Stream Monitor - AI LiveStream Monitoring Tool

5aa473eba3184692bbe4b9c5868b76a3

Overview

Stream Monitor is a comprehensive AI-powered application designed to monitor and analyze live video streams in real-time. The application utilizes advanced computer vision and machine learning algorithms to detect events, objects, and anomalies in video feeds, providing timely alerts and insights to users.

Architecture

The application follows a microservices architecture with two main components:

• Frontend: A lightweight React application served via Busybox httpd
• Backend: API server handling stream processing, AI analysis, and data storage

Technology Stack

• Frontend: React.js, served by Busybox httpd
• Backend: Python (Flask/FastAPI)
• Containerization: Docker
• Orchestration: Kubernetes
• Container Registry: AWS ECR
• CI/CD: GitHub Actions

Getting Started

Prerequisites

• Docker and Docker Compose for local development
• kubectl and AWS CLI for production deployment
• Node.js 16+ for frontend development

Local Development

1. Clone the repository:

   git clone https://github.com/kashimkyari/AI_LiveStream_Monioring_Tool.git
   cd AI_LiveStream_Monioring_Tool

2. Start the development environment:

   docker-compose up -d

3. Access the application:

   • Frontend: http://localhost:3000
   • Backend API: http://localhost:5000

Production Deployment

1. Build and push Docker images:

   # Build and push frontend image
   docker build -t 038462756156.dkr.ecr.us-east-1.amazonaws.com/stream-monitor-frontend:latest -f frontend/Dockerfile frontend/
   docker push 038462756156.dkr.ecr.us-east-1.amazonaws.com/stream-monitor-frontend:latest
   
   # Build and push backend image (command will vary based on backend Dockerfile location)

2. Deploy to Kubernetes:

   kubectl apply -f k8s/deploy-frontend.yml
   kubectl apply -f k8s/deploy-backend.yml  # Assuming this file exists

Features

• Real-time video stream monitoring
• Object detection and tracking
• Event and anomaly detection
• Customizable alerts and notifications
• Historical data analysis and reporting
• User-friendly dashboard interface

Security Features

The application implements several security best practices:

• Non-root container execution
• Read-only root filesystem in production
• Minimized container capabilities
• Resource limits and requests
• Health checks and liveness probes
• Rolling update deployment strategy

Monitoring and Maintenance

Checking Application Status

# Get pod status
kubectl get pods -l app=stream-monitor-frontend

# View frontend logs
kubectl logs -l app=stream-monitor-frontend

# View backend logs
kubectl logs -l app=stream-monitor-backend

Scaling

The application utilizes Horizontal Pod Autoscaler to automatically adjust to traffic demands:

# Check HPA status
kubectl get hpa

Manual scaling is also possible:

kubectl scale deployment stream-monitor-frontend --replicas=3

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some 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.

Contact

Project maintainer: DevOps Team - [email protected]

GitHub Repository: https://github.com/kashimkyari/AI_LiveStream_Monioring_Tool