481 lines
16 KiB
Python
481 lines
16 KiB
Python
import re
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import docx
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import os
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import copy
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from pprint import pprint
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from paddlenlp import Taskflow
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import pdfplumber
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from win32com import client as wc
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from pdf2docx import Converter
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# 文件路径
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schema = ['姓名', '所在地', '户口所在地', '籍贯', '婚姻状况', '民族', '电话', 'tel', '应聘职位', '到岗时间', '学历', '毕业学校', '专业', '期望薪资',
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'在校时间', '电子邮箱', '工作经验', 'Email', '性别', '年龄', '身份证号', '技能特长'
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]
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schema_dict = {'姓名': 'name', '所在地': 'account', '户口所在地': 'accounts', '婚姻状况': 'gam', '民族': 'nation',
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'电话': 'phone', '应聘职位': 'job_name', '到岗时间': 'come_time', '学历': 'education', '毕业学校': 'school',
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'专业': 'specialty', '期望薪资': 'hope_money', '在校时间': 'at_school', '电子邮箱': 'mail', '工作经验': 'work_exp',
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'Email': 'mails', '性别': 'gender', '年龄': 'age', '籍贯': 'account', 'tel': 'tels', '身份证号': 'id_card',
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'技能特长': 'specialty_do'}
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# 简历初始文档
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data_mode = {
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"interview_name": "吴操",
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"interview_type": 1,
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"interview_sign": 0,
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"hope_money": "",
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"feedback": 0,
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"interview_round": 0,
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"event_time": "",
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"name": "",
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"phone": "",
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"job_name": "",
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"hr_name": "吴操",
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"work_exp": 0,
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"interview_stage": 1,
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"owner_name": 2,
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"education": 1,
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"work_undergo": [],
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"project_undergo": [],
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"work_list": [],
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"school": "",
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"at_school": "",
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"specialty": "",
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"specialty_do": [],
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"mmended_state": 0,
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"mail": "",
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"account": "",
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"id_card": "",
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"gender": "",
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"age": 0,
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"gam": "",
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"interview_state": 1,
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"counts": 1,
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"nation": "汉",
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"review": [],
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"upgrade": [],
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"come_time": "",
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"now_money": "",
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"men_state": 1,
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"teacher_state": 1,
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"teacher_back": 1,
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"offer_state": 1,
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"offer_exam_state": 1,
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"notice_state": 1,
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"pass_why": 0,
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"pass_text": [],
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}
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def chkworlkandtime(listdata):
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"""
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获取工作经历中任职的公司名称和对应的在岗时间
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:param dictdata:
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:return:返回列表格式
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"""
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res = {}
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for i in listdata:
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for key, datalist in i.items():
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trueDict = {}
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for data in datalist:
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if data['text'] in trueDict:
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if data['probability'] <= trueDict[data['text']]['probability']:
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continue
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trueDict.update({
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data['text']: {
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'end': data['end'],
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'probability': data['probability'],
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'start': data['start'],
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}
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})
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trueList = []
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for key1, value1 in trueDict.items():
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value1.update({
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'text': key1
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})
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trueDict1 = copy.deepcopy(value1)
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trueList.append(trueDict1)
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trueList.sort(key=lambda item: item['start'])
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res.update({key: trueList})
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ress = []
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if res != {}:
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for i in range(len(res['公司名'])):
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date = {
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'name': res['公司名'][i]['text'],
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'time': res['时间'][i]['text']
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}
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ress.append(str(date))
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return ress
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def getText_docx(filename): # docx 转text
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"""将docx读成text"""
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doc = docx.Document(filename)
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fullText = []
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for i in doc.paragraphs: # 迭代docx文档里面的每一个段落
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fullText.append(i.text) # 保存每一个段落的文本
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numTables = doc.tables # 如果有表格的内容存放在这
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if len(numTables) > 0:
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for table in numTables:
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row_count = len(table.rows)
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col_count = len(table.columns)
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for i in range(row_count):
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for j in range(col_count):
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fullText.append(table.cell(i, j).text)
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return '\n'.join(fullText)
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def pdf_docx(url, filename):
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"""
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将pdf文件转为docx文件
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:param url:
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:param filename:
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:return:
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"""
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# 获取文件名称
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file_name = filename.split('.')[0]
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# pdf文件名称
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pdf_name = url + f"/{filename}"
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# docx文件名称
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docx_name = url + f"/{file_name}.docx"
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# 加载pdf文档
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cv = Converter(pdf_name)
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cv.convert(docx_name, start=0, end=12)
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cv.close()
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def getText_pdf(filename):
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"""将pdf读成text"""
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with pdfplumber.open(filename) as pdf_file:
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content = ''
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for i in range(len(pdf_file.pages)):
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page_text = pdf_file.pages[i]
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page_content = page_text.extract_text()
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if page_content:
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content = content + page_content + "\n"
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return content
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def doc_docx(url, filename):
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"""
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将doc文件转为docx文件
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:param filename:
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:return:
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"""
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word = wc.Dispatch("Word.Application")
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doc = word.Documents.Open(url + f"/{filename}")
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name = filename.split('.')[0]
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doc.SaveAs(url + f'/{name}.docx', 12) # 12为docx
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doc.Close()
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word.Quit()
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def clash(date, retain, pop):
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"""
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解决词性搜索时，最后的结果只取有值的一个
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例如'户口所在地','籍贯'只取默认的籍贯返回，（户口所在地有值把值给籍贯）
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:param date: 原数据
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:param retain: 要固定返回给前端的数据 户口所在地
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:param pop: 要删除的那个字段 籍贯
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:return:
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"""
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if date[retain] != '':
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date.pop(pop)
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else:
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date[retain] = date[pop]
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date.pop(pop)
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def get_date(schema, dates, schema_dict):
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"""
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把第三方获取的数据筛选出想要的基本信息
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:param schema:中文的词性标注
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:param dates:原数据
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:param schema_dict:对应中文的英文
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:return: 返回取出概率最大的基本信息数据
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"""
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date = data_mode
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for i in schema:
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text = dates[0].get(i, '')
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# 如果数据中没有搜到对应的键，返回空字符串
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if text == '':
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date[schema_dict[i]] = text
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else:
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if len(text) == 1:
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date[schema_dict[i]] = text[0]['text']
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else:
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aa = {}
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num = []
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for dic in text:
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aa[dic['probability']] = dic['text']
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num.append(dic['probability'])
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# 取出概率最大的值
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date[schema_dict[i]] = aa[max(num)]
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# 解决邮箱冲突的问题
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clash(date, 'mail', 'mails')
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# 解决户口所在地冲突的问题
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clash(date, 'account', 'accounts')
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# 解决电话冲突的问题
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clash(date, 'phone', 'tels')
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return date
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def fmtTxt(txt, istable=0):
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# 所有关键字
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chkStr = ['自我评价', '自我描述', '个人优势', '项目经历', '项目经验', '项目描述', '教育经历', '学习经历', '工作经历', '工作经验', '实习经历',
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'技能特长', '技能', '特长', '专长', '技能专长', '专业技能', '职业技能', '个人评价']
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# 自我描述
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chkList1 = ['自我评价', '自我描述', '个人优势', '个人评价']
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# 项目经验
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chkList2 = ['项目经历', '项目经验', '项目描述']
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# 教育背景
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chkList3 = ['教育经历', '学习经历']
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# 工作经历
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chkList4 = ['工作经历', '工作经验', '实习经历']
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# 个人技能
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chkList5 = ['技能特长', '技能', '特长', '专长', '技能专长', '专业技能', '职业技能']
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fmtList = [] # 返回拼接好的字符串列表
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trueIndex = 0
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fmtStr = ''
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nowChkList = []
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# 判断while循环是否需要停止
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stop_int = 0
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for index, i in enumerate(txt):
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if istable:
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text = i
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else:
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text = i.text
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# text = re.sub('\s+', '', text).lstrip() # 字符串去除空格和换行符
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# 没有检测出关键字
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if not fmtStr:
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# 自我描述
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for i in chkList1:
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# 判断是不是以关键字开头
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if not text.startswith(i, 0):
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continue
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else:
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if i in text:
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fmtStr = text
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nowChkList = [chk for chk in chkStr if chk not in chkList1]
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# 检测出关键字证明需要继续循环
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stop_int = 1
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break
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if fmtStr:
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continue
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# 项目经验
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for i in chkList2:
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if i in text:
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fmtStr = text
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nowChkList = [chk for chk in chkStr if chk not in chkList2]
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stop_int = 1
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break
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if fmtStr:
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continue
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# 教育背景
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for i in chkList3:
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if i in text:
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fmtStr = text
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nowChkList = [chk for chk in chkStr if chk not in chkList3]
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stop_int = 1
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break
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if fmtStr:
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continue
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# 工作经历
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for i in chkList4:
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# 判断是不是以关键字开头
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if not text.startswith(i, 0):
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continue
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else:
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if i in text:
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fmtStr = text
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nowChkList = [chk for chk in chkStr if chk not in chkList4]
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stop_int = 1
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break
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if fmtStr:
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continue
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# 个人技能
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for i in chkList5:
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if i in text:
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fmtStr = text
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nowChkList = [chk for chk in chkStr if chk not in chkList5]
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stop_int = 1
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break
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continue
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else:
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isTure = 1
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for i in nowChkList:
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if i in text:
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isTure = 0
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break
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if isTure:
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fmtStr += text
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continue
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else:
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fmtStrTrue = fmtStr
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fmtList.append(fmtStrTrue)
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trueIndex = index
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# fmtStr = ''
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# nowChkList = []
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# 剩余没有检索的部分
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txt1 = txt[trueIndex:]
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return fmtList, txt1, stop_int
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# 当列表全部检索完毕需要停止循环
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if fmtStr:
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fmtStrTrue = fmtStr
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fmtList.append(fmtStrTrue)
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stop_int = 0
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txt1 = txt[trueIndex:]
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return fmtList, txt1, stop_int
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def fmtList(txtlist, dates):
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chkList1 = ['自我评价', '自我描述', '个人优势']
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chkList2 = ['项目经历', '项目经验', '项目描述']
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chkList3 = ['教育经历', '学习经历']
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chkList4 = ['工作经历', '工作经验', '实习经历']
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chkList5 = ['技能特长', '技能', '特长', '专长', '技能专长', '专业技能', '职业技能']
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# 自我评价
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review = []
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# 项目经验
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project = []
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# 工作经验
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work = []
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# 教育经验
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upgrade = []
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# 技能特长
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specialty = []
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for text in txtlist:
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ischk = 0
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# 自我评价
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for i in chkList1:
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if i in text:
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review.append(text)
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ischk = 1
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break
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if ischk:
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continue
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# 项目经验
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for i in chkList2:
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if i in text:
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project.append(text)
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ischk = 1
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break
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if ischk:
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continue
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# 工作经验
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for i in chkList4:
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if i in text:
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work.append(text)
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ischk = 1
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break
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if ischk:
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continue
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# 教育经历
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for i in chkList3:
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if i in text:
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upgrade.append(text)
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ischk = 1
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break
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if ischk:
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continue
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# 自我评价
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for i in chkList5:
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if i in text:
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specialty.append(text)
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ischk = 1
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break
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if ischk:
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continue
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# 取出工作经验里面的公司名和时间
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work_list = []
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if len(work) > 0:
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works = ''
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for i in work:
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works += i
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schema = ['公司名', '时间']
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ie = Taskflow('information_extraction', schema=schema)
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text_lists = ie(works)
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work_list = chkworlkandtime(text_lists)
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# review自我评价, project项目经验，work工作经验，work具体工作的公司和时间，upgrade教育经历，specialty技能特长
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dates.update({
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'review': review,
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'project_undergo': project,
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'work_undergo': work,
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'work_list': work_list,
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'upgrade': upgrade,
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'specialty_do': specialty,
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})
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return dates
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def get_resume(file, path_data):
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url = path_data + f"/{file}"
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if os.path.splitext(file)[1] == '.pdf':
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pdf_docx(path_data, file) # 转为docx
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name = file.split('.')[0]
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open_txt = docx.Document(path_data + f"/{name}.docx") # 打开docx
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os.remove(path_data + f"/{name}.docx") # 删除生成的文件
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txt = getText_pdf(url) # 打开pdf格式文件转txt
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# txt = getText_docx(PATH_DATA + f"\{name}.docx")
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elif os.path.splitext(file)[1] == '.docx':
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open_txt = docx.Document(url) # 打开docx，将用来读取每一段的内容
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txt = getText_docx(url) # 打开docx格式文件转txt
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elif os.path.splitext(file)[1] == '.doc':
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doc_docx(path_data, file) # 转为docx
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name = file.split('.')[0]
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open_txt = docx.Document(path_data + f"/{name}.docx") # 打开docx
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txt = getText_docx(path_data + f"/{name}.docx") # 打开docx格式文件转txt
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os.remove(path_data + f"/{name}.docx") # 删除生成的文件
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ie = Taskflow('information_extraction', schema=schema) # 花费时间会安装文件
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# pprint(ie(txt)) # 姓名，电话，电子邮箱，民族，毕业院校，专业，工作经验，婚姻状况
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# 获取的基础数据
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text_lists = ie(txt)
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# 处理后的基本数据
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dates = get_date(schema, text_lists, schema_dict)
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# 打开docx获取的每一段数据
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txt_list = open_txt.paragraphs
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# 获取的文档内容
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txt_list1 = []
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stop_int = 1
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txt1 = txt_list
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while stop_int:
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txt_list2, txt1, stop_int = fmtTxt(txt1)
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txt_list1 += txt_list2
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# print(txt_list1)
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numTables = open_txt.tables # 获取表格里面的内容
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table_list = []
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if len(numTables) > 0:
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for table in numTables:
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row_count = len(table.rows)
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col_count = len(table.columns)
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for i in range(row_count):
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for j in range(col_count):
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texts = table.cell(i, j).text
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# texts = re.sub('\s+', '', texts).lstrip() # 字符串去除空格和换行符
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if not texts:
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continue
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if texts in table_list:
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continue
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table_list.append(texts)
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if table_list:
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stop_table = 1
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table1 = table_list
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while stop_table:
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table_list2, table1, stop_table = fmtTxt(table1, istable=1)
|
||
txt_list1 += table_list2
|
||
# print(txt_list1)
|
||
# review自我评价,project项目经验，work工作经验，upgrade教育经历，specialty技能特长
|
||
# 把两部分的数据合起来返回前端,数据都在dates中
|
||
fmtList(txt_list1, dates)
|
||
# pprint(dates)
|
||
a = 1
|
||
|
||
return dates
|
||
|
||
|
||
if __name__ == '__main__':
|
||
get_resume(file, path_data)
|