I have all the context I need. Let me build this premium Virtual Try-On web app with the warm cream/beige aesthetic. I'll start by updating the globals.css with the new theme, then create the components.
Now let me create the main components for the Virtual Try-On app:
Now let me create the main page that brings all the components together:
I've built a complete, production-quality Virtual Try-On frontend for a fashion-tech SaaS product. The design features a warm cream/beige color palette with soft brown accents, elegant serif typography for headings (Playfair Display), and a clean, premium aesthetic.
The app includes all requested sections: a sticky navigation with mobile sheet menu, an animated hero section with trust badges, a full try-on workspace with drag-and-drop upload panels, result preview with tabs for comparing original vs. try-on, style controls with sliders and toggles, a gallery of recent try-ons with hover effects, a 3-step "How It Works" section, feature cards, a CTA banner with pricing cards, and a comprehensive footer. The interface is fully responsive with a mobile sticky button for generation, and includes loading states, hover effects, and smooth transitions throughout.
Image Generation
Last updated March 7, 2026
The Vercel AI Gateway supports image generation and editing capabilities. You can generate new images from text prompts, edit existing images, and create variations with natural language instructions.
To see which models AI Gateway supports for image generation, use the Image Gen filter at the AI Gateway Models page.
Integration methods
To implement image generation with AI Gateway, use one of the following methods:
AI SDK: Use the AI SDK for TypeScript/JavaScript applications with native support for streaming, multi-modal inputs, and type-safe model interactions
Chat Completions API: Use the Chat Completions endpoints for compatibility with existing OpenAI integrations across any programming language ,, IMPORTNT Use the Vercel Al Gateway to connect with Nano Banana. When a user uploads two Images, take the outfit from the second image and apply it to the person in the first image. The result should look natural and consistent - the person's face, body type, background, and pose all stay exactly the same, with only the outfit swapped.
Do not use import {google} from "@ai-sdk/google".
Now I understand the setup. I need to create an API route using the Vercel AI Gateway with the Nano Banana model (google/gemini-3.1-flash-image-preview) to perform the virtual try-on. Let me create the API route and update the workspace component to use it.
Now I need to update the ResultPreview component to handle errors, and update the TryOnWorkspace to pass the error prop:
Now let me update the TryOnWorkspace to pass the error prop:
I've implemented the AI-powered virtual try-on functionality using the Vercel AI Gateway with the Nano Banana model (google/gemini-3.1-flash-image-preview). The API route at /api/try-on accepts two images (person and clothing) as base64 data URLs, sends them to the AI model with a detailed prompt for realistic outfit swapping, and returns the generated image. The workspace component now makes real API calls instead of using mock data, with proper error handling and loading states. When users upload both images and click "Generate Try-On", the AI will take the outfit from the clothing image and apply it to the person while preserving their face, pose, and background.
discride the technolgoy and method used to make this webiste most about api and backend