SmartPrep

SmartPrep is an AI-powered virtual teacher platform designed to revolutionize education. It converts standard textbooks into interactive daily lessons, providing a personalized learning experience through AI-generated content, quizzes, and a virtual assistant along with IOE/IOM entrance preparation.

Features

• AI Content Generation: Automatically converts textbook PDFs into structured lessons and summaries.
• Interactive Quizzes (MCQ): Generates multiple-choice questions to test understanding, complete with distractors.
• AI Chatbot: A virtual assistant to answer student queries in real-time.
• Performance Tracking: Visualizes student progress and performance over time.
• Note Summarizer: Summarizes comprehensive notes for quick revision.
• Mock Tests: Simulates exam conditions for better preparation for IOE/IOM entrance.
• Community Features: Connects students for collaborative learning.

Tech Stack

Frontend

• Framework: Next.js (React)
• Styling: Tailwind CSS

Backend

• Framework: FastAPI (Python)
• Database and Storage: MongoDB and Cloudinary
• AI/ML: LangChain, OpenAI

Getting Started

Prerequisites

• Docker and Docker Compose
• Node.js (v18+)
• Python (v3.9+)
• MongoDB (if running locally without Docker)

Installation

1. Clone the repository

   git clone https://github.com/yourusername/SmartEd.git
   cd SmartEd

2. Environment Setup Create a .env file in the server directory and a .env file in the client directory.

Docker Setup (Recommended)

The easiest way to run the application is using Docker Compose.

1. Build and Run

   cd server
   docker-compose up --build

   This will start the backend server and MongoDB database.

2. Run Frontend Open a new terminal configuration for the client:

   cd client
   npm install
   npm run dev

Manual Setup

Backend (Server)

1. Navigate to the server directory:

   cd server

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Run the server:

   uvicorn main:app --reload

   The API will be available at http://localhost:8000. API Documentation (Swagger UI): http://localhost:8000/docs

Frontend (Client)

1. Navigate to the client directory:

   cd client

2. Install dependencies:

   npm install

3. Run the development server:

   npm run dev

   The application will be available at http://localhost:3000.

Environment Variables

Server (server/.env)

Required environment variables for the backend:

MONGO_URI=mongodb://localhost:27017  # or mongodb://mongodb:27017 for Docker
DB_NAME=auth_db
JWT_SECRET=your_jwt_secret
JWT_ALGORITHM=HS256
ENV=development

# AI Services
GOOGLE_API_KEY=your_google_api_key
GROQ_API_KEY=your_groq_api_key

# Cloudinary (Image Uploads)
CLOUDINARY_CLOUD_NAME=your_cloud_name
CLOUDINARY_API_KEY=your_api_key
CLOUDINARY_API_SECRET=your_api_secret

# Azure OpenAI (Optional/If used)
AZURE_OPENAI_API_KEY=your_azure_key
AZURE_OPENAI_ENDPOINT=your_azure_endpoint
AZURE_OPENAI_DEPLOYMENT=your_deployment_name
AZURE_OPENAI_API_VERSION=your_api_version

# Google Search (Optional)
GOOGLE_SEARCH_API_KEY=your_search_key
GOOGLE_SEARCH_ENGINE_ID=your_search_engine_id

Client (client/.env)

Required environment variables for the frontend:

NEXT_PUBLIC_API_URL=http://localhost:8000/api

Contributing

1. Fork the project
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request