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JobTracker

AI powered tool to trace job application status based on emails.

Feature

  • Easy to customize and use, you can adjust the confidence interval threshold yourself
  • Up to 96% accuracy with 0 false positives

Getting Started

Video Tutorial (Click to play at YouTube)

How to use JobTracker

Prerequisites

  • Python 3.10
  • An OpenAI API key for ChatGPT access

Installation

  1. Clone the repository and install the required dependencies:

    git clone https://github.com/resumejob/JobTracker.git
cd JobTracker
# It is recommended to create a virtual environment for this project before install
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

  2. Configure your OpenAI API key: Set up your API Key

Usage

Export your emails in MBOX format and run the script with the following command:

python3 main.py -p <path_to_your_email_file.mbox> -o <output_filename.csv>

Replace <path_to_your_email_file.mbox> with the path to your MBOX file and <output_filename.csv> with your desired output file name.

Example Output

You would get

example_output

How It Works

  1. Access Data

    • Users export their emails in MBOX format from their email service provider.

  2. Clean Up and Collect Data

    • The system extracts basic information such as the sender, recipient, and body of the message.
    • It uses predefined keywords from config.KEYWORD to determine if an email is related to a job application.
    • If an email is related, the system collects the relevant information and forwards it to the ChatBot for analysis.

  3. Understand and Format Data

    • The ChatBot assesses whether the email pertains to a job application.
    • For job-related emails, it identifies the current status of the application and suggests subsequent steps.
    • A function call is utilized to neatly format the data.

  4. Export Data

    • The processed data is exported in CSV or Excel format for easy access and use by the job seeker.

Run Tests

Execute the following command to run tests:

python3 -m unittest discover -s tests

Roadmap

  • Distributing to PyPi
  • Support Local LLMs like Llama

Contributing

We welcome contributions from the community. If you would like to contribute, please fork the repository and submit a pull request.

Garen

  • Leveraged the ChatGPT/llama to process the email and a cost pipeline to estimate the price.
  • Merge email with same company and provide a clear view of the application timeline.
  • Increase model accuracy by adding a pre-process threashold.
  • Increase model speed by adding Hash to avoid the duplicate email.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Contact

For any queries, you can reach out to Project Maintainer.