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You should see a lovely visualization like this:įeel free to edit the app however you want. It'll automatically open a new browser tab and load your app. To build your app into a Docker image and launch a new container running your app at. With this setup, time to test your app! You can do that with: make run-container
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Navigate into your new streamlit-project directory and run: pip install -r requirements.txtĪs you might expect, this’ll install packages you’ll need to run this app. You’re going to need to clone the deployment-ready Streamlit project template from GitHub. It’s a good idea to set up and activate a Python virtual environment for the project too. You won’t need to interact with Docker to follow along with this post, but you will need it running! You’ll also need to have a valid version of Python 3.6 or greater, too. When this is done, make sure you have Docker installed and running. You can sign up for an account here (it's not an affiliate link, don't worry!): You’ll be deploying your app with Google App Engine, so this means you’ll need to both register for a Google Cloud account (there’s $300 of credit available on signup too!) and then install their gcloud command line tool. To get started, you’ll need to make sure you have few things set up. However, feel free to create your own app and deploy that instead (the template you’ll be using won’t need to be changed!). It’s going to be using one of the ‘standard’ demo apps provided by Streamlit: a visualization of Uber Pickups in New York City by time of day. This post aims to highlight how simple it is to deploy a Streamlit app as a live, public-facing website. While the docs for Streamlit are pretty good for getting started, there’s not a huge amount out there on deploying your apps.
All in all, these features conspire to make Streamlit one of the most productive, modern data visualization tools out there at the moment. You can embed your matplotlib charts alongside your dash plots to your heart's content, and then containerize it all ready for deployment in moments. As they put it: it's designed to be 'sprinkled' onto your code.įinally, Streamlit is also pretty much painless to integrate with existing data visualization tools, and is super easy to deploy to boot - it plays nicely with modern deployment tools and technologies. In the same way that Fire makes it incredibly easy to create CLIs for your existing code, Streamlit makes it ludicrously easy to create interactive apps from existing code. It does some clever tricks under the hood to make this work, but the end effect is the same: you can quickly and simply write apps with complex behaviour with very little change to your underlying code. The same concept is applied pretty much the same way to all of their interactive components. Whenever the Click Me button is pressed, the block beneath it will be executed.
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For example, say you want to run some code on the click of a button, you’d capture this in Streamlit with: import streamlit as st if st.button("Click Me"): print("Clicked") There’s no framework-specific design patterns/callbacks needed to manage events in Streamlit. This might sound dull, but it’s kind of a big deal in this case. This minimalism lends itself to a second key point: event management is a breeze. Altogether this simplicity makes it remarkably quick and easy to get an app built in (pretty much) seconds. There’s no boilerplate, and pretty much no framework-specific concepts you need to keep in mind. Well firstly, it is quite literally one of the most straightforward tools you’re likely to come across.
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