AI Closet is a cross-platform mobile application for iOS and Android that digitizes wardrobes, offers outfit inspirations, and enables virtual try-ons. It targets users looking to manage their clothing, experiment with styles, and visualize looks, benefiting from AI-driven automation for organization and creativity.

How It Works

Built with React Native and Expo, AI Closet leverages AI for automatic background removal and smart categorization of clothing items, reducing manual effort. Users can digitally store garments, mix and match them on a freeform canvas to create and save outfits, and utilize virtual try-on technology to preview how items might look on them. This integrated approach aims to streamline wardrobe management and styling.

Quick Start & Requirements

To run the application, clone the repository, navigate to the directory, and install dependencies using npm install. The app is launched with npm start via the Expo CLI. Core AI features require API keys for OpenAI, fal.ai (for background removal), and Kwai Kolors (for virtual try-on), which must be configured as environment variables (EXPO_PUBLIC_OPENAI_KEY, EXPO_PUBLIC_FAL_KEY, EXPO_PUBLIC_KWAI_ACCESS_KEY, EXPO_PUBLIC_KWAI_SECRET_KEY).

Highlighted Details

• Key differentiator: Integrated virtual try-on combined with comprehensive wardrobe management.
• AI-powered features include automatic background removal, smart clothing categorization, and virtual try-on.
• Provides cost estimations for AI services and plans a future credit-based system for premium features like virtual try-ons.
• Developed using TypeScript, React Native, and Expo for cross-platform mobile deployment.

Maintenance & Community

Contributions are welcomed via GitHub Issues for bug fixes and feature proposals, with discussions encouraged for questions and suggestions. The project appears to have active community engagement channels.

Licensing & Compatibility

This project is licensed under the MIT License, which generally permits commercial use and integration into closed-source projects.

Limitations & Caveats

Currently, only client-side components are implemented; cloud integration, a credit-based system, and backend services are in the planning stages. The core AI functionalities rely directly on third-party API keys configured in the client.