☁️ Cloud Fun Facts Generator

A fully serverless, cloud-native web application that generates witty AI-powered cloud computing facts — built on AWS with Generative AI at its core.

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📌 Overview

The Cloud Fun Facts Generator lets users instantly generate AI-crafted, witty cloud computing facts through a clean, responsive frontend hosted on AWS Amplify.

How it works — one click, five services:

1. The user clicks Generate Fun Fact on the frontend
2. The frontend sends a request to Amazon API Gateway (HTTP API)
3. API Gateway triggers an AWS Lambda function
4. Lambda fetches a cloud fact from Amazon DynamoDB
5. Amazon Bedrock (Nova Lite model) rewrites it into a witty AI response
6. The generated fact is returned and displayed on screen

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🏗️ Architecture Diagram

🌐 Final Result

This is what your project will look like once built:

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🎥 Demo

▶️ Watch Demo Video

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🛠️ AWS Services Used

Service	Role in the Project
AWS Lambda	Serverless backend — processes requests and orchestrates responses
Amazon API Gateway	Exposes a RESTful HTTP API endpoint for the frontend
Amazon DynamoDB	Stores a library of cloud computing facts
Amazon Bedrock	Generates witty, AI-powered rewordings using the Nova Lite model
AWS Amplify	Hosts and deploys the frontend application
AWS IAM	Manages permissions and access control across services

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✨ Features

• ⚡ Fully serverless — no servers to manage, scales automatically
• 🤖 Generative AI integration via Amazon Bedrock (Nova Lite)
• 🗄️ DynamoDB-backed fact storage with Lambda retrieval
• 🌐 HTTP API Gateway simple cheaper than REST API
• 🖥️ Responsive frontend hosted on AWS Amplify
• 🔒 Secure IAM permission model across all services
• 💸 Designed to stay within AWS Free Tier limits

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⚙️ Setup & Deployment

Deployment Steps

1. Create the DynamoDB Table

• Table name: CloudFacts
• Partition key: factId (String)
• Populate the table with cloud computing fact records

2. Create the Lambda Function

• Runtime: Python 3.x
• Attach an IAM role with permissions for DynamoDB and Bedrock
• Deploy the function from lambda/lambda_function.py

3. Set Up API Gateway (HTTP API)

• Create a new HTTP API
• Add a GET /funfact route
• Integrate the route with the Lambda function
• Enable CORS for the Amplify frontend origin

4. Configure Amazon Bedrock

• Enable model access in the AWS Console by submiting onetime usecase and provide IAM permissions
• Select and enable the Amazon Nova Lite model
• Update the Lambda function with the correct model ID and inference parameters

5. Deploy the Frontend via AWS Amplify

• Upload frontend/index.html to a new Amplify app
• Update the API endpoint URL in the frontend code
• Trigger a deployment and verify the live URL

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📁 Project Structure

CloudFunFactGenerator/
│
├── lambda/
│   └── lambda_function.py       # Core backend logic
│
├── frontend/
│   └── index.html               # Frontend UI
│
├── architecture/
│   └── architecture-diagram.png # Architecture diagram
│
└── README.md

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🚧 Challenges & How They Were Solved

Challenge	Resolution
CORS errors between Amplify and API Gateway	Configured CORS headers correctly on the HTTP API
IAM permission issues for DynamoDB and Bedrock	Scoped IAM role with least-privilege policies
Bedrock model deprecation mid-project	Migrated from legacy model to Amazon Nova Lite
API Gateway route not resolving	Debugged route configuration and redeployed the stage
Bedrock inference compatibility errors	Updated invocation payload to match Nova Lite's expected format
Frontend deployment failures on Amplify	Resolved by correcting build settings and re-uploading the app

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📚 Key Learnings

• Designing and wiring together a multi-service serverless architecture on AWS
• Integrating Generative AI into a real application using Amazon Bedrock
• Working with HTTP APIs in API Gateway and managing CORS in serverless deployments
• Writing Lambda functions that interface with both DynamoDB and Bedrock
• Structuring IAM roles and policies for least-privilege access
• Diagnosing and resolving real-world cloud deployment issues end-to-end

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🚀 Future Improvements

• Add user authentication with Amazon Cognito
• Automate infrastructure provisioning with Terraform
• Set up a CI/CD pipeline using GitHub Actions
• Add multiple AI-generated fact categories (e.g., DevOps, Security, Networking)
• Store and display a history of previously generated facts
• Build an analytics dashboard using CloudWatch metrics
• Improve the frontend UI/UX with a modern component library

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💰 Cost Considerations

This project was designed to stay within AWS Free Tier limits wherever possible:

Service	Cost Profile
AWS Lambda	Free Tier eligible (1M requests/month free)
Amazon API Gateway	Low-cost HTTP API pricing
Amazon DynamoDB	Free Tier eligible (25 GB storage free)
AWS Amplify	Free Tier eligible for hosting
Amazon Bedrock	Pay-per-use inference — minimal cost for testing

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🧹 Cleanup Steps

To avoid AWS charges, delete the following resources after testing:

• AWS Amplify App
• API Gateway HTTP API (FunfactsAPI)
• Lambda Function (CloudFunFacts)
• DynamoDB Table (CloudFacts)
• CloudWatch Log Groups (/aws/lambda/CloudFunFacts)
• Optional: IAM roles created for the project

Note: Amazon Bedrock is usage-based, so avoid unnecessary model invocations.

👩‍💻 Author

Deepigha Japamony

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