NJIT Global Explorer

Welcome to the NJIT Global Explorer, a comprehensive, full-stack interactive dashboard designed specifically for the analysis and visualization of NJIT's Study Abroad Program data. This tool is intended for use by the NJIT Office of Global Initiatives, providing them with a powerful resource to draw meaningful insights from the program's data.

NJIT Global Explorer is live at njitglobalexplorer.me

Technologies

The NJIT Global Explorer is built using a variety of modern technologies:

• Flask: A robust Python web framework.
• Bootstrap: A popular CSS framework for responsive, mobile-first front-end web development.
• HTML/CSS: Standard technologies for building web pages.
• JavaScript: A high-level, interpreted programming language for interactive web pages.
• Chart.js: A JavaScript library for creating beautiful charts.
• Meta-Llama-3-8B-hf LLM model: A powerful language model for data analysis.
• Firebase: A platform developed by Google for creating mobile and web applications. It provides secure data storage and user authentication.

Project Funding and Accolades

The NJIT Global Explorer is a product of the NJIT Honors Summer Research Institute, where it was developed as a paid research project. The project's primary focus is to analyze the impact of the NJIT Study Abroad Program. The project has been recognized and presented at the NJIT Undergraduate Summer Research Symposium.

Getting Started

Follow these instructions to get a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

Ensure you have the following installed on your system:

• Python 3.6 or higher
• pip (Python package installer)
• A Firebase account and a new project for testing

Installation

Follow these steps to set up the project:

1. Clone the repository to your local machine using git clone https://github.com/rshah713/NJITGlobalExplorer.git
2. Navigate to the project directory using cd NJITGlobalExplorer
3. Install the required Python packages using pip install -r requirements.txt
4. Create a .env file in the root directory and populate it with your Firebase project credentials (API key, database URL, etc.)
5. Register for the Meta-Llama-3-8B-hf LLM model using togetherAI and obtain your API key.
6. Add the togetherAI API key to your .env file.
7. Start the Flask server using python main.py, and visit http://127.0.0.1:5000 to view the project.