The contents of this repository provide a example Flask application that passes user-provided data to Amazon Comprehend and visualizes the results using Plotly. The steps below also demonstrate how to deploy this application to Amazon Elastic Beanstalk.
NOTE: This repository is designed to supplement this APG library pattern: Visualize AI/ML Model Results Using Flask and AWS Elastic Beanstalk.
- Sign in to an AWS account with administrator privileges.
- Clone this repository into your working machine.
- cd into the repository and install the requirements using
pip install -r requirements.txt - Test the Flask application locally, using
python application.pyThis should return information about serving the application, with an output similar to the below -
* Serving Flask app "application" (lazy loading)
* Environment: production
WARNING: This is a development server. Do not use it in a production deployment.
Use a production WSGI server instead.
* Debug mode: on
* Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)
You can now access the app using the URL in your output.
- Initialize your AWS Elastic Beanstalk application using
eb init -p python-3.6 <app-name> --region <region_name> - Create and deploy the Elastic Beanstalk environment using
eb create <env-name> - Make sure to add Amazon Comprehend access to the EC2 instance created using AWS Elastic Beanstalk. You can use either
the AWS console or use the command below.
aws iam attach-role-policy --policy-arn arn:aws:iam::aws:policy/ComprehendFullAccess --role-name aws-elasticbeanstalk-ec2-role
This will begin the provisioning all of the necessary architectural components within AWS.
This process can take about 5 minutes to complete. Once your application is successfully deployed,
the deployment URL should be returned in the command line, or you can run eb open to open
your application in the browser.
See CONTRIBUTING for more information.
This library is licensed under the MIT-0 License. See the LICENSE file.