AuraBeat: Context-Aware Music Curator Project Overview A

AuraBeat: Context-Aware Music Curator

Project Overview A full-stack, AI-driven application that generates highly personalized music soundscapes by blending real-time environmental data with deep user sentiment analysis. Built to seamlessly integrate advanced LLM decision-making with frontend aesthetics, the platform curates audio-visual experiences that map exactly to the user's current mood and local weather conditions.

Core Technologies & Frameworks

React 18, Tailwind CSS, Framer Motion

Node.js, Express.js

Google Gemini AI

YouTube Data API v3

Open-Meteo & Nominatim APIs

Key Features & Engineering Highlights

Context-Aware LLM Pipeline: Engineered an AI pipeline using Google Gemini to perform sentiment analysis on user input and merge it with real-time weather analytics for highly accurate, contextual song recommendations.

Dynamic UI & Color Theory: Built an ultra-responsive frontend featuring a dynamically shifting UI color palette that programmatically updates based on the AI's aesthetic evaluation of the recommended track.

Multi-API Orchestration: Seamlessly integrated multiple external APIs (YouTube, Open-Meteo, Nominatim) within an Express.js backend to stream official video content and fetch localized environmental data.

Decoupled Architecture: Designed a robust client-server architecture separating the Vite/React frontend from the Node.js backend, ensuring secure API key management and scalable request handling.