🛠️ Sustainable Shopper: Our Journey

💡 Inspiration

We were inspired by the growing need for eco-conscious fashion and the lack of accessible tools to help people shop sustainably. We set out to make sustainability stylish, simple, and tech-driven.

📚 What We Learned

The landscape of virtual try-on (VTON) models, including open-source solutions and commercial APIs
How to integrate LLMs for contextual outfit recommendations and wardrobe interactions
The importance of UX in sustainability tech, making green choices easy and intuitive

🏗️ How We Built It

Virtual Try-On: After testing various open-source models and paid APIs, we chose Fashn.ai for its high accuracy and realism
Backend: Built using Flask, handling authentication, product data, and API integration
Wardrobe Assistant: Powered by large language models, it suggests outfits and answers style queries based on your wardrobe
Marketplace: Curated list of sustainable clothing items, scraped and filtered using sustainability tags and metadata

⚠️ Challenges & Solutions

VTON quality vs. cost: Open-source models lacked realism, so we opted for Fashn.ai’s API to deliver better UX
LLM integration: Prompt engineering was key to tailoring responses to user wardrobes and fashion contexts
Sustainability data: Reliable eco-tagged product data was scarce, so we built custom scrapers and filters

Sustainable Shopper brings AI, fashion, and sustainability together to change the way people shop—for the better.