🎥 Transform YouTube videos into intelligent, comprehensive summaries using advanced AI models.
Scriptify is a modern web application that generates detailed, structured summaries from YouTube videos using cutting-edge AI technology. Built with a robust DevOps pipeline featuring Docker containerization and Kubernetes orchestration.
- 🤖 AI-Powered Summarization: Advanced natural language processing for intelligent video summaries
- 📊 Multiple Summary Types: Comprehensive, Brief, Bullet Points, and Academic formats
- ⏱️ Automatic Timestamps: Generated timestamps for key moments in videos
- 🎯 Key Highlights: AI-extracted important points and insights
- 📱 Modern React Frontend: Built with Vite for fast, responsive user experience
- 🚀 FastAPI Backend: High-performance Python backend with async support
- 🐳 Docker Containerization: Consistent deployment across environments
- ⚙️ Kubernetes Orchestration: Production-ready container management
- 📈 Auto-scaling: Horizontal Pod Autoscaler (HPA) for traffic management
- 🔐 Secure Secrets Management: Kubernetes secrets for API key protection
- 🔄 GitLab CI/CD Pipeline: Automated build, test, and deployment
- 🔧 Docker: Container image building and development
- ☸️ Kubernetes: Production deployment and orchestration
- 🌐 Frontend: React + Vite + Nginx (containerized)
- ⚡ Backend: FastAPI + Python (containerized)
- 🔀 Ingress: Traffic routing and load balancing
- 📊 HPA: Automatic scaling based on resource usage
- 🔐 Secrets: Secure API key management
Git Push → GitLab CI → Single Build Job → Test → Kubernetes Deploy → Live App
↓ ↓ ↓ ↓ ↓ ↓
Code Sequential Backend+Frontend Image K8s Manifests Auto Port
Changes Execution Images Built Verify Applied Forwarding
Use the GitLab CI/CD pipeline for automatic deployment:
- Clone the Repository
git clone https://github.com/AyushChoudhary6/Scriptify.git
cd Scriptify- Setup GitLab Runner
# Start GitLab runner with Docker
docker compose -f gitlab-runner-docker-compose.yml up -d
# Register runner with your GitLab project
docker exec -it gitlab-runner gitlab-runner register \
--url https://gitlab.com \
--registration-token YOUR_TOKEN_HERE \
--executor docker \
--docker-image alpine:latest \
--description "Local Docker Runner" \
--tag-list "local" \
--docker-privileged=true \
--docker-volumes /var/run/docker.sock:/var/run/docker.sock- Configure API Keys Update your Kubernetes secrets file with your API keys:
# Edit k8s/secrets.yml with your actual API keys
kubectl apply -f k8s/secrets.yml- Deploy via Pipeline
git add .
git commit -m "Deploy Scriptify"
git push origin mainThe pipeline will automatically:
- 🔧 Build backend and frontend Docker images in sequence
- 🧪 Run comprehensive image verification tests
- 🚀 Deploy complete application to Kubernetes
- 🌐 Set up automatic port forwarding
- ✅ Make app accessible at localhost:3000
# Build images
docker build -t scriptify-backend:latest ./backend
docker build -t scriptify-frontend:latest ./frontend
# Run with Docker Compose
docker compose up -d
# Access at:
# Frontend: http://localhost:3000
# Backend: http://localhost:8000# Build Docker images
docker build -t scriptify-backend:latest ./backend
docker build -t scriptify-frontend:latest ./frontend
# Apply Kubernetes manifests
kubectl apply -f k8s/
# Port forward to access locally
kubectl port-forward service/frontend-service 3000:80
kubectl port-forward service/backend-service 8000:8000- 🔧 Build: Complete application build (Backend + Frontend in sequence)
- 🧪 Test: Validation of built images and application health
- 🚀 Deploy: Kubernetes deployment with automatic port forwarding
- ✅ Sequential Execution: Build → Test → Deploy (one after another)
- ✅ Single Job per Stage: No parallel executions, clean pipeline flow
- ✅ Local GitLab Runner: Custom runner with "local" tag
- ✅ Dependency Management: Each stage waits for the previous to complete
- ✅ Kubernetes Integration: Complete k8s manifest application
- ✅ Auto Port Forwarding: Immediate localhost access after deployment
The pipeline will automatically:
- 🔧 Build backend and frontend Docker images sequentially
- 🧪 Run comprehensive tests on built images
- 🚀 Deploy to Kubernetes with all components
- 🌐 Set up automatic port forwarding
- ✅ Make app accessible at localhost:3000
- 📦 Deployments: backend-deployment.yml, frontend-deployment.yml
- 🌐 Services: backend-service.yml, frontend-service.yml
- 🔐 Secrets: secrets.yml (API keys management)
- 🔀 Ingress: ingress.yml (traffic routing)
- 📈 HPA: hpa.yml (horizontal pod autoscaling)
- ⚛️ React 18: Modern component-based UI
- ⚡ Vite: Fast build tool and dev server
- 🎨 CSS3: Custom styling with gradients and animations
- 🐳 Nginx: Production web server in container
- 🐍 FastAPI: High-performance async Python framework
- 🤖 AI Integration: AssemblyAI + Gemini APIs
- 📦 Uvicorn: ASGI server for production
- 🐳 Docker: Containerized deployment
- 🐳 Docker: Container runtime and image building
- ☸️ Kubernetes: Container orchestration platform
- 🔄 GitLab CI/CD: Automated pipeline with local runner
- 📊 HPA: Kubernetes Horizontal Pod Autoscaler
- 🔐 Secrets Management: Kubernetes native secret storage
- 📈 Resource Monitoring: CPU/Memory usage tracking
- 🔄 Auto-scaling: Dynamic pod scaling based on load
- 🔍 Health Checks: Liveness and readiness probes
- 📊 Deployment Status: Real-time pod and service monitoring
- 🐳 Docker & Docker Compose: Container runtime
- ☸️ Kubernetes Cluster: Local (kind, minikube) or cloud cluster
- 🔄 GitLab Runner: For CI/CD pipeline execution
- 🔑 API Keys: AssemblyAI and Gemini API access
- Access the Application: http://localhost:3000
- Paste YouTube URL: Any valid YouTube video link
- Select Summary Type: Choose from 4 AI summary formats
- Get AI Summary: Receive intelligent video summary with timestamps
After successful deployment:
- 🌐 Frontend: http://localhost:3000
- 🔧 Backend API: http://localhost:8000
- 📖 API Documentation: http://localhost:8000/docs
- 🎥 YouTube Summarizer: Ready to use!
- Fork the repository
- Create a feature branch:
git checkout -b feature/amazing-feature - Commit changes:
git commit -m 'Add amazing feature' - Push to branch:
git push origin feature/amazing-feature - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
Built with ❤️ by Ayush Choudhary | Showcasing DevOps Excellence with Docker + Kubernetes