Cross-Origin Resource Sharing for AI Web Applications with Spaces

Question

Pulumi · Accepted Answer

Cross-Origin Resource Sharing, or CORS, is a security feature that allows or restricts web applications from making requests to a domain different from the one that served the web application. When you're building web applications that interact with services from different domains, you will likely need to configure CORS to allow these cross-domain requests.

For AI web applications, an expected scenario might be a web app hosted on one service (like AWS S3) that needs to make API calls to an AI service hosted somewhere else (like AWS SageMaker, Google AI Platform, or Azure Machine Learning services).

Here's a program, written in Python using Pulumi, that sets up an AWS S3 bucket to host a web application with the necessary CORS policy to interact with an AI service. The AI service part isn't directly configurable regarding CORS, but you would set the corresponding service to respond correctly to CORS preflight requests.

```python
import pulumi
import pulumi_aws as aws

# Create an AWS S3 bucket that will host the web application files.
# Make sure to replace 'my-ai-web-app-bucket' with a unique bucket name.
web_app_bucket = aws.s3.Bucket('my-ai-web-app-bucket',
    website={
        'index_document': 'index.html',  # Assuming index.html is your entry file for the web app
    },
)

# Define CORS rules for your S3 bucket.
# Here, we are allowing GET requests from any origin, but be aware that
# in a production environment, you should limit this to specific origins for security.
cors_rules = [{
    'allowedHeaders': ['*'],
    'allowedMethods': ['GET'],
    'allowedOrigins': ['*'],  # Update this with your actual domain when in production
    'exposeHeaders': ['GET'],
    'maxAgeSeconds': 3000
}]

# Attach the CORS policy to the S3 bucket.
bucket_cors_policy = aws.s3.BucketCors('my-ai-web-app-bucket-cors',
    bucket=web_app_bucket.id,
    cors_rules=cors_rules
)

# Export the website URL. 
pulumi.export('web_app_bucket_url', web_app_bucket.website_endpoint)

# Reminder: In a real-world scenario, you also need to configure your
# AI service to handle the OPTIONS preflight requests that come as part of CORS.
# That configuration is service-specific and not covered by an S3 bucket policy,
# hence it's not included in this example.
```
In the code above:
- We create an S3 bucket to host the web application, with an index document, which is commonly `index.html`.
- We set up a CORS policy on the bucket allowing `GET` requests from any origin. In a production environment, `allowedOrigins` should list the exact origins (domains) from which requests are allowed (to enhance security).
- We export the S3 bucket website endpoint URL, which you can use to access your web app.

Please note that CORS is a client-side enforced security measure, and configuring an S3 bucket's CORS rules as shown above tells the browser that it's acceptable to make requests to this bucket from any specified origin. The configuration of the AI service's CORS policy is service-specific and would have to be done according to that service's documentation or configuration settings.

To create resources in AWS using Pulumi, ensure you have set up AWS credentials and the AWS Pulumi provider. Then, to deploy your infrastructure, you'll use the Pulumi CLI to run `pulumi up` within the directory of your Pulumi program.