# parse_llm_measure_to_db(text_measure_obj,'text',conn,cursor)
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# print(text_llm_measure)
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# update_ori_measure(conn,cursor)
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# cursor.close()
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# conn.close()
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# measure_info =['1. 2023年营业收入: 983698831.48', '2. 2023年营业收入变动比例: 15.10%', '3. 2023年归属母公司净利润: - (未在报告中找到)', '4. 2023年归属母公司净利润变动比例: - (未在报告中找到)', '5. 2023年毛利率: (营业收入 - 主营业务成本) / 营业收入 = (983698831.48 - 793604607.43) / 983698831.48', '6. 2022年毛利率: (主营业务收入 - 主营业务成本) / 主营业务收入 = (854682261.31 - 690932741.27) / 854682261.31', '7. 2023年主营业务收入: 983698831.48', '8. 2022年主营业务收入: 854682261.31']
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# parse_llm_measure_to_db(measure_info)
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# get_measure_from_llm()
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end_time = datetime.now()
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print("结束时间：", end_time.strftime("%Y-%m-%d %H:%M:%S"))
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#print(pdf_data) |