JobMatch AI 🔍✨

An intelligent job matching system that analyzes resumes and matches them with the most suitable job postings using AI.

Features

• 📄 Resume Analysis: Extract and analyze text from PDF resumes
• 🤖 AI-Powered Matching: Uses Google's Gemini AI to match resumes with job descriptions
• 🎯 Smart Scoring: Provides a suitability score (0-100) for each job match
• 💡 Actionable Insights: Get detailed reasons for matches and preparation tips
• ⚡ Fast Processing: Optimized for quick analysis of multiple job postings

Prerequisites

• Python 3.8+
• Google AI API key
• Apify API key (for job scraping, if needed)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/jobmatch-ai.git
   cd jobmatch-ai
   

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

3. Install the required packages:

   $pip install -r requirements.txt
   

4. Create a .env file and add your API keys:

   GOOGLE_AI_API=your_google_ai_api_key
   APIFY_API_KEY=your_apify_api_key
   

Usage

1. Start the Flask server:

   $python app.py
   

2. Send a POST request to /extract with a PDF resume:

   $curl -X POST -F "file=@/path/to/your/resume.pdf" http://localhost:3000/extract
   

API Endpoints

POST /extract

Upload a PDF resume and get job matches.

Request:

• Method: POST
• Content-Type: multipart/form-data
• Body: file (PDF file)

Response:

[
   {
    "job_title": "Frontend Software Engineer (React, TypeScript or JavaScript)",
    "score": 60,
    "match_reason": "The candidate has strong proficiency in React, JavaScript, and TypeScript, essential for this front-end role. Critically, their robust Python programming skills and hands-on experience in machine learning and AI (TensorFlow, PyTorch, Generative AI, Nillion AI Prize) make them an excellent match for supporting AI labs and developing coding benchmarks, which is the core focus. While the stated 3-10 years of experience is a hurdle, the depth of technical skills and relevance of AI projects are highly compelling, especially for a contract/part-time role that may value specific technical expertise.",
    "prepare": "Emphasize how their AI/ML knowledge allows them to understand and contribute to coding benchmarks for AI systems. Highlight any personal experience with code quality, testing (e.g., unit tests in projects), and debugging. Be ready to discuss the technical aspects of their AI projects and how they would approach curating issues and solutions for AI-related coding tasks."
  },
]

Project Structure

.
├── app.py              # Main Flask application
├── data.json           # Sample job postings
├── requirements.txt    # Python dependencies
├── .env                # Environment variables
└── README.md           # This file

Contributing

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

License

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