The "Content Generation" sample generative AI application demonstrates how to build end-to-end solutions with generative ai multimodals leveraging AWS services and AWS Generative AI CDK Constructs.
The key constructs used in this sample app are:
- Content Generation: Generate images from text using Amazon titan-image-generator-v1 or stability.stable-diffusion-xl model.
By wrapping the complexity of orchestrating services like AWS Lambda, Amazon AppSync, and Amazon Bedrock, the Generative AI Constructs library enables you to quickly build custom generative AI applications following AWS best practices.
Here is the architecture diagram of the sample application:
This Content Generation codebase is organized into folders containing the frontend and backend infrastructure code. The frontend client app is built with Streamlit and is located in the client_app folder. The backend code lives in bin/content-generation.ts and uses the AWS CDK resources defined in the lib folder.
The key folders are:
samples/content-generation
├── client_app # Frontend using Python Streamlit
│ │
│ ├── Home.ts # Sample app entry point
│ ├── assets/ # Static files
│ ├── common/ # Utility classes
│ ├── graphql/ # GraphQL statements and client
│ └── pages/ # Streamlit pages
│
├── bin
│ └── content-generation.ts # Backend - CDK app
├── lib # CDK Stacks
│ ├── content-generation-stack.ts # VPC resources
│
└── generative-ai-cdk-constructs@0.0.58.jsii.tgz # Local copy of generative-ai CDK constructs
To deploy this Sample application, follow these steps to set up the required tools and configure your AWS environment:
- An AWS account. We recommend you deploy this solution in a new account.
- AWS CLI: configure your credentials
aws configure --profile [your-profile]
AWS Access Key ID [None]: xxxxxx
AWS Secret Access Key [None]:yyyyyyyyyy
Default region name [None]: us-east-1
Default output format [None]: json
- Node.js: v18.12.1
- AWS CDK: 2.68.0
- jq: jq-1.6
This project is built using the AWS Cloud Development Kit (CDK). See Getting Started With the AWS CDK for additional details and prerequisites.
-
Clone this repository.
git clone <this>
-
Enter the code sample backend directory.
cd samples/content-generation -
Install packages
npm install
-
Boostrap AWS CDK resources on the AWS account.
cdk bootstrap aws://ACCOUNT_ID/REGION
-
Enable Access to Amazon Bedrock Models
You must explicitly enable access to models before they can be used with the Amazon Bedrock service. Please follow these steps in the Amazon Bedrock User Guide to enable access to the models (at minimum,
Amazon titan-image-generator-v1):.
- Deploy the sample in your account.
$ cdk deploy --all
The command above will deploy the stack in your account.
-
Configure client_app
cd client_app python -m venv venv source venv/bin/activate pip install -r requirements.txt
-
Still within the /client_app directory, create an
.envfile with the following content or mutate the.env-example. Replace the property values with the values retrieved from the stack outputs/console.
COGNITO_DOMAIN="<CognitoDomain>"
REGION="<Region>"
USER_POOL_ID="<UserPoolId>"
CLIENT_ID="<ClientId>"
CLIENT_SECRET="COGNITO_CLIENT_SECRET"
IDENTITY_POOL_ID="<IdentityPoolId>"
APP_URI="http://localhost:8501/"
AUTHENTICATED_ROLE_ARN="<AuthenticatedRoleArn>"
GRAPHQL_ENDPOINT = "<GraphQLEndpoint>"
S3_PROCESSED_BUCKET = "<generatedAssetsBucket>"
Note: The COGNITO_CLIENT_SECRET is a secret value that can be retrieved from the AWS Console. Go to the Amazon Cognito page in the AWS console, then select the created user pool. Under App integration, select App client settings. Then, select Show Details and copy the value of the App client secret.
- Run client_app
streamlit run Home.py
-
Create a user in the Cognito user pool. Go to the Amazon Cognito page in the AWS console, then select the created user pool. Under users, select Create user and fill in the form
-
Access the webapp (either locally or through the Amplify hosted domain) and sign in using the user credentials you just created
-
Go to Image generation page and enter a text to generate the image. The generated imaged will be displayed on the same page.
Subscription
subscription MySubscription {
updateGenerateImageStatus(jobid: "123") {
filename
image_path
input_text
jobid
message
status
}
}
Mutation
mutation MyMutation {
updateGenerateImageStatus {
jobid
filename
status
image_path
input_text
message
}
generateImage(imageInput: {
input_text: "bGl6YXJkIGluIHNub3c=",
jobid: "123",
model_config: {modelId: "amazon.titan-image-generator-v1",
provider: "Bedrock"},
negative_prompts: ""}) {
filename
image_path
input_text
jobid
message
status
}
}
Do not forget to delete the stack to avoid unexpected charges.
First make sure to remove all data from the Amazon Simple Storage Service (Amazon S3) Buckets. Then:
$ cdk destroy --allThen in the AWS Console delete the S3 buckets.
The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.
This solution collects anonymous operational metrics to help AWS improve the quality and features of the solution. Data collection is subject to the AWS Privacy Policy (https://aws.amazon.com/privacy/). To opt out of this feature, simply remove the tag(s) starting with “uksb-” or “SO” from the description(s) in any CloudFormation templates or CDK TemplateOptions.