WardrobeAI

Try it, Know it, Own it, Your AI-powered virtual wardrobe assistant for smarter, sustainable shopping.

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Inspiration

Shopping online can be overwhelming, especially when trying to visualize how clothes will look on you or avoid buying duplicates. We wanted to create a smart tool that makes online shopping more personalized, sustainable, and interactive.

What it does

FriendsWithWardrobe allows users to try on clothes virtually through a browser extension. It shows how selected shirts or outfits would look on you using a model and promotes sustainability by checking your wardrobe. For example, if you already own a white shirt, it alerts you before purchase, helping reduce unnecessary consumption. Added a chatbot that can suggest outfits for specific occasions, like interviews, by picking items from the existing wardrobe and mixing and matching them.

How we built it

We built the extension using React and Next.js for frontend and Python for backend development, MongoDB for managing user wardrobes and preferences, and S3 buckets to store clothing images securely. We integrated Google Gemini and Google Nano Banana for AI-based virtual try-ons. The extension interacts directly with shopping websites to provide seamless virtual fitting and sustainability alerts. The whole app has been Dockerized and put in a CICD workflow for easier updates and deployments.

Challenges we ran into

  • Implementing accurate wardrobe scanning required designing efficient queries to search the database (MongoDB) for color, type, and style duplicates. Ensuring fast and reliable matching while minimizing false positives was challenging.
  • Integrating the sustainability feature to accurately detect duplicates in a user’s wardrobe required careful design and data handling.

Accomplishments that we're proud of

We successfully developed a working browser extension that combines AI-driven virtual try-ons with a sustainability-focused wardrobe check. Users can see how clothes look on them before buying and make eco-conscious shopping decisions.

What we learned

We gained experience in AI integration using Google Gemini and Nano Banana, modeling, browser extension development, cloud storage with S3, and building sustainable features in a consumer-facing product. We also learned how to manage real-time data between user wardrobes and shopping websites.

What's next for our FWW

  • Add features where friends/friends circle could share clothing reviews and recommendations
  • Feature to get alerts during shopping sales on the saved outfits.

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