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

Backend

cd backend && .

Frontend

cd talentmatch && npm run dev Production Build bash

Build frontend

npm run build

Serve with nginx or similar

🤝 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

Built with ❤️ for better hiring experiences

Report Bug · Request Feature

📋 Project Overview

🏗️ TECH STACK

Project: talentmatch
Frontend: React
  Build: Vite
  Language: TypeScript
  CSS: Tailwind
  Orchestration: docker-compose

📁 PROJECT STRUCTURE

.
./.vscode
./backend
./backend/config
./backend/database
./backend/models
./backend/routes
./backend/src
./backend/tests
./backend/utils
./backups
./backups/pre-typescript-migration
./config
./config/environments
./config/production

🔑 KEY FILES

backend/app.py
dist/index.html
index.html
src/App.tsx
src/index.css
src/main.jsx
src/main.tsx
venv/Lib/site-packages/dotenv/main.py
venv/Lib/site-packages/flask/app.py
venv/Lib/site-packages/flask/sansio/app.py

📦 DEPENDENCIES

Frontend Dependencies:
@auth0/auth0-react
 framer-motion
 react
 react-dom
 react-router-dom
 

🔍 DETECTED FEATURES

✅ Auth: backend/production_auth.py
✅ Auth: backend/routes/auth.py
✅ Auth: config/production/oauth_config.py
✅ API: backend/routes/api.py
✅ API: src/lib/api.ts
✅ API: src/types/api.ts
✅ docker-compose ready

📊 QUICK STATS

Files (source):
2243

🚀 GETTING STARTED

# Install dependencies
npm install

# Available scripts:
npm run build
npm run dev
npm run preview
npm run type-check
npm run type-check:watch

# Start with Docker
docker-compose up

📝 COMMON NEXT STEPS

1. Check .env.example or .env for configuration
2. Run npm install / pip install
3. Start development server
4. Check README.md for project-specific instructions