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| import fitz # PyMuPDF | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| def extract_text_from_pdf(file_storage): | |
| with fitz.open(stream=file_storage.read(), filetype="pdf") as doc: | |
| return " ".join([page.get_text() for page in doc]) | |
| def rank_resumes_by_similarity(resume_files, job_description): | |
| resume_texts = [extract_text_from_pdf(f) for f in resume_files] | |
| documents = [job_description] + resume_texts | |
| vectorizer = TfidfVectorizer(stop_words='english') | |
| tfidf_matrix = vectorizer.fit_transform(documents) | |
| job_vector = tfidf_matrix[0] | |
| resume_vectors = tfidf_matrix[1:] | |
| similarities = cosine_similarity(job_vector, resume_vectors).flatten() | |
| ranked = sorted(zip(resume_files, similarities), key=lambda x: x[1], reverse=True) | |
| return [ | |
| {"filename": file.filename, "score": round(score, 4)} | |
| for file, score in ranked | |
| ] | |
| # utils/auth.py | |
| from functools import wraps | |
| from flask import request, jsonify | |
| from .models import User # adjust import to your structure | |
| def require_api_key(f): | |
| def decorated_function(*args, **kwargs): | |
| auth_header = request.headers.get('Authorization') | |
| if not auth_header or not auth_header.startswith('Bearer '): | |
| return jsonify({"error": "Missing or invalid Authorization header."}), 401 | |
| token = auth_header.split("Bearer ")[1].strip() | |
| user = User.query.filter_by(api_key=token).first() | |
| if not user: | |
| return jsonify({"error": "Invalid or revoked API key."}), 403 | |
| request.user = user # attach user to request for downstream use | |
| return f(*args, **kwargs) | |
| return decorated_function | |