JobSeekerTG Bot

JobSeekerTG is a Telegram bot that scrapes job postings from platforms like LinkedIn, Indeed, Glassdoor, and others (currently under development). It gathers job data based on title and location, reformats it into a structured format, and saves it to a MongoDB database. New job posts are automatically sent to a designated Telegram bot chat.

This project is based on the JobSpy project. Credits to the original creator.

Features

• Job scraping: Collects job postings from multiple job platforms.
• Structured data: Reformats job data into a structured format for easy processing and storage.
• Database storage: Saves job data into a MongoDB database.
• Telegram integration: Sends new job postings directly to a Telegram bot chat.

Data Structure

The scraped job postings are stored in the following format:

JobPost
├── title
├── company
├── company_url
├── job_url
├── location
│   ├── country
│   ├── city
│   ├── state
├── description
├── job_type: fulltime, parttime, internship, contract
├── job_function
│   ├── interval: yearly, monthly, weekly, daily, hourly
│   ├── min_amount
│   ├── max_amount
│   ├── currency
│   └── salary_source: direct_data, description (parsed from posting)
├── date_posted
├── emails
└── is_remote

Prerequisites

• Python 3.8+
• MongoDB
• Telegram bot token (create a bot via BotFather)

Installation

1. Clone the repository:

   git clone https://github.com/yariv245/JobSeeker.git
   cd JobSeeker
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Set up environment variables: Create a .env file in the root directory with the following:

   TELEGRAM_BOT_TOKEN=your_telegram_bot_token
   MONGO_URI=your_mongodb_connection_string
   

4. Run the bot:

   python bot.py
   

Usage

• Add the bot to a Telegram group or chat.
• Start the bot to receive job postings as they are scraped.

Testing

This project includes testing to ensure data scraping, formatting, and Telegram integration work as expected. Run the tests using:

pytest

Ensure you have the necessary environment variables and mock data set up before running the tests.

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/your-feature-name).
3. Commit your changes (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature/your-feature-name).
5. Open a pull request.

Acknowledgments

• JobSpy for inspiring this project.