🧠 EduGenie - AI-Powered Educational Assistant

EduGenie is a modular system designed to provide personalized academic support for students by integrating a React frontend, Node.js backend, LangChain-based Python scripts, and Google Gemini LLM.

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📁 Project Structure

├── BackEnd
├── FrontEnd
├── LangChain
└── PythonScripts

Node.js backend to connect frontend with APIs and Python scripts.

Frontend (React-based, not detailed here).

Jupyter notebooks to test Python scripts (LangChain + Gemini).

Finalized Python scripts hosted via FastAPI.

Create .env file under each folder

1. Add MONGODB_URL, SERV_PORT, JWT_SECRET in .env file under BackEnd folder
2. Add GEMINI_API_KEY in .env file under LangChain folder
3. Add GEMINI_API_KEY in .env file under PythonScripts folder

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⚙️ Backend Setup (Node.js)

Developed using Node.js, Express.js, and MongoDB for a RESTful API structure.
Implements secure authentication and scalable database integration.

1. Navigate to the BackEnd folder:

cd BackEnd

2. Install the required dependencies:

npm install

3. Setup nodemon:

npm install -g nodemon

4. Launch Node.js BackEnd:

nodemon index.js

🖥️ Frontend Setup (React.js)

Built using React.js with Tailwind CSS for styling and Vite as the build tool.

1. Navigate to the FrontEnd folder:

cd FrontEnd

2. Install the required dependencies:

npm install

3. Lauch React.js FrontEnd:

npm run dev

🔬 LangChain + Jupyter Lab Environment Setup

This setup allows you to develop and test LangChain-based Python scripts using Gemini.

1. Navigate to the LangChain folder:

cd LangChain

2. Create a new Conda environment:

conda create --prefix ./EduGenie.env python=3.10

3. Activate the environment:

conda activate ./EduGenie.env

4. Install JupyterLab:

conda install jupyterlab

5. Install required Python packages:

pip install PyMuPDF langchain langchain-google-genai google-generativeai langchain-community python-dotenv

6. Launch JupyterLab:

jupyter lab

🚀 PythonScripts (FastAPI Backend)

The PythonScripts folder contains finalized Python APIs. These are designed to be hosted using FastAPI for production usage.

1. Navigate to PythonScripts folder:

cd PythonScripts

2. Create virtual environment:

python -m venv env

4. Activate the environment:

./env/Scripts/activate

5. Install required Python packages:

pip install uvicorn fastapi PyMuPDF langchain langchain-google-genai google-generativeai langchain-community python-dotenv

6. Lauch FastAPI Backend:

uvicorn main:app --reload