This project aims to address the limitations of traditional ATS (Applicant Tracking System) scanners while providing a more intelligent, user-centric solution for job seekers and hiring teams.
Checkout the demo : YouTube
It compares the candidate's resume with the specific job requirements and background fit needs and provides a more accurate analysis of the candidate's strengths and relevancy with the job requirement.
Different options for LLM: Our application relies on different large language models (currently: Llama3, DeepSeek r1) to review and score the candidate's strengths.
No Login Flow: Breaking the extra barrier between the user and the application. Get started instantly; no sign-up hassle.
Local Processing: As there's no user-centric database, most of your data is stored and managed from your cached memory.
Interactive Charts: Dynamic, interactive charts to visualize the candidate's strengths, weaknesses, and key insights to make quick decisions.
Personalized Feedback: Job description-specific feedback and recommendations to improve the candidate's performance.
Backend Server Files : Click here
Run the following command to build and start the application using Docker Compose:
docker-compose up --build
This will:
- Build the Docker image using the Dockerfile
- Install dependencies
- Start the application inside a container
Once the container is up and running, the application will be accessible at:
http://localhost:3000
To stop the running containers, use:
docker-compose down
If you find this project helpful, please consider giving it a star on GitHub! ✨