AuraBeat: Context-Aware Music Curator Project Overview A full-stack, AI-driven application that g...AuraBeat: Context-Aware Music Curator Project Overview A full-stack, AI-driven application that g...
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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.
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