TalentMatch - AI-Powered Recruitment Platform
TalentMatch is a modern, AI-powered recruitment platform that intelligently matches candidates to job descriptions using semantic similarity and machine learning.
🚀 Features Core Functionality AI Job Analysis - Parse and extract key skills from job descriptions
Smart Candidate Matching - Semantic similarity matching using vector embeddings
Candidate Management - Upload and store candidate profiles
Interview Question Generation - AI-powered, context-aware interview questions
Real-time Ranking - Live candidate ranking with similarity scores
Technical Features Full-Stack Application - React frontend with Flask backend
Vector Similarity Search - Advanced matching using embeddings
Responsive Design - Mobile-friendly Tailwind CSS interface
RESTful API - Clean API architecture for extensibility
🛠 Tech Stack Frontend React 18 - Modern React with hooks
React Router - Client-side routing
Tailwind CSS - Utility-first CSS framework
Framer Motion - Smooth animations
Vite - Fast build tool and dev server
Backend Flask - Python web framework
Pinecone - Vector database for similarity search
OpenAI Embeddings - Text embedding generation
Python-dotenv - Environment configuration
📦 Installation Prerequisites Node.js 16+ and npm
Python 3.8+
Pinecone account (optional - fallback to in-memory store)
Backend Setup Navigate to backend directory
bash cd talentmatch/backend Install Python dependencies
bash pip install -r requirements.txt Set up environment variables
bash cp .env.example .env Edit .env with your credentials:
env PINECONE_API_KEY=your_pinecone_api_key PINECONE_ENVIRONMENT=your_environment OPENAI_API_KEY=your_openai_api_key DEBUG=True Start the Flask server
bash python -m backend.app Server runs on http://localhost:5000
Frontend Setup Navigate to project root
bash cd talentmatch Install dependencies
bash npm install Start development server
bash npm run dev Application runs on http://localhost:5173
🎯 Usage Basic Workflow Access the Application
Open http://localhost:5173 in your browser
Click "Try it now" to access the dashboard
Analyze Job Description
Paste a job description in the text area
Click "Analyze" to extract skills and requirements
Upload Candidates
Drag & drop resume files or paste candidate names
System automatically processes and stores candidates
View Matches
See real-time candidate rankings with similarity scores
Scores range from 0-100% based on semantic match
Generate Questions
Click "Generate" to create interview questions
Questions are tailored to job requirements and candidate skills
API Endpoints Jobs POST /jobs/analyze - Analyze job description
GET /jobs/ - Get jobs endpoint info
Candidates POST /candidates/upload - Upload multiple candidates
POST /candidates/add - Add single candidate
GET /candidates/debug - Debug stored candidates
Questions POST /questions/generate - Generate interview questions
POST /questions/submit - Submit and evaluate answers
env PINECONE_API_KEY=your_pinecone_key PINECONE_ENVIRONMENT=us-east-1 OPENAI_API_KEY=your_openai_key DEBUG=True Frontend (vite.config.js)
javascript export default defineConfig({ plugins: [react()], server: { port: 5173, proxy: { '/api': 'http://localhost:5000' } } }) 🚀 Deployment Development bash
cd backend && python -m backend.app
cd talentmatch && npm run dev Production Build bash
npm run build
🤝 Contributing Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request
📝 License This project is licensed under the MIT License - see the LICENSE file for details.
🆘 Support For support and questions:
Check the Issues page
Create a new issue with detailed description
🏆 Acknowledgments OpenAI for embedding models
Pinecone for vector database infrastructure
React and Flask communities
Tailwind CSS for styling system
Report Bug · Request Feature