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| from PyPDF2 import PdfReader | |
| from docx import Document | |
| import re | |
| from sentence_transformers import SentenceTransformer | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| import numpy as np | |
| import os | |
| # Initialize model for semantic similarity | |
| model = SentenceTransformer('all-MiniLM-L6-v2') | |
| def extract_text_from_file(file_path): | |
| if file_path.endswith('.pdf'): | |
| with open(file_path, 'rb') as f: | |
| reader = PdfReader(f) | |
| text = " ".join([page.extract_text() for page in reader.pages]) | |
| elif file_path.endswith('.docx'): | |
| doc = Document(file_path) | |
| text = " ".join([para.text for para in doc.paragraphs]) | |
| else: | |
| raise ValueError("Unsupported file format") | |
| # Clean text | |
| text = re.sub(r'\s+', ' ', text).strip() | |
| return text | |
| def evaluate_cv(cv_path, job_role): | |
| # Load job requirements (you would have these stored for each role) | |
| job_requirements = load_job_requirements(job_role) | |
| # Extract text from CV | |
| cv_text = extract_text_from_file(cv_path) | |
| # Create embeddings | |
| cv_embedding = model.encode(cv_text) | |
| requirements_embedding = model.encode(job_requirements["required_skills"]) | |
| # Calculate similarity | |
| similarity = cosine_similarity( | |
| cv_embedding.reshape(1, -1), | |
| requirements_embedding.reshape(1, -1) | |
| )[0][0] | |
| # Check minimum requirements | |
| rejection_reasons = [] | |
| meets_requirements = True | |
| # Check for minimum experience | |
| experience_pattern = r"(\d+)\s+years?" | |
| experience_matches = re.findall(experience_pattern, cv_text.lower()) | |
| total_experience = sum(int(match) for match in experience_matches) if experience_matches else 0 | |
| if total_experience < job_requirements["min_experience"]: | |
| meets_requirements = False | |
| rejection_reasons.append( | |
| f"Requires {job_requirements['min_experience']} years experience, found {total_experience}" | |
| ) | |
| # Check education | |
| education_keywords = job_requirements["required_education"] | |
| has_education = any(keyword.lower() in cv_text.lower() for keyword in education_keywords) | |
| if not has_education: | |
| meets_requirements = False | |
| rejection_reasons.append( | |
| f"Required education not found: {', '.join(education_keywords)}" | |
| ) | |
| # Check similarity threshold | |
| if similarity < 0.4: # Adjust threshold as needed | |
| meets_requirements = False | |
| rejection_reasons.append( | |
| "CV content doesn't sufficiently match the required skills" | |
| ) | |
| # Prepare CV summary for interview | |
| cv_summary = { | |
| "text": cv_text, | |
| "experience": total_experience, | |
| "skills_similarity": float(similarity), | |
| "education": has_education | |
| } | |
| return { | |
| "is_qualified": meets_requirements, | |
| "rejection_reasons": rejection_reasons, | |
| "cv_summary": cv_summary | |
| } | |
| def load_job_requirements(job_role): | |
| # In a real app, these would be stored in a database or files | |
| requirements = { | |
| "Software Engineer": { | |
| "min_experience": 2, | |
| "required_education": ["Bachelor in Computer Science", "BSc CS", "Engineering"], | |
| "required_skills": """ | |
| programming, algorithms, data structures, software development, | |
| testing, debugging, version control, agile methodologies | |
| """ | |
| }, | |
| "Data Scientist": { | |
| "min_experience": 3, | |
| "required_education": ["Master", "PhD", "Statistics", "Data Science"], | |
| "required_skills": """ | |
| machine learning, statistics, python, R, data analysis, | |
| data visualization, SQL, predictive modeling | |
| """ | |
| } | |
| } | |
| return requirements.get(job_role, requirements["Software Engineer"]) |