skill_matching.py

def create_skillset_dict(resume_names, resume_texts):
    '''Create a dictionary containing a set of the extracted skills. Name is key, matching skillset is value'''
    skillsets = [create_skill_set(resume_text) for resume_text in resume_texts]

    return dict(zip(resume_names, skillsets))

def match_skills(vacature_set, cv_set, resume_name):
    '''Get intersection of resume skills and job offer skills and return match percentage'''
    
    if len(vacature_set) < 1:
        print('could not extract skills from job offer text')   
    else:
        pct_match = round(len(vacature_set.intersection(cv_set[resume_name])) / len(vacature_set) * 100, 0)
        print(resume_name + " has a {}% skill match on this job offer".format(pct_match))
        print('Required skills: {} '.format(vacature_set))
        print('Matched skills: {} \n'.format(vacature_set.intersection(skillset_dict[resume_name])))
        
        return (resume_name, pct_match)
        
add_newruler_to_pipeline(skill_pattern_path)

resume_texts, resume_names = create_tokenized_texts_list(extension)

skillset_dict = create_skillset_dict(resume_names, resume_texts)

# example of job offer text (string). Can input your own.
vacature_text = vacatures_df[vacatures_df['soort_vacature'] == 'Data Scientist'].skills.iloc[13]

# Create a set of the skills extracted from the job offer text
vacature_skillset = create_skill_set(nlp(vacature_text))

# Create a list with tuple pairs containing the names of the candidates and their match percentage
match_pairs = [match_skills(vacature_skillset, skillset_dict, name) for name in skillset_dict.keys()]