README.md

Saka

A Modern AI-Powered Video Enhancement Microservice

🌐 Overview

Saka is a high-performance video enhancement platform that leverages state-of-the-art AI models to upscale and improve video quality. Built with scalability and performance in mind, it offers both API and CLI interfaces for seamless integration into existing workflows.

✨ Key Features

Category	Features
Enhancement Capabilities	• 2x/4x Video Upscaling • Anime-Optimized Processing • Artifact Removal • Face Enhancement
Performance	• GPU Auto-Scaling • Dynamic Batch Processing • Asynchronous Operations • Multi-GPU Support
Infrastructure	• S3-Compatible Storage • Real-time Monitoring • Docker Support • Quality Metrics (SSIM)

🏡 System Architecture

flowchart TD
    A[Client] -->|HTTP Request| B[serve_enhancer.py]
    B -->|Load Model| C[Real-ESRGAN]
    B -->|Config| D[configs/]
    C -->|Process| E[GPU Processing]
    E -->|Enhanced Video| F[storage.py]
    F -->|Store| G[S3/Local Storage]
    B -->|Metrics| H[Prometheus]
    H -->|Monitor| I[Grafana]
    
    subgraph Saka Microservice
        B
        C
        D
        E
        F
    end
    
    subgraph Monitoring
        H
        I
    end
    
    subgraph Storage
        G
    end

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🚀 Getting Started

Prerequisites

• NVIDIA GPU with CUDA 11.8+
• Docker 20.10+ (for containerized deployment)
• Python 3.10+
• 16GB+ RAM recommended

Installation

1. Clone the Repository

   git clone --recursive https://github.com/vikramxD/saka.git
   cd saka

2. Set Up Python Environment

   python -m venv venv
   source venv/bin/activate  # On Windows: .\venv\Scripts\activate
   pip install -r requirements.txt

3. Configure Environment

   cp .env.example .env
   # Edit .env with your configuration

Docker Deployment

1. Build and Run with Docker Compose

   docker-compose up -d

2. Check Service Status

   docker-compose ps

💻 Usage

API Interface

from saka.client import VideoEnhancerClient

# Initialize client
client = VideoEnhancerClient()

# Enhance video
result = client.enhance_video(
    input_path="input.mp4",
    upscale_factor=2,
    calculate_ssim=True
)

print(f"Enhanced video available at: {result['output_url']}")

CLI Interface

# Basic enhancement
saka enhance video.mp4 --output enhanced.mp4

# Advanced options
saka enhance video.mp4 --scale 4 --quality high --model anime

📎 Configuration

Core Settings (configs/settings.py)

class Settings(BaseSettings):
    realesrgan: UpscalerSettings = UpscalerSettings()
    api: APISettings = APISettings()
    s3: S3Settings = S3Settings()

Available Environment Variables

Variable	Description	Default
SAKA_MODEL_PATH	Path to model weights	./models
SAKA_GPU_IDS	GPUs to use (comma-separated)	0
SAKA_BATCH_SIZE	Processing batch size	4
SAKA_API_PORT	API server port	8000

📊 Monitoring

Metrics Dashboard

Access the Grafana dashboard at http://localhost:3000 with default credentials:

• Username: admin
• Password: admin

Key Metrics

• GPU Utilization
• Processing Latency
• Memory Usage
• Batch Processing Efficiency
• SSIM Quality Scores

Setup Monitoring Stack

./scripts/setup_monitoring.sh

📁 Project Structure

saka/
── api/                 # API implementation
──     ─ client.py       # Client library
──     ─ serve.py        # API server
──     ─ storage.py      # Storage handlers
── configs/            # Configuration
──     ─ settings.py     # Core settings
──     ─ models.py       # Model configs
── models/             # Model implementations
── scripts/            # Utility scripts
── tests/              # Test suite
── docker/             # Docker configurations

📜 Contributing

1. Fork the repository
2. Create your 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.

🙂 Acknowledgments

---

Made with ❤ by VikramxD

Documentation • Issues • Contributing