resume-parse/tools/irafa.py

# -*- coding: utf-8 -*-
# @Author: privacy
# @Date:   2022-07-07 13:12:17
# @Last Modified by:   privacy
# @Last Modified time: 2022-07-18 13:50:39

# 内部人才市场简历模板

import re
import json

import requests
from requests.adapters import HTTPAdapter

import pdfplumber
from docx import Document

# path = "d:\\desktop\\内部人才市场简历模板.docx"
path = "d:\\desktop\\内部人才市场简历模板.pdf"


class Inner(object):
    """docstring for Inner"""
    def __init__(self):
        super(Inner, self).__init__()
        self.keywords = ["姓名", "性别", "出生日期", "民族", "籍贯", "健康状况", "政治面貌", "参加工作时间", "外语水平", "专业技术资格（取得时间）", "计算机水平", "熟悉专业有何专长", "工作单位", "现任职务", "任职时间", "联系电话", "对报名岗位认识及工作", "对报名岗位认识及工作设想", "意向地区", "意向岗位", "意向单位", "意向专业", "职业证书", "资格等级", "取得日期", "学校/培训机构", "专业", "起始时间", "毕业时间", "姓名", "职业", "与本人关系"]
        self.json_obj = self.get_translate()

    def get_translate(self):
        # 转译数据库字段名
        with open("./resources/translate.json", "r", encoding="utf-8") as ff:
            json_obj = json.load(ff)
        return json_obj

    def parse_line(self, line):
        result = []
        key = None
        for cell in line:
            if cell and ''.join(cell.split()) in self.keywords:
                key = ''.join(cell.split())
            elif cell and key:
                schema = {key:cell}
                result.append(schema)
                key = None
        return result

    # 解析word
    def parse_word_layout(self, path):
        result = []
        doc = Document(path)
        lo = {}
        tables = doc.tables
        for _table in tables[:]:
            for i, row in enumerate(_table.rows[:]):
                row_content = []
                for cell in row.cells[:]:
                    c = cell.text
                    row_content.append(c)
                lo[len(lo.keys())] = row_content
        
        kwln = -1
        kwline = None
        for key in lo.keys():
            # pdb.set_trace()
            for val in lo[key]:# 通过全关键词，判断此行是否为关键词行
                if val and ''.join(val.split()) not in self.keywords:# 有非关键字元素，非关键词行，判断是否为关键词行元素
                    # pdb.set_trace()
                    for c in lo[key]:
                        # pdb.set_trace()
                        if c and ''.join(c.split()) in self.keywords:# 非关键词行元素
                            result.extend(self.parse_line(lo[key]))
                            break
                    else:# 关键词行元素
                        schema = dict()
                        for key, val in zip(kwline, lo[key]):
                            if key:
                                schema[key] = val
                        if "学校/培训机构" in schema.keys():
                            schema["学习经历"] = "学习经历"
                        elif "与本人关系" in schema.keys():
                            schema["家庭成员"] = "家庭成员"
                        elif "意向地区" in schema.keys():
                            schema["职业发展管理"] = "职业发展管理"
                        elif "职业证书" in schema.keys():
                            schema["职业资格证书"] = "职业资格证书"
                        result.append(schema)
                        break
                    break
            else:
                # print("此行为关键词行")
                kwline = [''.join(cell.split()) for cell in lo[key]]
                kwln = len(lo[key])

        job = {"工作经历":"工作经历"}
        flag = None
        for p in doc.paragraphs:
            text = p.text.replace("：", ":")
            if ":" in text:
                text = re.sub(r'(\w+)\W{0,2}:', r'\n\1:', text)
                for line in text.split("\n"):
                    if line.strip():
                        i = line.split(":")
                        if job.get(i[0].strip()):
                            result.append(job)
                            job = {"工作经历":"工作经历"}
                        job[i[0].strip()] = i[1].strip()
                        flag = i[0].strip()
            elif flag == "工作描述":
                job["工作描述"] += '\n' + text.strip()
        else:
            result.append(job)
        return result

    # 解析pdf
    def parse_pdf_layout(self, path):
        result = []
        lo = {}
        with pdfplumber.open(path) as pdf:
                for page in pdf.pages:
                    for table in page.extract_tables():
                        for line in table:
                            # lo[len(lo.keys())] = [cell for cell in line if cell]
                            lo[len(lo.keys())] = line

        kwln = -1
        kwline = None
        for key in lo.keys():
            # pdb.set_trace()
            for val in lo[key]:# 通过全关键词，判断此行是否为关键词行
                if val and ''.join(val.split()) not in self.keywords:# 有非关键字元素，非关键词行，判断是否为关键词行元素
                    # pdb.set_trace()
                    for c in lo[key]:
                        # pdb.set_trace()
                        if c and ''.join(c.split()) in self.keywords:# 非关键词行元素
                            result.extend(self.parse_line(lo[key]))
                            break
                        if c == "对报名岗位\n认 识及工作":
                            print(''.join(c.split()))
                            break
                    else:# 关键词行元素
                        schema = dict()
                        for key, val in zip(kwline, lo[key]):
                            if key:
                                schema[key] = val
                        if "学校/培训机构" in schema.keys():
                            schema["学习经历"] = "学习经历"
                        elif "与本人关系" in schema.keys():
                            schema["家庭成员"] = "家庭成员"
                        elif "意向地区" in schema.keys():
                            schema["职业发展管理"] = "职业发展管理"
                        elif "职业证书" in schema.keys():
                            schema["职业资格证书"] = "职业资格证书"
                        result.append(schema)
                        break
                    break
            else:
                # print("此行为关键词行")
                kwline = [''.join(cell.split()) for cell in lo[key]]
                kwln = len(lo[key])

        job = {"工作经历":"工作经历"}
        flag = None

        with pdfplumber.open(path) as pdf:
            for page in pdf.pages:
                for predict in page.extract_words():
                    # print(predict['text'])
                    text = predict['text'].replace("：", ":")
                    if ":" in text:
                        text = re.sub(r'(\w+)\W{0,2}:', r'\n\1:', text)
                        for line in text.split("\n"):
                            if line.strip():
                                i = line.split(":")
                                if job.get(i[0].strip()):
                                    result.append(job)
                                    job = {"工作经历":"工作经历"}
                                job[i[0].strip()] = i[1].strip()
                                flag = i[0].strip()
                    elif flag == "工作描述":
                        job["工作描述"] += '\n' + text.strip()
                else:
                    result.append(job)
        return result

    # 格式化数据
    def formatter(self, datalist):
        result = dict()
        for d in datalist:
            if len(d) == 1:
                for key in d.keys():
                    result[key] = d[key]
            else:
                for k in list(d.keys()):
                    if k == "".join(d[k].split()):
                        d.pop(k)
                        if result.get(k):
                            result[k].append(d)
                        else:
                            result[k] = [d]

        normal = self.json_obj["base"]
        itenormal = self.json_obj["base"]
        edunormal = self.json_obj["tal_training_experience"]
        jobnormal = self.json_obj["tal_his_job"]
        cetnormal = self.json_obj["tal_vocational_qualification_certificate"]
        family = self.json_obj["tal_family_social_relation"]

        for key in normal.keys():
            if result.get(key):
                result[normal[key]] = result[key]
                result.pop(key)

        for idx in range(len(result['职业发展管理'])):
            for key in itenormal.keys():
                if result['职业发展管理'][idx].get(key):
                    result['职业发展管理'][idx][itenormal[key]] = result['职业发展管理'][idx][key]
                    result['职业发展管理'][idx].pop(key)

        for idx in range(len(result['学习经历'])):
            for key in edunormal.keys():
                if result['学习经历'][idx].get(key):
                    result['学习经历'][idx][edunormal[key]] = result['学习经历'][idx][key]
                    result['学习经历'][idx].pop(key)

        for idx in range(len(result['工作经历'])):
            for key in jobnormal.keys():
                if result['工作经历'][idx].get(key):
                    result['工作经历'][idx][jobnormal[key]] = result['工作经历'][idx][key]
                    result['工作经历'][idx].pop(key)

        for idx in range(len(result['职业资格证书'])):
            for key in cetnormal.keys():
                if result['职业资格证书'][idx].get(key):
                    result['职业资格证书'][idx][cetnormal[key]] = result['职业资格证书'][idx][key]
                    result['职业资格证书'][idx].pop(key)

        for idx in range(len(result['家庭成员'])):
            for key in family.keys():
                if result['家庭成员'][idx].get(key):
                    result['家庭成员'][idx][family[key]] = result['家庭成员'][idx][key]
                    result['家庭成员'][idx].pop(key)

        tit = {
            "基本信息":"base",
            "职业发展管理":"intent_job",
            "学习经历":"tal_training_experience",
            "工作经历":"tal_his_job",
            "项目经历":"tal_his_project",
            "培训经历":"tal_training_experience",
            "获奖情况":"tal_reward_punishment",
            "语言能力":"tal_language",
            "职业资格证书":"tal_vocational_qualification_certificate",
            "专业技能":"tal_professional_tech_certificate",
            "家庭成员":"tal_family_social_relation"
        }

        for key in tit.keys():
            if result.get(key):
                result[tit[key]] = result[key]
                result.pop(key)

        return result

    # 推送后端
    def push_back(self, result):
        url = "http://192.168.1.110:9999/talent/getResumeData"
        session = requests.Session()
        session.mount('http://', HTTPAdapter(max_retries = 3))
        try:
            headers = {
                'contentType':'Application/json'
            }
            response = session.post(url=url, headers=headers, json={"ResumeData": result}, timeout=10)
            print(response.text)
        except Exception as e:
            print(e)

    def predict(self, path):
        if path.endswith(".docx"):
            result = self.formatter(self.parse_word_layout(path))
            self.push_back(result)
            print(self.formatter(self.parse_word_layout(path)))
        elif path.endswith(".pdf"):
            result = self.formatter(self.parse_pdf_layout(path))
            self.push_back(result)
            print(self.formatter(self.parse_pdf_layout(path)))



if __name__ == "__main__":
    i = Inner()
    i.predict(path)