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import re
from collections import Counter
from sklearn.feature_extraction.text import TfidfVectorizer
def extract_keywords(text, top_n=10):
words = re.findall(r'\b\w+\b', text.lower())
common_words = set(["the", "and", "is", "to", "in", "for", "on", "with"]) # Stopwords
filtered_words = [word for word in words if word not in common_words]
return [word for word, _ in Counter(filtered_words).most_common(top_n)]
def compute_ats_score(resume_text, job_desc):
resume_keywords = extract_keywords(resume_text)
job_keywords = extract_keywords(job_desc)
match_score = len(set(resume_keywords) & set(job_keywords)) / len(set(job_keywords))
return f"ATS Score: {match_score:.2f}"
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