Resume AI

Resume AI is a project that demonstrates proficiency in various technologies and software design principles. This application uses very rudimentary AI to analyze and score resumes.

Technologies Used

• Backend:
  • Django
  • Celery (with Redis as message broker)
  • Flower (for Celery monitoring)
  • SQLite (for development, easily swappable with PostgreSQL)
• AI Services:
  • OpenAI
  • Llama

Getting Started

To run the project after cloning, use Docker Compose:

docker-compose build
docker-compose up

Required ENV Variables:

• REPLICATE_API_TOKEN
• OPENAI_API_KEY
• DEBUG: True or False
• SECRET_KEY
• ALLOWED_HOSTS
• REDIS_URL

Architecture

• Django: The main framework used for the application.
• Celery: Used for handling asynchronous tasks. Included webhook code in celery_webhook.txt for external observation.
• Redis: Acts as the message broker for Celery.
• Flower: Provides a web-based monitoring interface for Celery tasks.
• SQLite: Used as the database for quick development. Can be easily replaced with PostgreSQL for production use.

Design Patterns and Principles

The project demonstrates proficiency in Object-Oriented Programming (OOP) design patterns and SOLID principles:

• Factory Pattern: Implemented in the user_scoring views to switch between different AI services (OpenAI or Llama) for resume scoring.
• Abstraction: Used throughout the project to separate concerns and improve code maintainability.
• SOLID Principles: Applied to ensure a robust and scalable codebase.

Code Quality

• Comprehensive comments have been added to classes and methods containing significant logic.
• Unit testing of views and tasks.

Future Improvements

• Implement user authentication and authorization.
• AI training for resume scoring.
• Develop a React.js front end.
• Implement unit testing for user_scoring services and serializers
• Implement unit testing for job_postings services and serializers
• Implement integration testing