mapa-streamlit VS streamlit

Note, that it is based on streamlit, using mapa and here is the source code of the app.

The aim of this article is to guide you step by step in creating an application that interacts with generative AI models. We will start by setting up a Python development environment, following best practices. Then, we will explain the basics of interacting with generative AI model as well as the fundamental principles of the Langchain framework. Once these foundations are laid, we will implement a simple chatbot using the OpenAI API and Langchain. Finally, we will integrate this chatbot into a graphical interface with Streamlit, to offer a smooth and interactive user experience.

Streamlit on GitHub: Explore the source code and contribute at github.com/streamlit/streamlit.

On the other hand, pure Python libraries like Dash and Streamlit can be great for small projects, but they are limited to a specific use case and don't have the features and performance to build a full web app. As your app grows in features and complexity, you may find yourself hitting the limits of the framework, at which point you either have to limit your idea to fit the framework, or scrap your project and rebuild it using a "real web framework".

Where to check it out: https://github.com/streamlit/streamlit

We are now all set to deploy our app. First upload the codebase in a GitHub repository. Then click here to sign in to your streamlit account and go to My Apps section:

Now, let’s build a real-time application using Streamlit that connects to the WebSocket server and receives live updates.

Deployment and monitoring Once the tests are successful, you can pull the necessary artifacts (commonly a docker image) to the production server and deploy the updates. You can also build a monitoring dashboard using your favorite tools (Grafana, Streamlit, etc.) to gauge application metrics or employ model monitoring for deployed machine learning models. Use the following commands to unpack the code from your remote registry to your deployment server and deploy it.

Real-time Messaging Application Example Let’s create a basic real-time messaging application using FastAPI for the backend and WebSockets for real-time communication. We’ll use Streamlit for the frontend to display messages.

Regarding the implementation, we have chosen Streamlit as the base of our operations, helping us tie up the outputs generated by the API calls to a chat interface. Unfortunately, the larger model asks for a higher VRAM, which I have chosen to fulfill using Tune Studio's API Calls.