tentative Kubernetes deployment configuration

README.md

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+# Media Analyzer
+
+Real-time video streaming and AI analysis platform that demonstrates modern cloud-native architecture and machine learning integration. The system ingests RTMP video streams (from sources like OBS), processes them with computer vision AI models, and provides live analysis results through a responsive web dashboard.
+
+## Features
+
+- **Video Ingestion**: Accept RTMP streams and convert to HLS for web playback
+- **AI Processing**: Real-time object detection (YOLO) and scene analysis (CLIP) on video segments
+- **Live Dashboard**: Angular frontend with WebSocket-powered real-time analysis overlays
+- **Scalable Architecture**: Kubernetes-deployed microservices with configurable processing modes
+- **Cloud Integration**: GCP services integration while maintaining platform agnostic design
+
+## Tech Stack
+
+- **Backend**: Django + Django Channels, PostgreSQL, Redis, Celery
+- **AI/ML**: OpenCV, YOLO, CLIP, Whisper (Hugging Face Transformers)
+- **Frontend**: Angular 17+ with HLS.js video player and Canvas overlays
+- **Infrastructure**: Docker containers, Kubernetes, NGINX
+- **Streaming**: FFmpeg for RTMP’HLS conversion, WebSocket for real-time data
+
+## Quick Start
+
+### Option 1: Docker Compose (Development)
+
+```bash
+# Start all services
+docker compose up
+
+# Run migrations (in separate terminal)
+docker compose --profile tools up migrate
+
+# Access the application
+# Frontend: http://localhost:4200
+# Backend API: http://localhost:8000
+# RTMP Stream: rtmp://localhost:1935/live
+# HLS Stream: http://localhost:8081/hls
+```
+
+### Option 2: Kubernetes (Production-ready)
+
+```bash
+# Build and push images to local registry
+./k8s/build-for-ctlptl.sh
+
+# Deploy to Kubernetes
+kubectl apply -k k8s/overlays/development
+
+# Check deployment status
+kubectl get pods -n media-analyzer
+
+# Access via port forwarding
+kubectl port-forward service/frontend -n media-analyzer 4200:80
+```
+
+## Architecture
+
+- **Django Backend**: Main API server with WebSocket support for real-time communication
+- **Celery Workers**: Distributed task processing for AI analysis (logo detection, visual analysis)
+- **PostgreSQL**: Primary database for application data and analysis results
+- **Redis**: Cache and message broker for Celery tasks
+- **Angular Frontend**: Single-page application with real-time video analysis overlays
+- **NGINX RTMP**: Stream ingestion server for OBS and other RTMP sources
+
+## Development
+
+The system supports both local development with hot reload and production deployment:
+
+- **Development**: Uses Angular dev server and Django development server
+- **Production**: Uses nginx for static files and optimized Docker images
+
+## Demo
+
+Stream video from OBS Studio to `rtmp://localhost:1935/live` and watch real-time AI analysis in the web dashboard with live object detection overlays.