Add the architectuural diagram and updated the Readme

Author: Emmy-github-webdev

README.md

@@ -2,6 +2,15 @@
 
 The goal of this project is to build, configure, and automate the deployment of a simple serverless application on AWS. Created a health check endpoint that logs requests and stores them in a database, with a CI/CD pipeline to manage deployments for both staging and production environments, fully provisioned via Terraform and deployed automatically using GitHub Actions.
 
+## Prerequisites
+- An AWS account and IAM user with rights to create IAM, Lambda, API Gateway, DynamoDB, CloudWatch.
+- Local toolchain:
+  - Terraform v1.2+
+- GitHub repo secrets (Repository > Settings > Secrets):
+  - `AWS_ACCESS_KEY_ID`
+  - `AWS_SECRET_ACCESS_KEY`
+  - `AWS_REGION` (e.g. `us-east-1`)
+
 ## Architectural desig
 
 ### Core Components
@@ -14,6 +23,8 @@ The goal of this project is to build, configure, and automate the deployment of
 
 Each environment (staging, prod) is isolated by naming convention and Terraform variables.
 
+[Live Demo Link](https://www.loom.com/share/3b1a44b435724293b7be8953ed51ae27)
+
 ### Runtime Request Flow
 
 1. _Client_: sends a GET or POST request to:
@@ -60,6 +71,7 @@ https://<api-id>.execute-api.<region>.amazonaws.com/health
 | Terraform vars | `staging.tfvars`                | `prod.tfvars`                |
 | Lambda         | `staging-health-check-function` | `prod-health-check-function` |
 | DynamoDB       | `staging-requests-db`           | `prod-requests-db`           |
+| S3 Bucket  | `serverless-health-check-api/staging/tfstate` | `serverless-health-check-api/prod/tfstate`|
 | API Gateway    | `staging-health-check-api`      | `prod-health-check-api`      |
 | Approval       |  None                           |  Required                   |
 
@@ -80,50 +92,74 @@ Each Lambda function has one dedicated IAM role with:
     - No credentials committed to repository
 
 
-
-
-
-
-
-
-create hello lambda funtion using Python
-
+![](/svrhecheck.drawio.png)
+
+## Setup
+
+_Repository Structure_
+.
+├── .github
+│   └── workflows
+│       └── deploy.yml
+        ├── destroy.yml
+├── lambda
+│   └── lambda_function.py
+├── terraform
+│   ├── provider.tf
+│   ├── variables.tf
+│   ├── main.tf
+│   ├── iam.tf
+│   ├── lambda.tf
+│   ├── apigw.tf
+│   ├── outputs.tf
+│   └── staging.tfvars
+│   └── prod.tfvars
+|   └── backend-prod.tfvars
+|   └── backend-staging.tfvars
+└── README.md
+
+- Create the lambda function, run the python funtion locally using VS Code Run Button, import boto3, logging,json, os, uuid.
+- Resource names follows _env-resource-name_
+
+## How to trigger deployments
+
+_Manual for test_
+- terraform init -backend-config=backend-staging.tfvars for staging environment
+- terraform init -backend-config=backend-prod.tfvars for prod environment
+- terraform plan -backend-config=backend-staging.tfvars for staging environment
+- terraform plan -backend-config=backend-prod.tfvars for prod environment
+- terraform apply -var-file="staging.tfvars"
+- terraform apply -var-file="prod.tfvars"
+- terraform destroy -backend-config=backend-staging.tfvars for staging environment
+- terraform destroy -backend-config=backend-prod.tfvars for prod environment
+
+_GitHub Action CICD_
+### Staging
+1. Push to branch `staging` (or open a PR merging into `staging` and merge).
+2. GitHub Action will automatically:
+   - `terraform init`
+   - `terraform plan -var-file="staging.tfvars"`
+   - `terraform apply` (auto-approved)
+
+### Production
+1. Push to branch `main` (or merge PR into `main`).
+2. Workflow will run and wait for **manual approval** in the `production` environment (configure environment reviewers in Settings).
+3. Approve in GitHub UI → workflow will `apply` the `prod.tfvars` plan.
+
+### Trigger Destroy CICD pipeline
+Manually trigger the terraform destroy workflow in GitHub UI -> actions -> destroy.yaml
+
+## Testing the /health endpoint
+
+After deployment, Terraform outputs the API endpoint. You can fetch it:
+- From the Actions run logs (last step runs `terraform output -json`)
+
+Example curl (replace `<API_ENDPOINT>` with the `api_endpoint` output):
 ```
-# Hello lambda function
-def lambda_handler(event, context):
-    name = event.get("name", "World")
-    message = f"Hello, {name}!"
-
-    return {
-        "statusCode": 200,
-        "body": message
-    }
-
-# Run the funtion locally for testing
-if __name__ == "__main__":
-    test_event = {"name": "Emmanuel Ogah"} 
-    result = lambda_handler(test_event, None)
-    print(result)
-```
-
-Run the python funtion locally using VS Code Run Button
-- Click the “Run Python File” button in the top right corner.
+# GET
+curl "https://nrbefv9bcj.execute-api.us-east-1.amazonaws.com/health"
 
-```
 # Output
+{"status": "healthy", "message": "Request processed and saved.", "id": "0fb4fa88-64a1-440d-84b1-5a6d4354364b"}
 
-{'statusCode': 200, 'body': 'Hello, Emmanuel Ogah!'}"
-```
-
-- Create the terraform folder structure
-
-- Deploy with: terraform init then terraform apply -var-file="staging.tfvars" (or prod.tfvars)
-
-
-endpoint - https://nrbefv9bcj.execute-api.us-east-1.amazonaws.com/health
-
-
-
-terraform init -backend-config=backend-staging.tfvars for staging environment
-
-terraform init -backend-config=backend-prod.tfvars for prod environment
+```