EduHub-AI

EduHub-AI is a platform designed to revolutionize education through the integration of artificial intelligence. It provides innovative features for both teachers and students, creating a seamless learning experience.

Features

• Automated Assignments: AI-generated assignments tailored to student performance.
• Interactive Learning: Talk to slides, ask questions, and receive real-time feedback.
• Live Classes: Conduct live streaming classes with integrated video streaming.
• AI Quiz Generation: Create quizzes with AI assistance, adapting difficulty based on performance.
• Analytics Dashboard: In-depth performance analytics for students and teachers.
• Secure and Collaborative: Join classes through authentication, collaborative group chat, and more.

How to Set Up

Client

Prerequisites

Make sure you have the following software installed on your machine:

• NodeJS

Clone the Repository

git clone https://github.com/superiorsd10/EduHub-AI.git
cd EduHub-AI

Install Dependencies

cd client
npm i

Set up .env File

1. Create a .env.local file in the root of the client folder.
2. Visit firebase.google.com, create a new project, and copy the credentials.
3. Paste the copied credentials into the .env.local file as follows:

NEXT_PUBLIC_FIREBASE_API_KEY="your_api_key"
NEXT_PUBLIC_FIREBASE_AUTH_DOMAIN="your_auth_domain"
NEXT_PUBLIC_FIREBASE_PROJECT_ID="your_project_id"
NEXT_PUBLIC_FIREBASE_STORAGE_BUCKET="your_storage_bucket"
NEXT_PUBLIC_FIREBASE_MESSAGING_SENDER_ID="your_messaging_sender_id"
NEXT_PUBLIC_FIREBASE_APP_ID="your_app_id"
NEXT_PUBLIC_FIREBASE_MEASUREMENT_ID="your_measurement_id"

Firebase Configuration

• Ensure that the SignInWithGoogle and SignInWithEmailAndPassword options are allowed in your Firebase project settings.

Run the frontend

npm run dev

Server

Prerequisites

Make sure you have the following software installed on your machine:

• Python (3.9 or higher)

Clone the Repository

git clone https://github.com/superiorsd10/EduHub-AI.git
cd EduHub-AI

Set Up Virtual Environment

Create and activate a virtual environment. Use the following commands based on your operating system:

Windows

cd server
python -m venv venv
.\venv\Scripts\activate

macOS/Linux

cd server
python -m venv venv
source venv/bin/activate

Install Dependencies

pip install -r requirements.txt

Setting Up Environment Variables

cp .env.example .env

Run the Server

python run.py

OR

Install Docker

Make sure you have Docker installed on your machine. You can download and install Docker from the official Docker website.

Navigate to Project Directory (Server)

Open your terminal or command prompt and navigate to the server directory of your project.

cd server

Setting Up Environment Variables

cp .env.example .env

Build Docker Image

Run the following command to build the Docker image for your application:

docker build -t my-flask-app .

Replace my-flask-app with the desired name for your Docker image.

Run Docker Container

Once the Docker image is built, you can run a Docker container using the following command:

docker run --env-file .env -p 5000:5000 my-flask-app

This command maps port 5000 of the Docker container to port 5000 of your host machine. Replace my-flask-app with the name of your Docker image.

Access the Server

After running the Docker container, you can access the server by navigating to http://localhost:5000 in your web browser/Postman/Thunder Client.

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Activating Pre-Commit Hooks

We use pre-commit to manage and run various hooks to ensure code consistency and quality. Follow these steps to activate the pre-commit hooks in your local environment:

1. Install pre-commit: Ensure you have pre-commit installed. You can install it using:

   pip install pre-commit

2. Set Up Pre-Commit: Run the following command to set up pre-commit:

   pre-commit install

Now, every time you commit changes (excluding client directory), pre-commit hooks will automatically run. If any issues are found, the hooks will prevent the commit and provide guidance on fixing them.

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Celery Guide

Celery is a distributed task queue framework for Python that allows you to run asynchronous tasks in the background. It's especially useful for executing long-running or resource-intensive tasks outside of the main application flow, improving application responsiveness and scalability.

Windows

pip install celery

MacOS

pip install celery

Once Celery is installed, you can start the Celery worker process by running the following command from your project directory i.e. server:

celery -A app.celery.celery worker --loglevel=INFO -E

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