amazon-bedrock-with-builder-and-command-patterns VS streamlit

The second service is what’s going to make our application come alive and give it the AI functionality we need and that service is AWS Bedrock which is their new generative AI service launched in 2023. AWS Bedrock offers multiple models that you can choose from depending on the task you’d like to carry out but for us, we’re going to be making use of Meta’s Llama V2 model, more specifically meta.llama2-70b-chat-v1.

Amazon Bedrock

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon. Each model is accessible through a common API which implements a broad set of features to help build generative AI applications with security, privacy, and responsible AI in mind.

For those keeping track, Amazon Bedrock became generally available in September of 2023. My team had access to a preview, so when the AWS Comprehend entity analysis did not lend itself well to my use case; and I didn't feel like training a model, I started to get familiar with Bedrock. The following post is a follow-on to the Community article above and fleshes out a few details that will help those newer to Amazon Bedrock navigate the product.

In this blog you will learn how to use Amazon Cognito credentials and IAM Roles to invoke Amazon Bedrock API in a react-based application with JavaScript and the CloudScape design system. You will deploy all the resources and host the app using AWS Amplify.

Working with the AWS Bedrock API is an exhilarating experience! I came across an interesting business case where I needed to develop an AI MVP. The MVP generates JSON data based on a prompt and utilizes the anthropic.claude-v2:1 model in AWS Bedrock.

I decided to use Streamlit to build the UI as it is a popular and fitting choice. Streamlit is an open-source Python library used for building interactive web applications specially for AI and data applications. Since the application code is written only in Python, it is easy to learn and build with.

Streamlit (https://streamlit.io/)

Does it need to be live (i.e when database or underlying spreadsheet updates does it need to be reflected in real time on the dashboard) or are you ok with static display.

Live updating data is a pain I've messed around using javascript to force refresh html iframes on a timer. But I was never really satisfied with this. I've heard you can do things with websockets but that is starting to get too complicated for me (I'm not a programmer).

For static stuff one of the data scientists in my org pointed me to Streamlit (https://streamlit.io/) it's a python package I found very easy to use. Can easily combine SQL with CSV imports and display them all on one dashboard. Can use forms toggle butotns etc to control the display.

Note that there are many tools that make this easier/simpler to prototype, including chainlit, streamlit, etc… The backend API we built is amenable to interacting with them as well.

2.-Go to https://streamlit.io, log in, and create a new app from your GitHub repository.

Developed using Langchain and Streamlit technologies for enhanced performance.

Hello,

Have you ever seen the https://streamlit.io/ ? I think this is what you are looking for.

While building dashboards in Streamlit, I found myself really missing Buefy's (Bulma) modern web components.

Specially due to the inability to add new values to Streamlit's multiselect [1], some missing controls like a polished image carousel [2] or a highly customizable data table.

Long story short, we put together streamfy (Streamlit + Buefy) as an MIT licensed project in GitHub to bring Buefy to Streamlit.

Demo: https://streamfy.streamlit.app

All the form components are implemented, missing half of other non-form UX components. There is plenty of room for PRs, testing, feedback, documentation, example, etc.

Please send issues and contributions to GitHub project [3] and general feedback to X / Twitter [4]

Thanks!

[1] https://github.com/streamlit/streamlit/issues/5348

Here's your savior, let's welcome Streamlit.

Looks cool. How do you see this differing from streamlit? https://streamlit.io/