Study Buddy

A full-stack student productivity and academic management platform. Study Buddy combines a React web dashboard, a React Native mobile app (Expo), a Node.js REST API, and two Flask ML microservices to give students personalized study recommendations, attendance tracking, document management, and academic performance insights.

Features

• Authentication — JWT-based registration and login with bcrypt password hashing
• Student Dashboard — attendance overview, calendar, performance charts (ApexCharts)
• Document Management — upload and browse study documents (Multer)
• Study Recommendations — ML-powered suggestions based on student performance
• Emotion/NLP Feedback — sentiment analysis on student feedback text
• Email Notifications — nodemailer + SendGrid for transactional emails
• Mobile App — React Native (Expo) with screens for Login, Home, Recommendations, Documents, Attendance, Profile, and Entry Records
• ML Microservices — two Flask APIs: student performance predictor and text emotion/spam classifier

Tech Stack

Layer	Technology
Web Frontend	React 17, Chakra UI, Framer Motion, ApexCharts, Axios
Mobile	React Native, Expo, react-navigation
Backend API	Node.js, Express, Mongoose, JWT, bcryptjs, Multer, Nodemailer
Database	MongoDB
ML Services	Flask, Scikit-learn, Pandas (2 microservices)
Testing	Jest, Supertest, mongodb-memory-server

Architecture

Study-Buddy/
├── frontend/       React web dashboard (Chakra UI)
├── backend/        Node.js / Express REST API (MongoDB)
├── mobile app/     React Native (Expo) mobile client
└── ml/
    ├── Student-Flask/      Student performance prediction API
    └── Text-Emotion-NLP/   Feedback sentiment / spam classifier API

Getting Started

Backend (Node.js API)

cd backend
npm install
# Create a .env file with MONGO_URI, JWT_SECRET, SENDGRID_API_KEY
npm run dev         # starts with nodemon on port 5000

Frontend (React)

cd frontend
npm install
npm start           # opens at http://localhost:3000

Mobile App (Expo)

cd "mobile app"
npm install
npx expo start      # scan QR code with Expo Go app

ML Services (Flask)

# Student performance predictor
cd ml/Student-Flask
pip install flask scikit-learn pandas numpy
python app.py       # runs on http://localhost:5000

# Text emotion / NLP
cd ml/Text-Emotion-NLP
python app.py       # runs on http://localhost:5001

API Endpoints (Backend)

Method	Endpoint	Description
POST	/api/auth/register	Register a new student
POST	/api/auth/login	Login and receive JWT
GET	/api/students/	Get student profile
POST	/api/documents/upload	Upload study document
GET	/api/attendance/	Fetch attendance records

ML API Endpoints

Service	Endpoint	Input	Output
Student-Flask	POST /predict	Student metrics	Performance prediction
Text-Emotion-NLP	POST /predict	Feedback text	Emotion / spam label

License

MIT License — see LICENSE for details.