Skip to content

pingqLIN/url-hero

Repository files navigation

URL-HERO banner

URL-HERO

React 19 TypeScript 5.8 Vite 6 Gemini powered OpenAI image support

Guess what your website mascot would be, then move through a clean four-step flow to turn that guess into a 3D concept, a polished prompt, and a fast preview image.

Drop in a domain. Discover the mascot hiding inside it.

Quick StartWhat It DoesWorkflowModel SupportDevelopment

Overview

URL-HERO is an AI visual concept generator built around one playful question: what would this website look like as a mascot? It reads the meaning behind a domain, guesses the character hiding inside it, and turns that idea into a 3D concept plus an English prompt ready for image generation.

  • Convert a plain URL into a character brief with personality, materials, accessories, scene, and lighting.
  • Generate a four-step editing flow that is easy to follow, review, and reuse.
  • Produce preview images with Gemini image models or OpenAI image generation.

View the AI Studio app: https://ai.studio/apps/c2ff7197-fe31-4aa0-bdf0-e75fb3bcf085

What It Does

1. Guesses the mascot

The core idea is simple: paste a URL and let the app guess what kind of mascot that website wants to be.

2. Reads the brand signal

The app interprets a target domain through naming, service meaning, and implied visual identity.

3. Builds the mascot concept

It generates:

  1. Core concept and visual keywords
  2. Character species and body design
  3. Tools, outfit, and signature accessories
  4. Scene and interface-aware environment
  5. Lighting and rendering direction
  6. A final English image prompt

4. Pushes the concept toward an image

The final prompt can be copied, regenerated, and used immediately for preview image generation inside the app.

Quick Start

Prerequisites

  • Node.js
  • A Gemini API key, OpenAI API key, or Anthropic API key for text generation
  • An optional OpenAI API key for OpenAI image preview generation

Install

npm install

Configure

Create a .env.local file and set the provider keys you want to use:

GEMINI_API_KEY="YOUR_GEMINI_API_KEY"
OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
ANTHROPIC_API_KEY="YOUR_ANTHROPIC_API_KEY"

Run

npm run dev

Open http://localhost:3000 after the dev server starts.

Workflow

  1. Enter a target domain such as spotify.com or tw.yahoo.com.
  2. Generate the mascot concept and review the analysis cards.
  3. Refine the final English prompt if needed.
  4. Generate a preview image from the prompt and tune the image controls.

Model Support

Stage Providers Notes
Text analysis Google Gemini Fully supported
Text analysis OpenAI Fully supported with a valid OpenAI API key
Text analysis Anthropic Fully supported with a valid Anthropic API key
Preview image generation Gemini image models Supports Gemini image generation flows directly in the app
Preview image generation OpenAI image models Requires a direct OpenAI API key

Output Format

The generated concept is intentionally split for creative control:

Section Output
Section 1 Core idea and style keywords
Section 2 Mascot species, face, body, and material design
Section 3 Props, outfit, and functional accessories
Section 4 Environment and scene composition
Section 5 Lighting and rendering mood
Section 6 Final English visual prompt

Notes:

  • Sections 1 to 5 are generated in Traditional Chinese.
  • Section 6 is always generated in English for image-model compatibility.
  • The prompt is designed to frame the website name as a glowing neon hologram inside the scene.

Development

npm run dev
npm run build
npm run lint

Tech Stack

Layer Tools
Frontend React 19, TypeScript, Vite
Motion Motion
UI Icons Lucide React
AI Text Google Gemini, OpenAI, Anthropic
AI Image Gemini image models, OpenAI image models

Why This Project Is Fun

Most URL tools stop at metadata. This one asks a better question: "guess what your website mascot is." Then it turns that answer into something visual, character-driven, and surprisingly pitch-ready.

About

URL to Mascot

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors

Languages