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c01955c | 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 | from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.metrics.pairwise import cosine_similarity
from langchain_community.document_loaders import PyPDFLoader
from typing import List
import os
def pdf_text_extractor(file_path="")->str:
if not os.path.exists(file_path):
raise FileNotFoundError(f"file not found at location {file_path}")
loader=PyPDFLoader(file_path=file_path)
documents=loader.load()
return documents.content
def extract_skills(text,SKILLS):
text = text.lower()
found = []
for skill in SKILLS:
if skill in text:
found.append(skill)
return found
def match_resume_jd(resume_txt:str,jd:str)->float:
vectorizer=TfidfTransformer()
vectors=vectorizer.fit_transform([resume_txt,jd])
score=cosine_similarity(vectors[0],vectors[1])[0][0]
return round(score*100,2)
def final_score(resume_file_path:str, jd_text,SKILLS:List[str]=["python", "java", "ml", "react", "docker", "aws"]):
resume_text=pdf_text_extractor(file_path=resume_file_path)
skills = extract_skills(resume_text,SKILLS=SKILLS)
skill_score = len(skills) * 5 # har skill = 5 marks
jd_score = match_resume_jd(resume_text, jd_text)
total = min(skill_score + jd_score, 100)
return total
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