Smart Digital Study Planner 📚

A production-ready, AI-powered full-stack web application for intelligent study planning, schedule generation, analytics, and exam preparation.

Design: Inspired by Google Gemini — dark theme, glassmorphism, gradient accents, Framer Motion animations.

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🏗️ Tech Stack

Layer	Technology
Frontend	React (Vite), TailwindCSS, Framer Motion, Chart.js, Axios
Backend	Node.js, Express, JWT, bcryptjs, better-sqlite3
AI Services	Python FastAPI, scikit-learn, pandas, numpy
AI Model	Groq API (LLaMA3-8B)
Database	SQLite (via better-sqlite3)

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📁 Project Structure

smart-study-planner/
├── frontend/                # React (Vite) app
│   ├── src/
│   │   ├── api/             # Axios API layer
│   │   ├── components/      # Sidebar, GlassCard
│   │   ├── pages/           # Login, Signup, Dashboard, Planner, Analytics, Assistant
│   │   └── styles/          # Global CSS
│   └── .env
├── backend/                 # Express API server
│   ├── controllers/         # authController, plannerController, analyticsController
│   ├── routes/              # auth, planner, analytics
│   ├── middleware/          # JWT auth, error handler
│   ├── db/                  # SQLite connection
│   ├── seed.js              # Sample data
│   └── .env
├── ai-services/             # FastAPI AI service
│   ├── modules/
│   │   ├── scheduler.py     # Schedule generation
│   │   ├── backlog.py       # Backlog recovery
│   │   ├── preparedness.py  # ML readiness score
│   │   └── chat.py          # Groq/LLaMA3 chat
│   ├── main.py
│   ├── requirements.txt
│   └── .env
└── database/
    ├── schema.sql
    └── study_planner.db     # Auto-created on first run

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🚀 Quick Start

1. Backend (Express — Port 5000)

cd smart-study-planner/backend
npm install
node seed.js         # optional: populate sample data
node server.js

Demo credentials (after seed): demo@study.com / demo1234

2. AI Services (FastAPI — Port 8000)

cd smart-study-planner/ai-services

# Create and activate virtual environment (recommended)
python -m venv venv
venv\Scripts\activate        # Windows
# source venv/bin/activate   # macOS/Linux

pip install -r requirements.txt
cp .env.example .env         # Add your GROQ_API_KEY
uvicorn main:app --reload --port 8000

Get a free Groq API key at https://console.groq.com

3. Frontend (Vite — Port 5173)

cd smart-study-planner/frontend
npm install
npm run dev

Open: http://localhost:5173

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🌐 API Endpoints

Backend (http://localhost:5000)

Method	Path	Description
POST	/api/auth/signup	Register user
POST	/api/auth/login	Login + JWT
GET	/api/auth/me	Get current user
GET	/api/planner/subjects	List subjects
POST	/api/planner/subjects	Add subject
DELETE	/api/planner/subjects/:id	Delete subject
GET	/api/planner/tasks	All tasks
GET	/api/planner/tasks/today	Today's tasks
PATCH	/api/planner/tasks/:id/status	Update status
POST	/api/planner/logs	Add study log
POST	/api/planner/generate-schedule	AI schedule generation
POST	/api/planner/backlog-recovery	Recover from backlog
GET	/api/analytics/overview	Dashboard stats
GET	/api/analytics/study-hours	Study hours data
GET	/api/analytics/subject-progress	Per-subject progress
POST	/api/analytics/preparedness	ML readiness score
POST	/api/analytics/ai-chat	AI assistant chat

Python AI Service (http://localhost:8000)

Method	Path	Description
POST	/generate-schedule	AI schedule
POST	/backlog-recovery	Recovery plan
POST	/preparedness-score	ML readiness
POST	/ai-chat	LLaMA3 chat

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⚙️ Environment Variables

Backend .env

PORT=5000
JWT_SECRET=supersecretjwtkey2024studyplanner
PYTHON_API_URL=http://localhost:8000
NODE_ENV=development

AI Services .env

GROQ_API_KEY=your_groq_api_key_here

Frontend .env

VITE_API_URL=http://localhost:5000

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✨ Features

Feature	Status
JWT Authentication (Signup/Login)	✅
Dark Mode Glassmorphism UI	✅
AI Schedule Generation (Python)	✅
Backlog Recovery Mode	✅
Study Analytics (Charts)	✅
ML Preparedness Score	✅
AI Chat (Groq LLaMA3)	✅
Framer Motion Animations	✅
Protected Routes	✅
SQLite Database	✅
Sample Seed Data	✅

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📝 Notes

• The AI chat works without a Groq key using smart fallback responses
• The schedule generator auto-assigns topics, revision slots, mock tests, and buffer days
• The preparedness ML model uses a weighted scoring algorithm (scikit-learn ready to extend)
• All three services can run independently; backend gracefully handles Python service downtime

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Built with ❤️ — Smart Study Planner v1.0.0