iamsamkhan / ats-resume-llms-ai-matcher-automation

The Resume Matcher takes your resume and job descriptions as input, parses them using Python, and mimics the functionalities of an ATS, providing you with insights and suggestions to make your resume ATS-friendly.

ATS-AI-resume-automation

The Resume Matcher takes your resume and job descriptions as input, parses them using Python, and mimics the functionalities of an ATS, providing you with insights and suggestions to make your resume ATS-friendly.

By shamshad ahmed.

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The Resume Matcher takes your resume and job descriptions as input, parses them using Python, and mimics the functionalities of an ATS, providing you with insights and suggestions to make your resume ATS-friendly.

Resume Matcher

Resume Matcher is an AI Based Free & Open Source Tool. To tailor your resume to a job description. Find the matching keywords, improve the readability and gain deep insights into your resume.

Upvote us on ProductHunt 🚀.

Don't let your resume be a roadblock from getting your next job. Use Resume Matcher!

How does it work?

The Resume Matcher takes your resume and job descriptions as input, parses them using Python, and mimics the functionalities of an ATS, providing you with insights and suggestions to make your resume ATS-friendly.

The process is as follows:

1. Parsing: The system uses Python to parse both your resume and the provided job description, just like an ATS would.

2. Keyword Extraction: The tool uses advanced machine learning algorithms to extract the most relevant keywords from the job description. These keywords represent the skills, qualifications, and experiences the employer seeks.

3. Key Terms Extraction: Beyond keyword extraction, the tool uses textacy to identify the main key terms or themes in the job description. This step helps in understanding the broader context of what the resume is about.

4. Vector Similarity Using Qdrant: The tool uses Qdrant, a highly efficient vector similarity search tool, to measure how closely your resume matches the job description. The more similar they are, the higher the likelihood that your resume will pass the ATS screening.

How to install

Follow these steps to set up the environment and run the application.

1. Fork the repository here.

2. Clone the forked repository.

   git clone https://github.com/iamsamkhan/ATS-Resume-LLMs-AI-Matcher-automation.git
   cd Resume-Matcher

3. Create a Python Virtual Environment:

Install virtualenv in Python A virtual environment in Python allows you to create an isolated environment for your projects. It means that your projects can have their own dependencies – independent of every other project's dependencies.

With a Python virtual environment for each project, you are free to install different versions of the same Python package for each project. This is because every Python environment is independent of all the others.

At their core, virtual environments in Python are just directories containing a few scripts; consequently, you can set as many Python virtual environments as you like.

Let’s install virtualenv in Python!

virtualenv is easy to install. First, let's update pip.

pip install --upgrade pip

pip --version My output:

pip 22.0.3 Next, you can install virtualenv:

pip install virtualenv Now that virtualenv is installed, let's create a virtual environment in Python called mytest:

virtualenv -p python3.11 myenv

OR

4. Activate the Virtual Environment.

   • On Windows.

     env\Scripts\activate

   • On macOS and Linux.

     source env/bin/activate

   • pyenv installer

        curl https://pyenv.run | bash
     

   • Install desired python version

       pyenv install -v 3.11.0
     

   • pyenv with virtual enviroment

        pyenv virtualenv 3.11.0 venv
     

   • Activate virtualenv with pyenv

        pyenv activate venv
     

5. Install Dependencies:

   pip install -r requirements.txt

6. Prepare Data:

   • Resumes: Place your resumes in PDF format in the Data/Resumes folder. Remove any existing contents in this folder.
   • Job Descriptions: Place your job descriptions in PDF format in the Data/JobDescription folder. Remove any existing contents in this folder.

7. Parse Resumes to JSON:

   python run_first.py

8. Run the Application:

   streamlit run streamlit_app.py

Note: For local versions, you do not need to run "streamlit_second.py" as it is specifically for deploying to Streamlit servers.

Additional Note: The Vector Similarity part is precomputed to optimize performance due to the resource-intensive nature of sentence encoders that require significant GPU and RAM resources. If you are interested in leveraging this feature in a Google Colab environment for free, refer to the upcoming blog (link to be provided) for further guidance.

Docker

1. Build the image and start application

       docker-compose up

2. Open localhost:80 on your browser

Running the Web Application

The full stack Next.js (React and FastAPI) web application allows users to interact with the Resume Matcher tool interactively via a web browser.

Warning

The results returned from through the web app are currently entirely mocked / faked. This means that the results returned are not real and are just for demonstration purposes. This will be implemented with real data results in a future release.

To run the full stack web application (frontend client and backend api servers), follow the instructions over on the webapp README file.

Google Colab

1. Create an account in ngrok and get you token
2. 
3. Go to archive/resume_matcher_colab.ipynb and run the notebook.
4. Enter your ngrok token and run the notebook.
5. Copy the url and open it in your browser.
6. 

Cohere and Qdrant

1. Visit Cohere website registration and create an account.
2. Go to API keys and copy your cohere api key.
3. Visit Qdrant website and create an account.
4. Get your api key and cluster url.
5. Go to open dashboard in qdrant and enter your api key for only the first time

6. Now create a yaml file named config.yml in Scripts/Similarity/ folder. 7. The format for the conifg file should be as below: ```yaml cohere: api_key: cohere_key qdrant: api_key: qdrant_api_key url: qdrant_cluster_url ```

Heads Up! 📝

Your support means the world to us 💙. We're nurturing this project with an open-source community spirit, and we have an ambitious roadmap ahead! Here are some ways you could contribute and make a significant impact:

✨ Transform our Streamlit dashboard into something more robust.

💡 Improve our parsing algorithm, making data more accessible.

🖋 Share your insights and experiences in a blog post to help others.

Take the leap, contribute, and let's grow together! 🚀

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