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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.

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 FastEmbed: The tool usesFastEmbed, a highly efficient embedding system, 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.

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

1. Fork the repositoryhere.

2. Clone the forked repository.

   git clone https://github.com/<YOUR-USERNAME>/Resume-Matcher.gitcd Resume-Matcher

3. Create a Python Virtual Environment:

   • Usingvirtualenv:

     Note: Check how to install virtualenv on your system herelink.

     virtualenv env

   OR

   • Create a Python Virtual Environment:

     python -m venv env

4. Activate the Virtual Environment.

   • On Windows.

     env\Scripts\activate

   • On macOS and Linux.

     source env/bin/activate

   OPTIONAL (For pyenv users)

   Run the application with pyenv (Refer thisarticle)

   • Build dependencies (on ubuntu)

     sudo apt-get install -y make build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev xz-utils tk-dev libffi-dev liblzma-dev python openssl

     sudo apt-get install build-essential zlib1g-dev libffi-dev libssl-dev libbz2-dev libreadline-dev libsqlite3-dev liblzma-dev libncurses-devsudo apt-get install python-tk python3-tk tk-devsudo apt-get install build-essential zlib1g-dev libffi-dev libssl-dev libbz2-dev libreadline-dev libsqlite3-dev liblzma-dev

   • pyenv installer

        curl https://pyenv.run | bash

   • Install desired python version

       pyenv install -v 3.11.0

   • pyenv with virtual environment

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

7. Parse Resumes to JSON:

   pythonrun_first.py

8. Run the Application:

   streamlitrunstreamlit_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.

1. Build the image and start application

       docker-compose up

2. Openlocalhost:80 on your browser

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

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

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. 

This project usesBlack for code formatting. We believe this helps to keep the code base consistent and reduces the cognitive load when reading code.

Before submitting your pull request, please make sure your changes are in accordance with the Black style guide. You can format your code by running the following command in your terminal:

black.

We also usepre-commit to automatically check for common issues before commits are submitted. This includes checks for code formatting with Black.

If you haven't already, please install the pre-commit hooks by running the following command in your terminal:

pip install pre-commitpre-commit install

Now, the pre-commit hooks will automatically run every time you commit your changes. If any of the hooks fail, the commit will be aborted.

Pull Requests & Issues are not just welcomed, they're celebrated! Let's create together.

🎉 Join our livelyDiscord community and discuss away!

💡 Spot a problem? Create an issue!

👩‍💻 Dive in and help resolve existingissues.

🔔 Share your thoughts in ourDiscussions & Announcements.

🚀 Explore and improve ourLanding Page. PRs always welcome!

📚 Contribute to theResume Matcher Docs and help people get started with using the software.

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! 🚀