Open source Python library to efficiently build generative AI & data science apps.

Streamlit is an open-source Python library that give you an intuitive and highly efficient way to build interactive, custom web apps that are perfect for AI, machine learning, and data science projects. Streamlit's goal is to simplify and accelerate the process of turning data scripts into shareable web apps.


Open source Python library to quickly build generative AI & data science apps.

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Getting Started with Streamlit

The platform offers a comprehensive guide to help you get started. This guide provides an overview of how Streamlit works, instructions for installing it on your preferred operating system, and tips for creating your first Streamlit application.

Get started - Streamlit Docs

Streamlit Installation

Streamlit can be installed on Windows, macOS, and Linux systems. The installation guide below walks you through the process:

Installation - Streamlit Docs

Create a Streamlit App

This section of the documentation explains how to create your first streamlit app:

Create an app - Streamlit Docs

Generative AI Apps

Streamlit has become the platform of choice for thousands of developers building generative AI applications. It allows you to create, deploy, and share Large Language Model (LLM)-powered apps at a speed only limited by the computing power of your AI models.

Whether you're developing a ChatGPT-powered chatbot, a LangChain search tool, or a file Q&A application with Anthropic, Streamlit offers the perfect playground for your LLM code.

Explore the App Gallery to see what other Streamlit users have created:

In summary, Streamlit is a highly dynamic and extremely user-friendly tool that allows both novice and experienced developers to create and deploy custom web applications for data science and machine learning.

If you're interested in creating simple data visualizations, complex interactive dashboards, or generative AI-enabled apps, Streamlit is an extremely useful tools to help bring your projects to life.

More Streamlit Tutorials

MLQ Academy: Building a GPT-3 Enabled App with Streamlit
In this video tutorial, we’ll be creating a simple web app using Streamlit that allows users to upload a PDF, input a YouTube video, or retrieve an earnings call transcript, and compute the embeddings of the document or video.
GPT-4 & Streamlit - Building a Frontend for an AI/ML Tutor: MLQ Academy
In this video tutorial, we’ll build a simple frontend for an AI/ML tutor using GPT-4, Streamlit, and Pinecone.


Please note this pages serves informational purposes only and does not constitute an endorsement of any AI tool. Some of the company descriptions are assisted by our GPT-4 research assistant and is provided without any expressed or implied warranties.

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