Wiggly - AI-Powered 3D Interactive Animation Design Platform

Wiggly is an innovative platform that transforms user-uploaded images into custom T-shirt designs with AI-generated textures and 3D models. The platform combines computer vision, AI texture generation, and 3D modeling to create personalized apparel.

🚀 Features

• AI Texture Generation: Upload front and back images to generate seamless T-shirt textures
• 3D Model Creation: Automatically generate 3D T-shirt models with applied textures
• Real-time Processing: Fast texture and model generation using Modal cloud infrastructure
• Multiple Deployment Options: Support for both direct API and n8n workflow integration
• Cloud-Native Architecture: Scalable deployment on Modal with Weaviate vector database

🏗️ Architecture

The platform follows a modern cloud-native architecture:

📁 Project Structure

├── frontend/                 # Next.js frontend application
│   ├── src/
│   │   ├── app/             # App router pages
│   │   └── components/      # React components
│   └── public/              # Static assets
├── wiggle/
│   ├── backend/             # Backend API and services
│   │   ├── modal_integrated_deploy.py  # Main Modal deployment
│   │   ├── direct_api.py               # Direct API server
│   │   ├── weaviate_*.py              # Database utilities
│   │   ├── n8n_workflow.json          # N8N automation workflow
│   │   └── README.md                  # Backend documentation
│   └── frontend/            # Alternative frontend (if needed)
├── front.png               # Sample front image
├── back.png                # Sample back image
└── README.md               # This file

🛠️ Technology Stack

Frontend

• Next.js 14 - React framework with App Router
• TypeScript - Type-safe development
• Tailwind CSS - Utility-first styling
• Three.js - 3D model rendering

Backend

• FastAPI - High-performance Python web framework
• Modal - Serverless cloud platform for AI workloads
• Blender - 3D modeling and texture application
• Weaviate - Vector database for AI embeddings
• N8N - Workflow automation platform

AI/ML

• Computer Vision - Image processing and analysis
• Texture Synthesis - AI-powered texture generation
• 3D Modeling - Automated T-shirt model creation

🚀 Quick Start

Prerequisites

• Node.js 18+ and npm/bun
• Python 3.12+
• Modal account and CLI
• Weaviate cloud instance

1. Frontend Setup

cd frontend
npm install
npm run dev

The frontend will be available at http://localhost:3000

2. Backend Setup

Option A: Modal Cloud Deployment (Recommended)

cd wiggle/backend
pip install modal
modal token new
modal deploy modal_integrated_deploy.py

Option B: Local Development

cd wiggle/backend
pip install -r requirements_direct_api.txt
python run_direct_api.py

3. Environment Configuration

Create .env files with the following variables:

WEAVIATE_GRPC_ENDPOINT=your_weaviate_endpoint
WEAVIATE_API_KEY=your_weaviate_key
MODAL_API_URL=your_modal_deployment_url

📡 API Endpoints

Main API (Modal Deployment)

• Base URL: https://ykzou1214--wiggle-integrated-api-fastapi-app.modal.run

Endpoints:

• POST /api/texture/generate - Generate texture and 3D model
• GET /api/texture/download/{filename} - Download generated texture
• GET /api/model/download/{filename} - Download 3D model
• GET /health - Health check

Example Usage:

curl -X POST "https://ykzou1214--wiggle-integrated-api-fastapi-app.modal.run/api/texture/generate" \
  -F "userId=user123" \
  -F "front=@front.png" \
  -F "back=@back.png"

Response:

{
  "status": "completed",
  "buildId": "uuid-here",
  "modelUrl": "/api/model/download/model_uuid.glb",
  "textureUrl": "/api/texture/download/texture_uuid.png",
  "message": "3D model with texture generated successfully"
}

🔄 N8N Workflow Integration

The project includes a pre-configured N8N workflow for complete automation:

• File: wiggle/backend/n8n_workflow.json
• Features:
  • Automated texture generation pipeline
  • Webhook integration for external triggers
  • Database storage with Weaviate
  • Error handling and logging
  • File management and cleanup
• Setup: Import the JSON file into your N8N instance
• Documentation: See N8N Deployment Guide for detailed setup instructions

🧪 Testing

Test the Complete Pipeline:

# Test texture generation
curl -X POST "https://ykzou1214--wiggle-integrated-api-fastapi-app.modal.run/api/texture/generate" \
  -F "userId=test_user" \
  -F "front=@front.png" \
  -F "back=@back.png"

# Download generated files
curl -o model.glb "https://ykzou1214--wiggle-integrated-api-fastapi-app.modal.run/api/model/download/model_id.glb"
curl -o texture.png "https://ykzou1214--wiggle-integrated-api-fastapi-app.modal.run/api/texture/download/texture_id.png"

📊 Performance

• Texture Generation: ~30-60 seconds
• 3D Model Creation: ~10-20 seconds
• File Sizes:
  • Textures: ~200KB PNG
  • Models: ~1KB OBJ format
• Concurrent Users: Scales automatically with Modal

🔧 Development

Local Development Setup:

# Backend
cd wiggle/backend
python -m venv venv
source venv/bin/activate  # or `venv\Scripts\activate` on Windows
pip install -r requirements_direct_api.txt
python run_direct_api.py

# Frontend
cd frontend
npm install
npm run dev

Deployment:

# Deploy to Modal
modal deploy modal_integrated_deploy.py

# Build frontend
npm run build

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

For support and questions:

• Check the Backend README for detailed setup instructions
• Review the N8N Deployment Guide
• Open an issue on GitHub

🎯 Roadmap

• Support for additional garment types
• Advanced texture customization options
• Real-time 3D preview
• Mobile app development
• Marketplace integration