AI-powered digital wardrobe & personal stylist. Digitize your closet, build your style profile, and let AI dress you every morning.

Built for Hack Canada — targeting four tracks: Best Use of Auth0, Best Use of Cloudinary, Best Use of Google Gemini API, and [MLH] Best Use of Solana.

Outfitted is a high-end web app where users photograph their clothing. The app strips the background via AI and stores every piece in a sleek virtual closet. Items can be marked as Owned (in your physical closet) or Wishlisted (items you want). From there, a Gemini-powered stylist reads your style profile and assembles curated outfit flatlay cards using only your owned items — and Solana records ownership of those items on-chain.

• Aesthetic: Minimalist Luxury — dark mode by default
• Style: Glassmorphism (frosted overlays), masonry/grid layouts, bold typography
• Palette: Monochrome with subtle gold or neon accents — premium fashion platform energy

A high-conversion landing page. Users hit Get Started and are routed through Auth0 for secure sign-up or login.

First-time users go through a two-part onboarding flow:

• The Guide — A skippable tutorial with tips for photographing clothes (flat surface, good lighting, contrasting background).
• The Questionnaire — A 5–10 question multi-step form covering personal style, vibe, fit preferences, and typical occasions.
• The Result — Answers compile into a single User Style string saved to their profile, which becomes the AI's context.

The core engine:

1. Upload — Drag-and-drop clothing photos from phone or computer.
2. Tag — Each item is marked as Owned (you have it) or Wishlisted (you want it).
3. Process — The image is sent to Cloudinary, which returns a clean transparent PNG via AI background removal.
4. On-Chain — Owned items are minted as lightweight NFTs on Solana devnet, creating a verifiable ownership record.
5. Display — Items are laid over glassmorphic cards in a filterable grid. Filter by category, status (Owned/Wishlisted), wear frequency, or search by notes.

Where the magic happens:

1. The Brains — Backend sends the User Style string + owned wardrobe items to the Gemini API. Wishlisted items are excluded — you can't wear what you don't have.
2. The Output — Gemini returns outfit groupings (item IDs) with a 2-sentence stylist explanation for each look.
3. The Canvas — The frontend assembles the transparent PNGs into a beautiful e-commerce-style flatlay Outfit Card using CSS Grid.

The user's identity hub:

• Manage account & logout
• View Style Badges generated from their questionnaire (e.g., "Streetwear Minimalist", "Corporate Goth")
• Retake the questionnaire anytime to update the AI stylist's parameters

outfitted/
├── frontend/     # Next.js app
└── backend/      # API routes & DB layer

Setup instructions coming soon. The app requires Auth0, Cloudinary, Google Gemini API keys, a local PostgreSQL instance, and a Solana wallet (Phantom recommended) connected to devnet.