Flask App with AWS Deployment Pipeline

Automated cloud deployment demonstrating containerization, infrastructure as code, and CI/CD best practices.

🎯 What This Project Demonstrates

A Flask web application with complete automation from code to cloud:

• ✅ Dockerized Flask app with Prometheus metrics instrumentation
• ✅ AWS infrastructure provisioned via Terraform (VPC, EC2, ECR, IAM)
• ✅ Automated CI/CD pipeline using GitHub Actions
• ✅ Monitoring with Prometheus integration

🏗️ Architecture

GitHub Push → GitHub Actions → Build Docker Image → Push to ECR → Deploy to EC2

AWS Resources Created:

• VPC with public subnet and Internet Gateway
• EC2 instance (t3.micro) with Docker pre-installed
• ECR repository with image scanning enabled
• IAM roles and security groups

🚀 Quick Start

Local Development:

# With Docker
docker build -t nordhealth-app:latest .
docker run --rm -p 5000:5000 nordhealth-app:latest

Deploy Infrastructure:

cd aws-resources-terraform
terraform init
terraform apply

Access:

• App: http://localhost:5000/
• Metrics: http://localhost:5000/metrics

📊 Application Endpoints

• GET / - Hello World with request timing
• GET /metrics - Prometheus metrics (request duration histogram & gauge)

🔄 CI/CD Pipeline

GitHub Actions workflow automatically:

1. Builds Docker image on push to main
2. Pushes to AWS ECR with Git SHA tag
3. SSH into EC2 and deploys latest container

📸 Screenshots

Application endpoint with request timing

Prometheus metrics endpoint

Infrastructure provisioned via Terraform

🛠️ Tech Stack

Application: Python 3.12, Flask 3.1.2, prometheus_client
Containerization: Docker, Docker Compose
Cloud & IaC: AWS (EC2, ECR, VPC, IAM), Terraform
CI/CD: GitHub Actions

📦 Project Structure

├── main.py                      # Flask app with Prometheus metrics
├── Dockerfile                   # Alpine-based container image
├── .github/workflows/deploy.yml # CI/CD pipeline
└── aws-resources-terraform/     # Complete AWS infrastructure
    ├── main.tf
    ├── variables.tf
    └── outputs.tf

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Built as a demonstration of modern DevOps practices and cloud-native development.