AWS Nova Canvas Image Generation

From Thought - To Concept
Nova Canvas »

An optimized, high-performance Gradio application for advanced image generation using AWS Nova Canvas. This refactored version provides comprehensive image manipulation capabilities with improved error handling, performance optimizations, and better monitoring.

📋 Capabilities

• Text to Image: Generate images from text prompts
• Inpainting: Modify specific image areas
• Outpainting: Extend image boundaries
• Image Variation: Create image variations
• Image Conditioning: Generate images based on input image and text
• Color Guided Content: Create images using reference color palettes
• Background Removal: Remove image backgrounds
• Health Monitoring: Real-time system health and performance metrics

🛠 Prerequisites

• AWS credentials configured (AmazonBedrockFullAccess)
• HF Token for NSFW content checking (optional)
• Python >= 3.12
• Docker (for containerized deployment)

📦 Installation

git clone <repository-url>
cd canvas-demo
pip install -r requirements.txt

⚙️ Configuration

Create a .env file in the root directory:

# AWS Configuration (Lambda-compatible names)
AMP_AWS_ID=<your-aws-access-key>
AMP_AWS_SECRET=<your-aws-secret-key>
AWS_REGION=us-east-1
BUCKET_REGION=us-west-2
NOVA_IMAGE_BUCKET=<your-bucket-name>

# Optional Features
HF_TOKEN=<huggingface-token>
ENABLE_NSFW_CHECK=true
RATE_LIMIT=20
LOG_LEVEL=INFO

# Lambda Configuration (auto-detected)
AWS_LAMBDA_FUNCTION_NAME=<function-name>  # Auto-set in Lambda
AWS_LAMBDA_HTTP_PORT=8080                 # Auto-set in Lambda

🚀 Running the Application

Local Development

python app.py

Docker Deployment

docker build -t canvas-demo.
docker run -p 8080:8080 --env-file .env canvas-demo

AWS Lambda Deployment

The application automatically detects Lambda environment and configures accordingly.

CloudWatch Logging

When deployed to Lambda, all application logs are automatically sent to CloudWatch:

• Log Group: /aws/lambda/canvas-demo (auto-created by AWS)
• Log Stream: Canvas-Stream (auto-created by application)
• Log Format: [timestamp] message
• Batching: Logs are batched for performance (10 logs or 30 seconds)

To view logs in AWS Console:

1. Navigate to CloudWatch > Log groups
2. Select /aws/lambda/canvas-demo
3. Click on Canvas-Stream log stream

📊 Monitoring & Health Checks

Health Check Endpoint

Access /health for health status or use the "System Info" tab in the UI.

Performance Metrics

• Request/error rates
• Memory usage
• Service health status
• Rate limiting statistics

🏗 Architecture

src/
├── models/          # Configuration and data models
├── services/        # Business logic and AWS integrations
├── handlers/        # Request handlers and business operations
└── utils/           # Utilities (logging, exceptions, etc.)

Key Components

• Config Management: src/models/config.py - Centralized configuration
• AWS Services: src/services/aws_client.py - Optimized AWS client management
• Image Processing: src/services/image_processor.py - Async image operations
• Rate Limiting: src/services/rate_limiter.py - Optimized rate limiting
• Health Monitoring: src/handlers/health.py - System health checks
• Canvas Operations: src/handlers/canvas_handlers.py - Main business logic

🔧 Technical Details

• Model: Amazon Nova Canvas (amazon.nova-canvas-v1:0)
• Prompt Model: Amazon Nova Lite (us.amazon.nova-lite-v1:0)
• Default Resolution: 1024x1024
• Supported Formats: PNG, JPG
• Max Image Size: 4MP (4194304 pixels)
• Rate Limiting: Configurable (default: 20 requests/20min)

🚨 Error Handling

• Graceful Degradation: Services continue operating even if non-critical components fail
• User-Friendly Messages: Clear error messages without technical details
• Automatic Retries: Intelligent retry logic for transient failures
• Circuit Breakers: Prevent cascade failures from external services

🔒 Security

• Input validation and sanitization
• No credentials in logs
• Secure error handling
• Rate limiting protection
• NSFW content filtering (optional)

📝 License

Apache 2.0 License

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

📞 Support

For issues and questions, please use the GitHub issue tracker.