from Models import Models from ResumeSegmenter import ResumeSegmenter from datetime import datetime from dateutil import parser import re from string import punctuation class ResumeParser: def __init__(self, ner, ner_dates, zero_shot_classifier, tagger): self.models = Models() self.segmenter = ResumeSegmenter(zero_shot_classifier) self.ner, self.ner_dates, self.zero_shot_classifier, self.tagger = ner, ner_dates, zero_shot_classifier, tagger self.parsed_cv = {} def parse(self, resume_lines): resume_segments = self.segmenter.segment(resume_lines) print("Parsing the Resume...") for segment_name in resume_segments: if segment_name == "contact_info": contact_info = resume_segments[segment_name] self.parse_contact_info(contact_info) elif segment_name == "work_and_employment": resume_segment = resume_segments[segment_name] self.parse_job_history(resume_segment) return self.parsed_cv def parse_contact_info(self, contact_info): contact_info_dict = {} name = self.find_person_name(contact_info) email = self.find_contact_email(contact_info) self.parsed_cv['Name'] = name contact_info_dict["Email"] = email self.parsed_cv['Contact Info'] = contact_info_dict def find_person_name(self, items): class_score = [] splitter = re.compile(r'[{}]+'.format(re.escape(punctuation.replace("&", "") ))) classes = ["person name", "address", "email", "title"] for item in items: elements = splitter.split(item) for element in elements: element = ''.join(i for i in element.strip() if not i.isdigit()) if not len(element.strip().split()) > 1: continue out = self.zero_shot_classifier(element, classes) highest = sorted(zip(out["labels"], out["scores"]), key=lambda x: x[1])[-1] if highest[0] == "person name": class_score.append((element, highest[1])) if len(class_score): return sorted(class_score, key=lambda x: x[1], reverse=True)[0][0] return "" def find_contact_email(self, items): for item in items: match = re.search(r'[\w.+-]+@[\w-]+\.[\w.-]+', item) if match: return match.group(0) return "" def parse_job_history(self, resume_segment): idx_job_title = self.get_job_titles(resume_segment) current_and_below = False if not len(idx_job_title): self.parsed_cv["Job History"] = [] return if idx_job_title[0][0] == 0: current_and_below = True job_history = [] for ls_idx, (idx, job_title) in enumerate(idx_job_title): job_info = {} job_info["Job Title"] = self.filter_job_title(job_title) # company if current_and_below: line1, line2 = idx, idx+1 else: line1, line2 = idx, idx-1 job_info["Company"] = self.get_job_company(line1, line2, resume_segment) if current_and_below: st_span = idx else: st_span = idx-1 # Dates if ls_idx == len(idx_job_title) - 1: end_span = len(resume_segment) else: end_span = idx_job_title[ls_idx+1][0] start, end = self.get_job_dates(st_span, end_span, resume_segment) job_info["Start Date"] = start job_info["End Date"] = end job_history.append(job_info) self.parsed_cv["Job History"] = job_history def get_job_titles(self, resume_segment): classes = ["organization", "institution", "job title", "role"] idx_line = [] for idx, line in enumerate(resume_segment): has_verb = False sentence = self.models.get_flair_sentence(line) self.tagger.predict(sentence) for entity in sentence.get_spans('pos'): if entity.tag.startswith("V"): has_verb = True break if not has_verb: out = self.zero_shot_classifier(line, classes) class_score = zip(out["labels"], out["scores"]) highest = sorted(class_score, key=lambda x: x[1])[-1] if highest[0] == "job title": idx_line.append((idx, line)) return idx_line def get_job_dates(self, st, end, resume_segment): search_span = resume_segment[st:end] dates = [] for line in search_span: for dt in self.get_ner_in_line(line, "DATE"): if self.isvalidyear(dt.strip()): dates.append(dt) if len(dates): first = dates[0] exists_second = False if len(dates) > 1: exists_second = True second = dates[1] if len(dates) > 0: if self.has_two_dates(first): d1, d2 = self.get_two_dates(first) return self.format_date(d1), self.format_date(d2) elif exists_second and self.has_two_dates(second): d1, d2 = self.get_two_dates(second) return self.format_date(d1), self.format_date(d2) else: if exists_second: st = self.format_date(first) end = self.format_date(second) return st, end else: return (self.format_date(first), "") else: return ("", "") def filter_job_title(self, job_title): job_title_splitter = re.compile(r'[{}]+'.format(re.escape(punctuation.replace("&", "") ))) job_title = ''.join(i for i in job_title if not i.isdigit()) tokens = job_title_splitter.split(job_title) tokens = [''.join([i for i in tok.strip() if (i.isalpha() or i.strip()=="")]) for tok in tokens if tok.strip()] classes = ["company", "organization", "institution", "job title", "responsibility", "details"] new_title = [] for token in tokens: if not token: continue res = self.zero_shot_classifier(token, classes) class_score = zip(res["labels"], res["scores"]) highest = sorted(class_score, key=lambda x: x[1])[-1] if highest[0] == "job title": new_title.append(token.strip()) if len(new_title): return ', '.join(new_title) else: return ', '.join(tokens) def has_two_dates(self, date): years = self.get_valid_years() count = 0 for year in years: if year in str(date): count+=1 return count == 2 def get_two_dates(self, date): years = self.get_valid_years() idxs = [] for year in years: if year in date: idxs.append(date.index(year)) min_idx = min(idxs) first = date[:min_idx+4] second = date[min_idx+4:] return first, second def get_valid_years(self): current_year = datetime.today().year years = [str(i) for i in range(current_year-100, current_year)] return years def format_date(self, date): out = self.parse_date(date) if out: return out else: date = self.clean_date(date) out = self.parse_date(date) if out: return out else: return date def clean_date(self, date): try: date = ''.join(i for i in date if i.isalnum() or i =='-' or i == '/') return date except: return date def parse_date(self, date): try: date = parser.parse(date) return date.strftime("%m-%Y") except: try: date = datetime(date) return date.strftime("%m-%Y") except: return 0 def isvalidyear(self, date): current_year = datetime.today().year years = [str(i) for i in range(current_year-100, current_year)] for year in years: if year in str(date): return True return False def get_ner_in_line(self, line, entity_type): if entity_type == "DATE": ner = self.ner_dates else: ner = self.ner return [i['word'] for i in ner(line) if i['entity_group'] == entity_type] def get_job_company(self, idx, idx1, resume_segment): job_title = resume_segment[idx] if not idx1 <= len(resume_segment)-1: context = "" else:context = resume_segment[idx1] candidate_companies = self.get_ner_in_line(job_title, "ORG") + self.get_ner_in_line(context, "ORG") classes = ["organization", "company", "institution", "not organization", "not company", "not institution"] scores = [] for comp in candidate_companies: res = self.zero_shot_classifier(comp, classes)['scores'] scores.append(max(res[:3])) sorted_cmps = sorted(zip(candidate_companies, scores), key=lambda x: x[1], reverse=True) if len(sorted_cmps): return sorted_cmps[0][0] return context