APNA - AI-Powered Job Platform

A comprehensive job matching platform built with Django and React that uses AI-powered resume analysis and intelligent job matching to connect candidates with recruiters.

Live Demo

🔗 View Live Demo

Experience the full functionality of APNA job platform with our live deployment on Vercel.

Features

For Candidates

• Secure Authentication - Registration, login, and password reset with JWT tokens
• AI-Powered Resume Analysis - Upload resumes for automatic skill extraction using Google Gemini AI
• Intelligent Profile Verification - AI-generated scenario-based questions for profile verification
• Smart Job Matching - Get personalized job recommendations based on extracted skills
• Interactive Job Applications - Take custom tests created by HR teams
• Application Tracking - Monitor application status and view applied jobs
• Responsive Dashboard - Mobile-friendly interface for job management

For HR/Recruiters

• Comprehensive Job Posting - Create detailed job listings with custom requirements
• Custom Assessment Creation - Design scenario-based questions for candidate evaluation
• Candidate Management - View and evaluate applicant profiles and test results
• Advanced Filtering - Search and filter candidates based on various criteria
• Application Analytics - Track application metrics and candidate performance
• Resume Viewer - Access detailed candidate profiles and uploaded resumes

AI-Powered Features

• Resume Text Extraction - Support for PDF resumes with OCR fallback using Tesseract
• Skill Identification - Automatic extraction of technical and soft skills
• Experience Analysis - Work history and project identification
• Question Generation - AI-generated interview questions based on candidate profile
• Intelligent Matching - Algorithm-based job-candidate matching system

Architecture & Technology Stack

Backend

• Framework: Django 5.1.6 with Django REST Framework
• Database: MongoDB with PyMongo
• Authentication: JWT (PyJWT) with bcrypt encryption
• AI Integration: Google Generative AI (Gemini)
• File Processing: PyMuPDF for PDF parsing, Tesseract OCR for text extraction
• Image Processing: Pillow for image manipulation
• CORS: django-cors-headers for cross-origin requests

Frontend

• Framework: React 19.0.0 with Create React App
• Routing: React Router DOM 7.1.5
• Styling: TailwindCSS 3.2.4 with responsive design
• State Management: React Hooks (useState, useEffect)
• HTTP Client: Fetch API with cookie-based authentication
• UI Components: React Icons, React Toastify for notifications
• Cookie Management: js-cookie for token storage

Development Tools

• Package Managers: npm (Frontend), pip (Backend)
• Bundling: Webpack (via Create React App)
• PostCSS: Autoprefixer and TailwindCSS processing
• Build Tools: Django Extensions, Gunicorn for production

Project Structure

APNA/
├── Backend/                    # Django Backend Application
│   ├── manage.py              # Django management script
│   ├── requirements.txt       # Python dependencies
│   ├── db.sqlite3            # SQLite database (development)
│   ├── backend/              # Main Django project configuration
│   │   ├── settings.py       # Django settings and CORS configuration
│   │   ├── urls.py           # Main URL routing
│   │   ├── wsgi.py           # WSGI configuration
│   │   └── asgi.py           # ASGI configuration
│   ├── apna/                 # Main Django application
│   │   ├── models.py         # Database models (using MongoDB)
│   │   ├── candidate_views.py # Candidate-related API endpoints
│   │   ├── hr_views.py       # HR-related API endpoints
│   │   ├── urls.py           # Application URL patterns
│   │   ├── admin.py          # Django admin configuration
│   │   └── migrations/       # Database migrations
│   └── uploads/              # Resume file storage
│       ├── user@email.com/   # User-specific upload directories
│       └── ...
├── Frontend/                  # React Frontend Application
│   ├── package.json          # NPM dependencies and scripts
│   ├── tailwind.config.js    # TailwindCSS configuration
│   ├── postcss.config.js     # PostCSS configuration
│   ├── public/               # Static assets
│   │   ├── index.html        # Main HTML template
│   │   ├── favicon.ico       # Application icon
│   │   └── manifest.json     # PWA manifest
│   └── src/                  # React source code
│       ├── App.js            # Main application component with routing
│       ├── index.js          # React entry point
│       ├── components/       # Reusable UI components
│       │   ├── HrNavbar.js           # HR navigation component
│       │   ├── ApplicantNavbar.js    # Candidate navigation component
│       │   └── PasswordStrengthBar.js # Password validation component
│       └── pages/            # Page components
│           ├── Landing.js            # Homepage
│           ├── Candidate/            # Candidate-specific pages
│           │   ├── CandidateLogin.js
│           │   ├── CandidateRegister.js
│           │   ├── CandidateDashboard.js
│           │   ├── CandidateProfile.js
│           │   ├── CandidateResumeUpload.js
│           │   ├── Jobs.js
│           │   ├── JobsForYou.js
│           │   └── JobPreview.js
│           └── HR/                   # HR-specific pages
│               ├── Login.js
│               ├── Register.js
│               ├── HrDashboard.js
│               ├── HRProfile.js
│               ├── PostJob.js
│               └── CandidateDetails.js
└── README.md                 # Project documentation

Installation & Setup

Prerequisites

• Python 3.8+ with pip
• Node.js 14+ with npm
• MongoDB Atlas account or local MongoDB installation
• Google AI API key (for Gemini integration)
• Tesseract OCR (for resume text extraction)

Backend Setup

1. Clone the repository

   git clone https://github.com/Hariharanpugazh/APNA.git
   cd APNA/Backend

2. Create virtual environment

   python -m venv venv
   # Windows
   venv\Scripts\activate
   # Linux/Mac
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Install Tesseract OCR

   • Windows: Download from GitHub Tesseract
   • Linux: sudo apt-get install tesseract-ocr
   • Mac: brew install tesseract

5. Configure environment variables Update backend/settings.py with your MongoDB connection string and Google AI API key in candidate_views.py

6. Run the development server

   python manage.py runserver

   The backend will be available at http://localhost:8000

Frontend Setup

1. Navigate to frontend directory

   cd ../Frontend

2. Install dependencies

   npm install

3. Start the development server

   npm start

   The frontend will be available at http://localhost:3000

Configuration

Database Configuration

The application uses MongoDB for data storage. Update the connection string in both view files:

client = MongoClient('mongodb+srv://username:password@cluster.mongodb.net/')

AI Integration Setup

Configure Google Gemini AI in candidate_views.py:

genai.configure(api_key="your-google-ai-api-key")

CORS Configuration

The backend is configured to allow requests from the React frontend. Update CORS_TRUSTED_ORIGINS in settings.py if needed.

Database Schema

Collections in MongoDB

candidate Collection

{
  _id: ObjectId,
  name: String,
  email: String,
  password: String (hashed),
  otp: Number,
  otp_expiry: DateTime,
  resume_skills: [String],
  work_experience: [String],
  projects: [String],
  generated_questions: [{
    question: String,
    keyword: String/Array
  }],
  profile_status: String // "Pending" | "Verified"
}

hr Collection

{
  _id: ObjectId,
  name: String,
  email: String,
  password: String (hashed),
  otp: Number,
  otp_expiry: DateTime
}

job_details Collection

{
  _id: ObjectId,
  hr_id: ObjectId,
  job_title: String,
  company_name: String,
  job_description: String,
  contact_email: String,
  application_deadline: String,
  work_type: String,
  job_location: String,
  category: String,
  skills_required: String/Array,
  salary: Number,
  experience: Number,
  pass_percentage: Number,
  hr_questions: [{
    question: String,
    keyword: String/Array
  }],
  selected_candidates: [Object],
  applied_candidates: [Object]
}

API Endpoints

Authentication Endpoints

Candidate Authentication

• POST /apna/register/ - Candidate registration
• POST /apna/login/ - Candidate login
• POST /apna/forgot-password/ - Password reset request
• POST /apna/reset-password/ - Password reset confirmation

HR Authentication

• POST /apna/hr_register/ - HR registration
• POST /apna/hr_login/ - HR login
• POST /apna/hr_forgot-password/ - HR password reset request
• POST /apna/hr_reset-password/ - HR password reset confirmation

Candidate Endpoints

• GET /apna/candidate_profile/ - Get candidate profile
• GET /apna/get_all_jobs/ - Fetch all available jobs
• GET /apna/get_job_details/<job_id>/ - Get specific job details
• POST /apna/apply_job/ - Apply for a job with test submission
• GET /apna/check_application_status/<job_id>/ - Check application status
• GET /apna/get_applied_jobs/ - Get candidate's applied jobs
• POST /apna/upload_resume/ - Upload and analyze resume
• POST /apna/candidate_test/ - Submit profile verification test
• GET /apna/get_matched_jobs/ - Get AI-matched job recommendations

HR Endpoints

• GET /apna/get_hr_profile/ - Get HR profile
• POST /apna/post_job/ - Create new job posting
• GET /apna/get_jobs/ - Get HR's posted jobs
• GET /apna/get_selected_candidates/<job_id>/ - Get candidates for a job
• GET /apna/candidate/<email> - Get specific candidate details
• DELETE /apna/delete_job/<job_id>/ - Delete job posting

Testing

Running Backend Tests

cd Backend
python manage.py test

Running Frontend Tests

cd Frontend
npm test

Deployment

Backend Deployment (Using Gunicorn)

pip install gunicorn
gunicorn backend.wsgi:application --bind 0.0.0.0:8000

Frontend Deployment

npm run build
# Serve the build folder using a web server

Environment Variables for Production

• Set DEBUG = False in Django settings
• Configure proper ALLOWED_HOSTS
• Use environment variables for sensitive data
• Set up proper CORS origins
• Configure MongoDB with authentication

Security Features

• Password Hashing: bcrypt encryption for all passwords
• JWT Authentication: Secure token-based authentication
• CORS Protection: Configured cross-origin request handling
• Input Validation: Server-side validation for all inputs
• File Upload Security: Restricted file types and size limits
• SQL Injection Prevention: MongoDB parameterized queries

Contributing

1. Fork the repository
2. Create a feature 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

Team

• Developer: Hariharan Pugazh , Kavin-Bakyaraj
• Repository: GitHub - APNA

Known Issues

• Resume OCR processing may require Tesseract path configuration on Windows
• Large PDF files may take longer to process
• MongoDB connection needs proper network access configuration

Future Enhancements

• Real-time chat between candidates and HR
• Video interview integration
• Advanced analytics dashboard
• Mobile application development
• Integration with LinkedIn and other job platforms
• Machine learning model improvements
• Multi-language support
• Email notification system
• Calendar integration for interviews

Support

For support and questions, please create an issue in the GitHub repository or contact the development team.

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Built with ♥ using Django, React, and AI technologies