AI Stack Integrator

A comprehensive collection of AI-powered image processing and generation tools with implementations in multiple programming languages.

📁 Project Structure

Each AI feature is organized in its own folder with 4 language implementations:

AIStack-Integrator/
├── 01-remove-background/          # Background removal
├── 02-cleanup-picture/            # Image cleanup and enhancement
├── 03-expand-photo/               # Photo expansion
├── 04-replace-item/               # Object replacement
├── 05-cartoon-character/          # Cartoon character generation
├── 06-caricature-generator/       # Caricature creation
├── 07-ai-avatar/                  # AI avatar generation
├── 08-product-photoshoot/         # Product photography
├── 09-background-generator/       # Background generation
├── 10-ai-portrait/                # Portrait generation
├── 11-ai-faceswap/                # Face swapping
├── 12-ai-outfit/                  # Virtual outfit try-on
├── 13-ai-image2image/             # Image-to-image transformation
├── 14-ai-sketch2image/            # Sketch to image conversion
├── 15-ai-hairstyle/               # Hairstyle generation
├── 16-ai-upscaler/                # Image upscaling
├── 17-ai-filter/                  # AI filters
├── 18-ai-haircolor/               # Hair color change
├── 19-ai-virtual-tryon/           # Virtual try-on
├── 20-ai-headshot/                # Professional headshots
└── 21-haircolor-rgb/              # RGB hair color manipulation

🚀 Available Implementations

Each folder contains implementations in 4 programming languages:

• Node.js (.js files) - JavaScript/TypeScript implementation
• Python (.py files) - Python implementation
• Kotlin (.kt files) - Android/Kotlin implementation
• Swift (.swift files) - iOS/Swift implementation

📋 Prerequisites

For Node.js Implementation:

npm install axios
# or
yarn add axios

For Python Implementation:

pip install requests pillow

For Kotlin Implementation:

• Android Studio
• Add dependencies in build.gradle:

implementation 'com.squareup.okhttp3:okhttp:4.9.3'
implementation 'com.google.code.gson:gson:2.8.9'

For Swift Implementation:

• Xcode
• Add dependencies in Package.swift or use CocoaPods:

pod 'Alamofire'
pod 'SwiftyJSON'

🔑 API Key Setup

1. Get your API key from LightX API
2. Replace YOUR_API_KEY in the code with your actual API key
3. For production, use environment variables:

Node.js:

const API_KEY = process.env.LIGHTX_API_KEY || 'YOUR_API_KEY';

Python:

import os
API_KEY = os.getenv('LIGHTX_API_KEY', 'YOUR_API_KEY')

Kotlin:

val apiKey = BuildConfig.LIGHTX_API_KEY ?: "YOUR_API_KEY"

Swift:

let apiKey = Bundle.main.object(forInfoDictionaryKey: "LIGHTX_API_KEY") as? String ?? "YOUR_API_KEY"

🎯 How to Use

1. Choose Your Feature

Navigate to the folder of the AI feature you want to use (e.g., 01-remove-background/)

2. Select Your Language

Choose the implementation file for your preferred language:

• lightx-[feature]-nodejs.js for Node.js
• lightx-[feature]-python.py for Python
• LightX[Feature].kt for Kotlin
• LightX[Feature].swift for Swift

3. Configure the Code

• Add your API key
• Set the input image path
• Configure any additional parameters
• Set the output path

4. Run the Code

Node.js:

node lightx-[feature]-nodejs.js

Python:

python lightx-[feature]-python.py

Kotlin:

• Open in Android Studio
• Build and run on device/emulator

Swift:

• Open in Xcode
• Build and run on device/simulator

📝 Example Usage

Background Removal (Node.js):

const fs = require('fs');
const axios = require('axios');

const API_KEY = 'YOUR_API_KEY';
const inputImage = 'path/to/input.jpg';
const outputPath = 'path/to/output.png';

// Read image file
const imageBuffer = fs.readFileSync(inputImage);

// Make API request
const response = await axios.post('https://api.lightx.ai/v1/remove-background', {
    image: imageBuffer.toString('base64')
}, {
    headers: {
        'Authorization': `Bearer ${API_KEY}`,
        'Content-Type': 'application/json'
    }
});

// Save result
fs.writeFileSync(outputPath, Buffer.from(response.data.result, 'base64'));

🔧 Common Parameters

Most implementations support these common parameters:

• image: Input image (base64 encoded or file path)
• output_format: Output format (png, jpg, webp)
• quality: Image quality (1-100)
• resolution: Output resolution
• style: Processing style (varies by feature)

📊 API Response Format

{
    "success": true,
    "result": "base64_encoded_image",
    "processing_time": 2.5,
    "credits_used": 1
}

🛠️ Troubleshooting

Common Issues:

1. API Key Error: Make sure your API key is valid and has sufficient credits
2. Image Format: Supported formats are JPG, PNG, WEBP
3. File Size: Maximum file size is usually 10MB
4. Rate Limits: Check your API rate limits and usage

Error Codes:

• 401: Invalid API key
• 402: Insufficient credits
• 413: File too large
• 415: Unsupported file format
• 429: Rate limit exceeded

📞 Support

• Documentation: LightX API Docs
• Support: LightX Support
• Community: LightX Community

📄 License

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

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

📈 Roadmap

• Add more AI features
• Implement batch processing
• Add web interface
• Create Docker containers
• Add unit tests
• Implement caching

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Note: This is a collection of implementation examples. Make sure to follow LightX API terms of service and usage policies.