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Resume Ranker is a web application designed to rank resumes based on their content. It processes resumes and evaluates them based on specific criteria to provide a ranking score. This project leverages on analysis and ranking, with an intuitive FastAPI backend and PostgreSQL for data storage.
Brief Description of Your Project
Resume Ranker is a web application designed to rank resumes based on their content. It processes resumes and evaluates them based on specific criteria to provide a ranking score. This project leverages on analysis and ranking, with an intuitive FastAPI backend and PostgreSQL for data storage.
zip -r project_name.zip .
python3 -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
Install the necessary Python dependencies:
pip install -r requirements.txt
If you are using Docker, follow these steps to build and start the web application and the database:
docker-compose up --build
This will build the Docker images and start both the web application and the database.
Create a .env file in the root directory with the following variables:
postgres_user=your_db_user
postgres_password=your_db_password
postgres_db=your_db_name
DATABASE_URL=db_url
Once everything is set up, you can access the app:
uvicorn app.main:app --reload
0.0.0.0 on port 8000:
uvicorn app.main:app --host 0.0.0.0 --port 8000
To run the app with Docker, use the following command:
docker-compose up
This will start the application along with the PostgreSQL container. The app will be accessible at http://localhost:8000.
To run the tests, follow these steps:
Install the test dependencies (if any):
pip install -r requirements.txt
Run the tests with pytest:
pytest
Or, for a custom environment:
PYTHONPATH=.:$PYTHONPATH pytest tests
This will execute the tests and show the results in the terminal.
Selected from shared topics, language and repository description—not editorial ratings.
masaramravikiran /
ChatGPT said: This project is an AI-powered resume ranker that helps recruiters quickly evaluate candidates. The backend (built with FastAPI and Python) parses resumes, extracts skills, generates embeddings, and ranks candidates against a given job description. The frontend (built with React, TypeScript, and Tailwind) provides a simple dashboard.