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Streamlit for Data Science

You're reading from   Streamlit for Data Science Create interactive data apps in Python

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Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781803248226
Length 300 pages
Edition 2nd Edition
Languages
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Author (1):
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Tyler Richards Tyler Richards
Author Profile Icon Tyler Richards
Tyler Richards
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Table of Contents (15) Chapters Close

Preface 1. An Introduction to Streamlit FREE CHAPTER 2. Uploading, Downloading, and Manipulating Data 3. Data Visualization 4. Machine Learning and AI with Streamlit 5. Deploying Streamlit with Streamlit Community Cloud 6. Beautifying Streamlit Apps 7. Exploring Streamlit Components 8. Deploying Streamlit Apps with Hugging Face and Heroku 9. Connecting to Databases 10. Improving Job Applications with Streamlit 11. The Data Project – Prototyping Projects in Streamlit 12. Streamlit Power Users 13. Other Books You May Enjoy
14. Index

Deploying Streamlit with Hugging Face

Hugging Face offers an entire suite of products focused on machine learning and is especially used by machine learning engineers and folks in the natural language processing space. It gives developers the ability to easily use pre-trained models through its transformers library (which we already used!) but also create products that let developers host their own models, datasets, and even their own data apps through a product called Hugging Face Spaces. You can think of a Space as a place to deploy an app on the Hugging Face infrastructure, and it is quite easy to get started.

For this chapter, we’ll deploy the same Hugging Face app that we created in Chapter 4. We can deploy any of our Streamlit apps on Hugging Face, but I thought it would be more fitting to deploy that one!

To start, we need to go to https://huggingface.co/spaces and click the button that says Create new Space.

Figure 8.1: Hugging Face login

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