RecruitSheriff – AI Resume Analyzer

RecruitSheriff is a full‑stack resume analysis tool that helps candidates and recruiters quickly evaluate how well a resume matches a given job description. It provides an ATS‑style match score and generates AI‑powered interview questions based on the resume and role.

Features

• Resume upload with job description input (web UI).
• ATS‑like match score between resume and job description.
• AI‑generated interview questions tailored to the candidate’s profile.
• Privacy‑friendly: processing happens on your machine; no data is stored in a database.
• Built with a modern stack: Flutter for the frontend and Node.js for the backend.

Tech Stack

• Frontend: Flutter (Web)
• Backend: Node.js
• Language Model API: Groq (LLM API)
• Other: REST API communication between Flutter and Node.js

Project Structure

RecruitSheriff/
├── lib/ # Flutter UI and app logic
├── web/ # Flutter web entry & assets
├── android/ios/... # Mobile platform scaffolding (generated by Flutter)
├── server/ # Node.js backend (Express-style HTTP server)
│ ├── index.js # Main server entry point
│ └── package.json # Backend dependencies and scripts
└── README.md # Project documentation (this file)

Getting Started

Prerequisites

• Flutter SDK installed and configured for web.
• Node.js and npm installed (recent LTS or current version).
• A Groq API key (or your own LLM provider key) configured in the backend.

1. Clone the repository

git clone https://github.com/Swaraj-Mandre/RecruitSheriff.git cd RecruitSheriff

2. Setup and run the backend

cd server npm install node index.js

The backend will start on port 8080 by default (configurable in index.js).

3. Run the Flutter web frontend

In a second terminal from the project root:

flutter pub get flutter run -d chrome

Flutter will build the web app and open it in Chrome on a http://localhost:<port> URL.

Usage

1. Open the Flutter web app in your browser.
2. Upload your resume (PDF) and paste a job description.
3. Click Analyze Resume.
4. View:
   • The match score (0–100) estimating how well the resume fits the role.
   • A list of AI‑generated interview questions you can use for preparation or screening.

Configuration

• Backend environment variables (example):
  • GROQ_API_KEY – API key for the Groq LLM service.
• Ports and other settings can be adjusted in server/index.js.

Contributing

Contributions, issues, and feature requests are welcome.

Typical workflow:

1. Fork the repository.
2. Create a feature branch: git checkout -b feature/my-improvement
3. Commit your changes and push the branch.
4. Open a Pull Request to the main branch.

Please keep PRs focused and add a short description of the change and testing steps.

Roadmap / Ideas

• More detailed ATS breakdown (skills, keywords, experience match).
• Support for multiple resumes comparison.
• Exportable reports (PDF/HTML).
• Basic auth or admin dashboard for multi‑user use.

License

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