StudySprint - AI-Powered Study Group Platform

StudySprint is a dynamic web application designed to help students connect and form effective study groups. It features an AI-powered matching system, real-time group chat, and accountability tools to enhance the learning experience. The platform includes a main landing page and a feature-rich study group hub.

✨ Features :

The platform is split into a landing page and the main study group application, with the following key features:

*Main Application (/study-groups):

• 👤 User Profiles: Students can create a profile with their name, university, subjects, and study preferences.
• 🎯 AI-Powered Matching: The platform suggests study groups based on shared subjects, goals, and schedules.
• 👥 Group Management: Users can browse existing groups, join them, or create their own.
• 💬 Real-Time Group Chat: Integrated chat allows for seamless communication between group members.
• 📈 Progress Tracking: An accountability section helps students track their study goals, streaks, and session attendance.
• 🤖 AI Assistant: A helpful chatbot, powered by the Gemini API, provides study tips, book recommendations, and answers to academic questions.

Landing Page (/):

• 🚀 Modern Landing Page: A visually appealing home page to attract and inform new users.
• Responsive Design: The entire site is built to work seamlessly across desktops and mobile devices.

🛠️ Tech Stack :

This project is built with a modern set of technologies:

• Backend ::
  • Python: The primary backend language.
  • Flask: A lightweight web framework used to build the web server and API.
  • Flask-SocketIO: Enables real-time, bidirectional communication for the chat feature.
  • Gunicorn: A robust WSGI server for running the application in production.
  • Eventlet: A concurrent networking library required for Flask-SocketIO with Gunicorn.
• Frontend:
  • HTML5: For the structure of the web pages.
  • CSS3: For custom styling and responsive design.
  • JavaScript: To handle interactive elements, page navigation, and communication with the backend.
• External APIs:
  • Google Gemini API: Powers the AI assistant for providing intelligent study support.

⚙️ Setup and Installation

Follow these steps to get the project running on your local machine.

1. Prerequisites :

• Python 3.8+ and pip
• A virtual environment tool (venv)

2. Clone the Repository

First, clone the project to your local machine (if it's in a git repository).

git clone <your-repository-url>
cd vibehack