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| import os | |
| import random | |
| import string | |
| from datetime import datetime | |
| from supabase import create_client, Client | |
| from transformers import pipeline, AutoModelForSequenceClassification,AutoModelForCausalLM, AutoTokenizer | |
| from sentence_transformers import SentenceTransformer | |
| from huggingface_hub import login | |
| import gradio as gr | |
| import os | |
| from sentence_transformers import SentenceTransformer, util | |
| from transformers import pipeline | |
| from huggingface_hub import login | |
| import gradio as gr | |
| # Load environment variables (for Hugging Face integration) | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| # Hugging Face Authentication (for private models) | |
| huggingface_token = os.getenv("HUGGINGFACE_TOKEN") | |
| login(token=huggingface_token) # Authenticate with Hugging Face | |
| # Supabase setup | |
| SUPABASE_URL = os.getenv("SUPABASE_URL") | |
| SUPABASE_KEY = os.getenv("SUPABASE_KEY") | |
| supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY) | |
| # Load Sentence Transformer model | |
| model = SentenceTransformer('all-MiniLM-L6-v2') | |
| # Function to calculate skill matching score | |
| def calculate_skill_score(jd, cv): | |
| skills = [ # Core Programming Languages | |
| 'Python', 'Java', 'JavaScript', 'C++', 'C#', 'Ruby', 'Go', | |
| 'Rust', 'Swift', 'Kotlin', 'PHP', 'TypeScript', | |
| # Web Technologies | |
| 'React', 'Angular', 'Vue.js', 'Node.js', 'Django', | |
| 'Flask', 'Spring', '.NET', 'Express.js', 'Laravel', | |
| # Cloud & DevOps | |
| 'AWS', 'Azure', 'Google Cloud', 'Docker', 'Kubernetes', | |
| 'Jenkins', 'GitHub Actions', 'GitLab CI', 'Terraform', | |
| # Databases | |
| 'SQL', 'MongoDB', 'PostgreSQL', 'MySQL', 'Cassandra', | |
| 'Redis', 'Oracle', 'SQLite', 'MariaDB', | |
| # Data Science & AI | |
| 'Machine Learning', 'Data Analysis', 'Deep Learning', | |
| 'TensorFlow', 'PyTorch', 'Scikit-learn', 'Pandas', | |
| 'NumPy', 'Keras', 'Natural Language Processing', | |
| # Big Data | |
| 'Hadoop', 'Spark', 'Apache Kafka', 'Elasticsearch', | |
| # Cybersecurity | |
| 'Network Security', 'Penetration Testing', 'Ethical Hacking', | |
| # Mobile Development | |
| 'Android', 'iOS', 'React Native', 'Flutter', 'Xamarin'] | |
| jd_skills = [skill for skill in skills if skill.lower() in jd.lower()] | |
| cv_skills = [skill for skill in skills if skill.lower() in cv.lower()] | |
| common_skills = set(jd_skills) & set(cv_skills) | |
| return len(common_skills) / len(jd_skills) * 4 if jd_skills else 0 | |
| # Function to calculate role match score | |
| def calculate_role_score(jd, cv): | |
| roles = ['Software Engineer', 'Data Scientist', 'DevOps', 'Frontend Developer', 'Project Manager', 'Delivery Manager', 'Delivery Head', 'BI'] | |
| matched_roles = [role for role in roles if role.lower() in jd.lower() and role.lower() in cv.lower()] | |
| return len(matched_roles) / len(roles) * 3 if roles else 0 | |
| # Function to calculate textual similarity (JD and CV) | |
| def calculate_text_similarity(jd, cv): | |
| jd_embedding = model.encode(jd, convert_to_tensor=True) | |
| cv_embedding = model.encode(cv, convert_to_tensor=True) | |
| cosine_sim = util.pytorch_cos_sim(jd_embedding, cv_embedding) | |
| return cosine_sim.item() | |
| # Function to calculate the overall rating | |
| def calculate_profile_rating(jd, cv): | |
| skill_score = calculate_skill_score(jd, cv) | |
| role_score = calculate_role_score(jd, cv) | |
| text_similarity = calculate_text_similarity(jd, cv) | |
| text_similarity_score = text_similarity * 3 | |
| total_score = skill_score + role_score + text_similarity_score | |
| total_score = min(total_score, 10) # Ensure score doesn't exceed 10 | |
| return round(total_score, 1) | |
| # Gradio interface to interact with the model | |
| def process_jd_cv(jd, cv): | |
| # Truncate both JD and CV to the first 3500 characters | |
| jd = jd[:3500] | |
| cv = cv[:3500] | |
| # Calculate the final rating | |
| rating = calculate_profile_rating(jd, cv) | |
| return f"Profile Rating: {rating}/10" | |
| # Gradio interface to accept inputs from the user | |
| iface = gr.Interface(fn=process_jd_cv, | |
| inputs=["text", "text"], # JD and CV text inputs | |
| outputs="text", # Display the rating score | |
| live=True) | |
| # Launch the Gradio interface | |
| iface.launch(share=True) |