Visualizing output from artificial intelligence and machine learning (AI/ML) services often requires complex API calls that must be customized by your developers and engineers. This can be a drawback if your analysts want to quickly explore a new dataset.
You can enhance the accessibility of your services and provide a more interactive form of data analysis by using a web-based user interface (UI) that enables users to upload their own data and visualize the model results in a dashboard.
This pattern uses Flask and Plotly to integrate Amazon Comprehend with a custom web application and visualize sentiments and entities from user-provided data. The pattern also provides the steps to deploy an application by using AWS Elastic Beanstalk. You can adapt the application by using Amazon Web Services (AWS) AI services or with a custom trained model hosted on an endpoint (for example, an Amazon SageMaker endpoint).
