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Wardrowbe

Your closet already knows what you own; Wardrowbe is the part that remembers it for you and decides what goes together tomorrow morning. Photograph garments one at a time or several in a single shot, and a vision model tags each piece with its type, color, pattern, fabric, style, and formality, with a confidence score you can correct. From that catalog the app builds complete outfits out of clothes you already have, weighing your local weather, the occasion, notes you attached to individual pieces, and the aesthetic you described. A daily suggestion can arrive on a schedule through ntfy, Mattermost, or email, so the outfit is waiting before you are. Accept, reject, and rate what you wear, and the history calendar keeps every past recommendation while the feedback sharpens the next ones. Pairings work the other way around too: pick one item and get outfits built around it, each with a plain-language explanation of why the combination works. Analytics show what you actually wear, what never leaves the rail, and how your colors distribute, and the wardrobe view flags pieces that need washing. Households manage several members' wardrobes with personalized suggestions for each person. Everything runs against any OpenAI-compatible endpoint, so Ollama with a local multimodal model keeps the whole pipeline offline and free. The interface speaks eight languages and follows your browser by default. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Wardrowbe
Wardrowbe
Wardrowbe
Wardrowbe
Wardrowbe
Wardrowbe

Benefits

  • Photos Become A Catalog
  • Upload one garment or a pile in a single photo and the vision model finds each piece, tagging type, color, pattern, fabric, and formality with a confidence score you can edit.
  • Outfits Matched To Your Day
  • Daily recommendations combine local weather, the occasion, per-item notes, and your stated aesthetic, and scheduled delivery via ntfy, Mattermost, or email puts the outfit on your phone each morning.
  • Local AI, No Bill
  • Any OpenAI-compatible endpoint works, and pairing Wardrowbe with Ollama and a local multimodal model such as Gemma 3 keeps photo analysis and outfit generation entirely offline and free.
  • The Whole Household Dresses
  • Family wardrobes give each household member their own catalog and personalized suggestions, while wear history, ratings, and analytics reveal forgotten items and what each person actually wears.

Features

  • AI Photo Tagging
  • A vision model (OpenAI, Ollama, LocalAI, or any OpenAI-compatible API) detects multiple garments per photo and extracts colors, patterns, styles, and formality in a background worker.
  • Weather-Based Outfits
  • Set your location once and recommendations factor in the forecast and occasion; scheduled notifications deliver the day's outfit through ntfy, Mattermost, or email.
  • Pairings With Reasons
  • Choose any item and Wardrowbe builds outfits around it, explaining each combination in plain language with styling tips, so suggestions teach rather than just prescribe.
  • Wear Tracking Analytics
  • Rate outfits, log what you wore, and see most-worn, least-worn, and never-worn items plus color distribution; the grid flags pieces that need washing after wear.
  • Households And Languages
  • Multiple household members keep separate wardrobes with personalized suggestions. The interface ships in eight languages and can follow the browser, with OIDC and trusted-header sign-in options.

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