A clothing pairing app

A clothing pairing app



Tópico: A clothing pairing app
Categoria: Tutoriais | Programação & Tecnologia
Idioma Principal: Português (Conteúdo de Tecnologia)

Descrição do Conteúdo / Informações:
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This is a submission for the Gemma 4 Challenge: Build with Gemma 4



What I Built


A clothing pairing app — upload a photo of one piece you own, get a complementary match from a curated catalogue, then see both styled together in an AI-generated editorial image.

The product catalogue is the Myntra fashion dataset (~44k products), available at kaggle.com/datasets/paramaggarwal/fashion-product-images-dataset.



Demo


pair-vite.vercel.app



Code




sea2709
/
pair







Pair.

A clothing pairing app — upload a photo of one piece you own, get a complementary match from a curated catalogue (which is downloaded at https://www.kaggle.com/datasets/paramaggarwal/fashion-product-images-dataset), then see both styled together in an AI-generated editorial image.

How it works

•
Upload a photo of a top, bottom, shoes, or accessory

•
Analyze — Gemma 4 identifies the piece: category, gender, style, palette

•
Match — a complementary item is selected from ~44k Myntra products via MongoDB + Gemma 4

•
Lookbook — Gemini generates a fashion editorial photo of both pieces styled on a model

Stack

Layer
Tech

Frontend
Vite + React + TypeScript + Tailwind CSS v4

Backend
Hono on Node.js

AI
Google GenAI SDK (Gemma 4 + Gemini)

Database
MongoDB (Myntra product dataset)

Bot protection
Google reCAPTCHA v3

Prerequisites

• Node 24 (use nvm use — .nvmrc is included)

• MongoDB running locally

• Google AI Studio API key — aistudio.google.com

• Google reCAPTCHA...

View on GitHub



How I Used Gemma 4


Gemma 4 (gemma-4-31b-it) is used for two tasks where we need text output from a structured prompt, not image generation:

Clothing analysis — given an image, return a JSON object with category, gender, style, colors, etc.

Match selection — given 30 product candidates from MongoDB, pick the best one and return a JSON object with the index, match score, and stylist note.