AI-Powered Event Registration & Similarity Checker

A modern web application built with Next.js, Firebase, and Transformers.js that helps organizers host events and automatically prevent identical or highly-similar projects from being registered.

🚀 Key Features

• Custom Event Creation: Organizers can spin up new events, toggle public/private visibility, and construct custom registration forms with text, date, or dropdown fields.
• In-Browser AI Similarity Check: An embedded machine learning model (all-MiniLM-L6-v2 via @xenova/transformers) runs completely client-side. It analyzes project titles and abstracts to ensure uniqueness.
• Duplicate Prevention Gate: When organizers toggle "Enable Abstract Similarity Check," the platform blocks any registration whose abstract has a semantic similarity score ≥ 60% compared to previously approved projects.
• Real-time Database & Auth: Built securely with Firebase Authentication and Firestore to manage users, events, and participant data.
• Beautiful UI: Glassmorphism, deep dark themes, and rich interactive components powered by Tailwind CSS v4.

🛠 Tech Stack

• Framework: Next.js 15 (App Router)
• Language: TypeScript
• Styling: Tailwind CSS 4
• Backend & DB: Firebase (Firestore, Auth)
• Local AI: @xenova/transformers

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💻 Getting Started

1. Clone the repository and install dependencies

npm install

2. Configure Firebase Environment Variables

Create a .env.local file in the root directory and add your Firebase configurations:

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_sender_id"
NEXT_PUBLIC_FIREBASE_APP_ID="your_app_id"
NEXT_PUBLIC_FIREBASE_MEASUREMENT_ID="your_measurement_id"

(Make sure .env.local is listed in your .gitignore so you don't leak your keys when pushing to GitHub).

3. Start the Development Server

npm run dev

Open http://localhost:3000 with your browser to see the result.

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🧠 How the Similarity Check Works

The application uses Transformers.js to pull the lightweight all-MiniLM-L6-v2 NLP model directly into the browser's memory without needing expensive backend GPUs:

1. Calculates multi-dimensional vector embeddings for the title and abstract text.
2. Performs a Cosine Similarity mathematical check against existing abstracts tied to the event.
3. Automatically blocks duplicate scopes if the calculated overlap breaches the 0.6 (60%) combined similarity threshold.

🚀 Deployment

The easiest way to deploy your Next.js app is to use the Vercel Platform. Make sure to define your Environment Variables in the Vercel dashboard prior to building.