ImageCraft Editor Skill

Build a complete AI-powered image editing web application using StepFun's step-image-edit-2 API with a Flask backend and React (Vite) frontend.

When to Use

• User asks to build an image editor, photo editor, or image processing web app
• User wants AI-powered photo editing features (restoration, retouching, color grading, style transfer)
• User mentions StepFun image API integration
• User asks for a "图片编辑网站" or similar

Architecture

Flask backend (port 5050)  →  Vite + React frontend (port 5174)
         ↓
StepFun step-image-edit-2 API

The backend proxies image uploads, applies feature-specific prompts, calls StepFun API, and returns results. The frontend provides the UI with feature selection, drag-and-drop upload, before/after comparison slider, and download.

How to Build

1. Create the Project

Copy the template files from the assets/ directory:

assets/backend/          →  <project>/backend/
assets/frontend/         →  <project>/frontend/

Create the upload/results directories:

mkdir -p backend/uploads backend/results

2. Configure API Key

Copy assets/backend/.env.example to backend/.env and set:

STEPFUN_API_KEY=<user's actual api key>
STEPFUN_BASE_URL=https://api.stepfun.com/v1

If using Step Plan subscription, change base URL to https://api.stepfun.com/step_plan/v1 instead.

3. Install Dependencies

cd backend && pip install -r requirements.txt
cd frontend && npm install

4. Start the Application

# Terminal 1: Backend
cd backend && python app.py

# Terminal 2: Frontend
cd frontend && npx vite --host 0.0.0.0

Backend runs on port 5050. Frontend runs on port 5174 (5173 is often occupied; if 5174 is also busy, Vite will auto-select the next available port).

Feature Modules

The app has 4 features, each with a carefully crafted English prompt for the StepFun API:

老照片修复 (Old Photo Restoration)

• Trigger: Removes scratches, tears, fading, noise, color degradation
• Prompt strategy: Restoration + clarity enhancement + natural color recovery
• API param: feature=restore

人像精修 (Portrait Retouching)

• Trigger: Natural skin smoothing, eye brightening, facial feature enhancement
• Prompt strategy: Natural texture-preserving retouch + studio lighting
• API param: feature=portrait

风景调色 (Landscape Color Grading)

• Trigger: Saturation boost, contrast enhancement, dynamic range expansion
• Prompt strategy: Cinematic travel photography aesthetic
• API param: feature=landscape

风格调整 (Style Transfer)

• Trigger: 8 artistic styles
• Styles: oil painting, watercolor, sketch, anime, cyberpunk, vintage film, Chinese ink wash (水墨画), pop art
• API param: feature=style&style=

API Integration Details

Endpoint

POST https://api.stepfun.com/v1/images/edit

Request Format

{
  "model": "step-image-edit-2",
  "image": "<base64_encoded_image>",
  "prompt": "<feature-specific_english_prompt>",
  "response_format": "b64_json"
}

Image Preprocessing

• Convert RGBA/P-mode images to RGB
• Resize to max 2048px on the longest side to reduce API payload
• Encode to base64 before sending

Response Parsing

{
  "data": [{ "b64_json": "<base64_result>" }]
}

Frontend UI Flow

Three stages:

1. Feature Selection — 4 cards in a 2×2 grid, each with icon, name, description
2. Upload — Drag-and-drop zone + file input; style selector shown for "风格调整"
3. Result — Side-by-side comparison with draggable slider; download button

Key Implementation Notes

• The Vite dev server proxies /api, /uploads, /results to Flask backend at port 5050
• If port 5173 is occupied, update vite.config.js to use 5174
• Image comparison uses CSS clipPath with a range slider for smooth before/after transition
• The .env file in assets/backend/ is renamed to .env.example to avoid leaking keys
• All prompts must be in English for best API results, even though the UI is Chinese
• Old photo restoration and portrait retouching prompts emphasize "natural" and "preserving original" to avoid over-processing