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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 2. Uploading, Downloading, and Manipulating Data FREE CHAPTER 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 with Streamlit Community Cloud

Now that all of our necessary files are in the GitHub repository, we have almost all that we need to deploy our application. You can use the following list of steps to deploy our application:

  1. When we deploy to Streamlit Community Cloud, Streamlit uses its own servers to host the app. Because of this, we need to explicitly tell Streamlit which Python libraries are required for our app to run. The following code installs a very helpful library called pipreqs and creates a requirements.txt file in the format we need for Streamlit:
    pip install pipreqs
    pipreqs .
    
  2. When we look at our requirements.txt file, we can see that pipreqs looked through all of our Python files, checked what we imported and used, and created a file that Streamlit can use to install the exact same versions of our libraries to prevent errors:

Figure 5.3: Requirements.txt

  1. We have a new file, so we need to also add it to...
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