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283 lines
13 KiB
283 lines
13 KiB
5 months ago
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import re
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import os
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import docx
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from docx.document import Document
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from docx.text.paragraph import Paragraph
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from docx.parts.image import ImagePart
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from qwen_agent.agents import Assistant
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from docx.oxml.table import CT_Tbl
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from docx.oxml.text.paragraph import CT_P
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import shutil
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import re
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import json_repair
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import uuid
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# 记录程序开始的时间戳
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def getOutlineLevel(inputXml):
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"""
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功能 从xml字段中提取出<w:outlineLvl w:val="number"/>中的数字number
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参数 inputXml
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返回 number
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"""
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start_index = inputXml.find('<w:outlineLvl')
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end_index = inputXml.find('>', start_index)
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number = inputXml[start_index:end_index + 1]
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number = re.search("\d+", number).group()
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return number
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def isTitle(paragraph):
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"""
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功能 判断该段落是否设置了大纲等级
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参数 paragraph:段落
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返回 None:普通正文，没有大纲级别 0:一级标题 1:二级标题 2:三级标题
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"""
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# 如果是空行，直接返回None
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if paragraph.text.strip() == '':
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return None
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# 如果该段落是直接在段落里设置大纲级别的，根据xml判断大纲级别
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paragraphXml = paragraph._p.xml
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if paragraphXml.find('<w:outlineLvl') >= 0:
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return getOutlineLevel(paragraphXml)
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# 如果该段落是通过样式设置大纲级别的，逐级检索样式及其父样式，判断大纲级别
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targetStyle = paragraph.style
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while targetStyle is not None:
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# 如果在该级style中找到了大纲级别，返回
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if targetStyle.element.xml.find('<w:outlineLvl') >= 0:
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return getOutlineLevel(targetStyle.element.xml)
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else:
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targetStyle = targetStyle.base_style
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# 如果在段落、样式里都没有找到大纲级别，返回None
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return None
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# 该行只能有一个图片
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def is_image(graph: Paragraph, doc: Document):
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images = graph._element.xpath('.//pic:pic') # 获取所有图片
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for image in images:
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for img_id in image.xpath('.//a:blip/@r:embed'): # 获取图片id
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part = doc.part.related_parts[img_id] # 根据图片id获取对应的图片
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if isinstance(part, ImagePart):
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return True
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return False
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# 获取图片（该行只能有一个图片）
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def get_ImagePart(graph: Paragraph, doc: Document):
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images = graph._element.xpath('.//pic:pic') # 获取所有图片
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for image in images:
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for img_id in image.xpath('.//a:blip/@r:embed'): # 获取图片id
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part = doc.part.related_parts[img_id] # 根据图片id获取对应的图片
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if isinstance(part, ImagePart):
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return part
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return None
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#寻找标题名称
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def findTitleName(docxPath):
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yield '文档图片信息检查----检查是否存在详细设计方案'
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document = docx.Document(docxPath)
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# 逐段读取docx文档的内容
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titleWords=[]
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firstTitle = 0
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secondTitle = 0
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sanjiTitle = 0
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for paragraph in document.paragraphs:
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# 判断该段落的标题级别
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# 这里用isTitle()临时代表，具体见下文介绍的方法
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text = paragraph.text
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if text.strip():#非空判断
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level = isTitle(paragraph)
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if level=="0":
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firstTitle+=1
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secondTitle = 0
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if(text.find("附件")>=0):
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continue
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titleWords.append("一级标题:".format(firstTitle)+text)
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elif level=="1":
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secondTitle+=1
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sanjiTitle=0
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# words.append("\t"+"{}.{}".format(firstTitle,secondTitle)+text)
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# titleWords.append("第{}章的二级标题:".format(firstTitle,firstTitle,secondTitle)+text)
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elif level=="2":
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sanjiTitle += 1
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# words.append("\t"+"{}.{}".format(firstTitle,secondTitle)+text)
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# titleWords.append("第{}章的三级标题".format(firstTitle, secondTitle,firstTitle, secondTitle,sanjiTitle) + text)
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findTitleName_llm_cfg = {
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# 'model':"qwen2-72b",
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# 'model_server': 'http://127.0.0.1:1025/v1', # base_url, also known as api_base
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'model': "qwen2-72b-instruct",
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'model_server': 'DashScope', # base_url, also known as api_base
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'api_key': 'sk-ea89cf04431645b185990b8af8c9bb13',
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}
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findTitleName_bot = Assistant(llm=findTitleName_llm_cfg,
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name='Assistant',
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# system_message='1：这样的是一级标题。1.1：这样的是二级标题。1.1.1：这样的是三级标题'
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)
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prompt='''\n是文档的大纲，一级标题组成，哪一章存在与方案相关的内容
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类似详细设计方案,详细服务方案，详细建设方案为最相关的，优先选择
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类似设计方案，服务方案，建设方案为次相关，次级选择
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类似方案是最后选择
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按照这样的顺序选择最合适的
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你只能从这两个答案中选择一个：{"name":"一级标题名称","answer":"存在"}或{"name":"","answer":"不存在"}，不做过多的解释,严格按回答格式作答
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'''
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# print("\n".join(titleWords)+prompt)
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messages = [({'role': 'user', 'content': "\n".join(titleWords)+prompt})]
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runList=[]
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for rsp in findTitleName_bot.run(messages):
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runList.append(rsp)
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data = runList[len(runList) - 1][0]["content"]
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parsed_data = json_repair.loads(data.replace('`', ''))
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print(parsed_data)
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if(parsed_data["answer"]=="存在"):
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print("存在",parsed_data["name"])
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yield parsed_data["name"]
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else:
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print("不存在",parsed_data["name"])
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yield "文档图片信息检查----未找到与详细设计方案相关内容，无法进行图文检查"
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def saveImage(fileName,titleName,imagePath):
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fristName=""
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doc = docx.Document(fileName)
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for paragraph in doc.paragraphs:
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# 判断该段落的标题级别
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# 这里用isTitle()临时代表，具体见下文介绍的方法
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text = paragraph.text
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if text.strip(): # 非空判断
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level = isTitle(paragraph)
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if level == "0":
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fristName = text
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print(text)
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if level:
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levelText = f"{int(level) + 1}级标题-" + text
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else:
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# 空说明是表格或者图片
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r = is_image(paragraph, doc)
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if r and fristName == titleName:
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part = get_ImagePart(paragraph, doc)
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img_name = levelText+"_"+ os.path.basename(part.partname)
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with open(f'{imagePath}/{img_name}', "wb") as f:
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f.write(part.blob)
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#保存完成后，上传大模型进行分析
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def checkImageText(filename):
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llm_cfg_vl = {
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#'model': 'qwen1.5-72b-chat',qwen2-72b-instruct
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'model':"qwen-vl-max",
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'model_server': 'DashScope', # base_url, also known as api_base
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'api_key': 'sk-ea89cf04431645b185990b8af8c9bb13',
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}
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botImage = Assistant(llm=llm_cfg_vl,
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name='Assistant',
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# system_message="你是一个地理专家，可以准确的判断地理位置，如果你不确定，可以使用工具"1_image4
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)
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llm_cfg = {
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#'model': 'qwen1.5-72b-chat',
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'model':"qwen2-72b-instruct",
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'model_server': 'DashScope', # base_url, also known as api_base
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'api_key': 'sk-ea89cf04431645b185990b8af8c9bb13',
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}
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bot = Assistant(llm=llm_cfg,
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name='Assistant',
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# description='使用RAG检索并回答，支持文件类型：PDF/Word/PPT/TXT/HTML。'
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)
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for titleName in findTitleName(filename):
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yield titleName
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if (titleName != "文档图片信息检查----未找到与详细设计方案相关内容，无法进行图文检查"):
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yield "文档图片信息检查----文档内容解析中"
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imagePath = "Image" + str(uuid.uuid4())
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os.mkdir(imagePath)
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saveImage(filename,titleName,imagePath)
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imagePathList = os.listdir(imagePath)
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count = 0
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resMap={}
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for image in imagePathList:
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count+=1
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yield f"文档图片信息检查---当前处理进度{count}/{len(imagePathList)}"
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outpath=os.path.join("imagePath", image)
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print(outpath)
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messagesImage = [{'role': 'user', "content": [{"image": outpath}, {"text": '提取图片中的信息，每个信息进行自动分类，不要出现与图中无关的信息，不要删减，不要修改，不要总结内容，不做过多的解释,严格按要求作答'}]}]
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runListImage = []
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for rsp in botImage.run(messagesImage):
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runListImage.append(rsp)
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data = runListImage[len(runListImage) - 1][0]["content"]
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print(str(data))
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prompt='''
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依次上述内容是否与文档有关，你只能在[无关，有关]选项中选择答案,
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按照这样的格式回答[{“text”：“内容”,"answer":"答案"},{“text”：“内容”,"answer":"答案"}]不做过多的解释,严格按回答格式作答
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'''
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messages = [{'role': 'user', 'content': [{'text':str(data)+prompt},{"file":filename}]}]
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runList = []
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for rsp in bot.run(messages):
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runList.append(rsp)
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textdata = runList[len(runList) - 1][0]["content"]
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print(textdata)
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parsed_data = json_repair.loads(textdata)
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print(parsed_data)
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for res in parsed_data:
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if (res["answer"] == "无关"):
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print("无关", res["name"])
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map = resMap.get(image)
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if map:
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#存在map说明之前已经保存过了
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resMap[image]=map+"，"+res["text"]
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else:
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resMap[image]=res["text"]
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out=''
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if(len(resMap)>0):
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for key,value in resMap:
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out+=f"在{key}图片中,{value}以上内容在文档中未出现相关描述<br>"
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yield out
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else:
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yield "文档图片信息检查----图文符合要求"
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shutil.rmtree(imagePath)
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# except Exception as e:
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# yield f"文档图片信息检查----未找到与详细设计方案相关内容，无法进行图文检查"
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# return
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for i in checkImageText("1.docx"):
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print(i)
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# import docx
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# doc = docx.Document('1.docx')
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# dict_rel = doc.part._rels # rels其实是个目录
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# for rel in dict_rel:
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# rel = dict_rel[rel]
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# print("rel", rel.target_ref)
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# if "image" in rel.target_ref:
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# # create_dir(desc_path)
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# img_name = re.findall("/(.*)", rel.target_ref)[0] # windos:/
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# print("img_name", img_name)
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# word_name = os.path.splitext("1.docx")[0]
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# print("word_name", word_name)
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# #检查文件路径分隔符（os.sep），并根据不同的操作系统（Windows或Unix/Linux）处理文件名。
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# if os.sep in word_name:
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# new_name = word_name.split('\\')[-1]
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# else:
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# new_name = word_name.split('/')[-1]
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# img_name = f'{new_name}_{img_name}'
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# print(img_name)
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# desc_path='workspace'
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# with open(f'{desc_path}/{img_name}', "wb") as f:
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# f.write(rel.target_part.blob)
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# #
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# # # prompt='''
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# # # .根据上述文本判断，是否为非泛化的公司或组织名称，你可以使用工具利用互联网查询，你只能在[非泛化的公司或组织名称,公益组织,统称,泛化名称,政府单位,机关单位,学校，委员单位]选项中选择答案,回答格式[{“placeName”：“名称”,"回答":"答案"}]，不做过多的解释,严格按回答格式作答;
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# # # '''
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# llm_cfg_vl = {
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# #'model': 'qwen1.5-72b-chat',qwen2-72b-instruct
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# 'model':"qwen-vl-max",
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# 'model_server': 'DashScope', # base_url, also known as api_base
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# 'api_key': 'sk-ea89cf04431645b185990b8af8c9bb13',
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# }
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# botvl = Assistant(llm=llm_cfg_vl,
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# name='Assistant',
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# # system_message="你是一个地理专家，可以准确的判断地理位置，如果你不确定，可以使用工具"1_image4
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# )
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# messages = [{'role': 'user', "content": [{"image": "workspace/1.png"},{"text": '提取图片中的信息，每个信息进行自动分类，不要出现与图中无关的信息，不要删减，不要修改，不要总结内容，不做过多的解释,严格按要求作答'}]}]
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# runList = []
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# for rsp in botvl.run(messages):
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# runList.append(rsp)
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# print(rsp)
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# data = runList[len(runList) - 1][0]["content"]
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# print(str(data))
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