🤖 AI Resume Screener

Intelligent resume screening powered by keyword & skill matching

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📸 Overview

AI Resume Screener is a full-stack web application that helps recruiters efficiently screen and rank candidates by automatically analyzing resumes against job descriptions. Resumes are scored using a multi-factor AI matching algorithm, and candidates are notified via automated email at each stage of the process.

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✨ Features

Feature	Description
🧠 AI Resume Matching	Multi-factor scoring: skills (50pts), keywords (30pts), depth (10pts), experience (±10pts)
📤 Bulk Resume Upload	Upload multiple PDFs at once with real-time progress tracking
📧 Email Notifications	Automated HTML emails on submission, approval, and rejection via Brevo SMTP
🔒 JWT Authentication	Secure recruiter login with BCrypt passwords and Bearer token authorization
📄 Resume Text Preview	In-app scrollable preview dialog with download option
📋 Pagination & Filtering	10 results/page with ellipsis navigation, resets on filter change

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🛠️ Tech Stack

Frontend

• Framework: Next.js (React 19)
• Styling: Tailwind CSS
• HTTP Client: Axios
• Deployment: Vercel

Backend

• Framework: Spring Boot 3.5 (Java 21)
• Database: MongoDB Atlas
• PDF Parsing: Apache PDFBox
• Email: Brevo SMTP
• Security: Spring Security + JWT
• Deployment: Railway

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🏗️ Architecture

┌─────────────────────┐         ┌──────────────────────┐         ┌─────────────────┐
│   Next.js Frontend  │  HTTP   │  Spring Boot Backend  │  ODM    │  MongoDB Atlas  │
│      (Vercel)       │ ──────► │      (Railway)        │ ──────► │  resume_matcher │
└─────────────────────┘         └──────────────────────┘         └─────────────────┘
                                          │
                                          │ SMTP
                                          ▼
                                 ┌────────────────┐
                                 │  Brevo Email   │
                                 │    Service     │
                                 └────────────────┘

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🚀 Getting Started

Prerequisites

• Java 17+
• Node.js 18+
• MongoDB (local or Atlas)
• Maven

1. Clone the repository

git clone https://github.com/FaithEX1331/AI-Resume-Screening-App.git
cd AI-Resume-Screening-App

2. Backend Setup

cd backend

Create a .env or set the following environment variables:

MONGODB_URI=mongodb://localhost:27017/resume_matcher
MONGODB_DATABASE=resume_matcher
JWT_SECRET=your_jwt_secret_here
JWT_EXPIRATION_MS=86400000
MAIL_API_KEY=your_brevo_smtp_key
CORS_ORIGIN=http://localhost:3000
UPLOAD_DIR=uploads
RECRUITER_NAME=HR Team

Run the backend:

./mvnw spring-boot:run

3. Frontend Setup

cd ai-resume-screener

Create a .env.local file:

NEXT_PUBLIC_API_URL=http://localhost:8080/api

Run the frontend:

npm install
npm run dev

The app will be available at http://localhost:3000.

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📁 Project Structure

AI-Resume-Screening-App/
├── ai-resume-screener/          # Next.js Frontend
│   └── src/
│       ├── pages/
│       │   ├── ApplicantPage.jsx
│       │   └── DashboardPage.jsx
│       └── services/
│           └── apiClient.js
│
└── backend/                     # Spring Boot Backend
    └── src/main/java/com/ai/screener/backend/
        ├── service/
        │   ├── MatchingService.java
        │   ├── EmailService.java
        │   └── AuthService.java
        ├── security/
        │   ├── JwtUtil.java
        │   ├── JwtFilter.java
        │   └── SecurityConfig.java
        └── resources/
            └── application.yaml

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🌐 Deployment

Service	Platform	URL
Frontend	Vercel	https://ai-resume-screening-app-orpin.vercel.app
Backend	Railway	https://ai-resume-screening-app-production.up.railway.app
Database	MongoDB Atlas	Cluster: resume-screener

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📬 API Endpoints

Method	Endpoint	Description
POST	/api/auth/login	Recruiter login
POST	/api/resumes/upload	Upload single resume
POST	/api/resumes/bulk-upload	Upload multiple resumes
GET	/api/resumes	Get all resumes (paginated)
PUT	/api/resumes/:id/status	Update resume status
DELETE	/api/resumes/:id	Delete a resume

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🤝 Contributing

Contributions are welcome! Please open an issue first to discuss what you'd like to change.

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

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📄 License

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

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Made with ❤️ by FaithEX1331