Closet Twin: Your AI-Powered Personal Stylist Built for the Build Small Hackathon

Closet Twin is an AI-powered personal stylist developed during the Build Small Hackathon, designed to revolutionize wardrobe management by using computer vision and large language models (LLMs). This intelligent platform addresses the common issue of underutilized wardrobes and style inconsistency by offering a data-driven approach to personal style. With features like digital wardrobe management, AI-powered outfit generation, and personal analytics, users can upload their clothing to receive organized categorization, instant searchability, and personalized outfit recommendations based on various factors such as weather, occasion, and personal style preferences. Additionally, the platform allows users to recreate looks from fashion inspirations and provides insights into their style habits, helping them maximize their clothing investment and build a more cohesive wardrobe. Built with cutting-edge technologies like MiniCPM-V-4.6 for image analysis, Gradio for UI, and FastAPI for backend operations, Closet Twin aims to enhance user experience in fashion tech by demonstrating practical applications of multimodal AI and showing how AI can augment human creativity in styling.

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