Spaces:
Runtime error
Runtime error
File size: 3,249 Bytes
e5e34f2 d06fa03 e5e34f2 39af895 e5e34f2 9c34257 8bd8e0f e5e34f2 680c8c1 e5e34f2 680c8c1 e5e34f2 4f082f2 e5e34f2 9da8328 ac5b468 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
import spacy
import gradio as gr
from spacy.pipeline import EntityRuler
from spacy import displacy
import jsonlines
from spacy.cli import download
download('en_core_web_sm')
nlp = spacy.load('en_core_web_sm')
# Create list with entity labels from jsonl file
with jsonlines.open("skill_patterns.jsonl") as f:
created_entities = [line['label'].upper() for line in f.iter()]
def extract_text_from_word(txt):
'''Opens en reads in a .doc or .docx file from path'''
return txt.replace('\n', ' ').replace('\t', ' ').lower()
def add_newruler_to_pipeline(skill_pattern_path):
'''Reads in all created patterns from a JSONL file and adds it to the pipeline after PARSER and before NER'''
# new_ruler = EntityRuler(nlp).from_disk(skill_pattern_path)
ruler=nlp.add_pipe("entity_ruler",after='parser')
ruler.from_disk(skill_pattern_path) # loads patterns only
def create_skill_set(doc):
'''Create a set of the extracted skill entities of a doc'''
return set([ent.label_.upper()[6:] for ent in doc.ents if 'skill' in ent.label_.lower()])
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(cv_set[resume_name])))
return (resume_name, pct_match)
add_newruler_to_pipeline("skill_patterns.jsonl")
def match(CV,JD):
resume_texts=[]
resume_texts.append(nlp(CV))
resume_names=['ABHI']
skillset_dict = create_skillset_dict(resume_names, resume_texts)
jd_skillset = create_skill_set(nlp(JD))
match_pairs = [match_skills(jd_skillset, skillset_dict, name) for name in skillset_dict.keys()]
if match_pairs[0]:
return match_pairs[0][1]
else:
return "No matching skill set."
exp=["Who is steve jobs?","What is coldplay?","What is a turing test?","What is the most interesting thing about our universe?","What are the most beautiful places on earth?"]
desc="A Machine Learning Based Resume Matcher, to compare Resumes with Job Descriptions. "
inp1=gr.inputs.Textbox(lines=10, placeholder=None, default="", label="Resume Details")
inp2=gr.inputs.Textbox(lines=10, placeholder=None, default="", label="Job Description")
out=gr.outputs.Textbox(type="auto",label="Match Score")
iface = gr.Interface(fn=match, inputs=[inp1,inp2], outputs=out,title="A Machine Learning Based Resume Matcher, to compare Resumes with Job Descriptions",article=desc,theme="huggingface",layout='vertical')
iface.launch(debug=True) |