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How to Deploy Your Streamlit App on Streamlit Sharing

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In this article, you will see how you can deploy your app on streamlit sharing. Before you read the article further, make sure you have an invite to streamlit sharing.

To deploy your app on Streamlit Sharing you need to have a GitHub account. Because you will have to upload all your codes there and link that to your streamlit sharing account.

Once you have uploaded your code to your GitHub. Go to this website: https://share.streamlit.io/ and click on Sign in with Github.

Once you log in and give access to streamlit to your GitHub account you can see the below page. In your case, there will be no apps since you just created your account.

Before you click on the New App button, there are a couple of these things you need to add to your GitHub. When you want to deploy the app, it should have a starting point and all the libraries installed so that it will be deployed properly.

For this purpose, you have to add two files to your Github repository. One is the requirements.txt and the other is the packages.txt file.

To generate a requirements file you can install pipreqs.

pip install pipreqs

Then you can use it like this

pipreqs /home/project/location

The contents of the packages.txt file are in the image below. To get an example file, click here

Once this is setup into your Github, now you can click on the New App button on your Streamlit Sharing account. The below image will come up. Choose your repo that you want to deploy from the dropdown. Choose the respective branch. Then you have to select the file that runs the code and the Click Deploy.

If everything is correct in the requirements.txt file then your app will be deployed and you can directly share the link with others.

I have created 3 apps till now on streamlit. You are free to go through them and contribute to it on Github.

Here are the links

Data Visualisation using Streamlit

Image Processing using Streamlit

Predict Air Pollution Index

Hope this was helpful

Thanks !

Peace

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Aniket Wattamwar
Aniket Wattamwar

Written by Aniket Wattamwar

Machine Learning | AWS | Designer

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