logic.py

from keybert import KeyBERT
from sentence_transformers import SentenceTransformer, util

kw_model = KeyBERT()
model = SentenceTransformer('all-MiniLM-L6-v2')

def extract_keywords(text, top_n=10):
    return [kw[0] for kw in kw_model.extract_keywords(text, stop_words='english', top_n=top_n)]

def compare_skills(job_text, user_text):
    job_keywords = extract_keywords(job_text, top_n=8)
    user_keywords = extract_keywords(user_text, top_n=10)

    job_embeds = model.encode(job_keywords, convert_to_tensor=True)
    user_embeds = model.encode(user_keywords, convert_to_tensor=True)

    results = []
    for i, job_kw in enumerate(job_keywords):
        sim_scores = util.cos_sim(job_embeds[i], user_embeds)[0]
        max_score = float(sim_scores.max())
        is_matched = max_score > 0.5
        results.append((job_kw, is_matched))
    return job_keywords, [kw for kw, matched in results if not matched]