1. This assumes you have saved a trained model as a pickle file. If you are using Tfidf, then note:

   *Notes: The stop_words_ attribute can get large and increase the model size when pickling. This attribute is provided only for introspection and can be safely removed using delattr or set to None before pickling.

    vectorizer = TfidfVectorizer()
    X = vectorizer.fit_transform(corpus)
    vectorizer.stop_words_ = None
   
    import pickle
   
    pickle.dump( vectorizer, open( "tfidf_small.pkl", "wb" ) )  # save tfidf as pickle
   
    tfidf = pickle.load( open( "tfidf_small.pkl", "rb" ) ) # open pickle file
   

2. install streamlit

    $ pip install streamlit
    $ streamlit hello
   

   *Note: you might need to install

    $ pip install --upgrade protobuf
   

3. create new folder

4. create first_app.py file and open with editor

5. follow https://docs.streamlit.io/en/stable/getting_started.html

6. Next, import Streamlit. To make things easier later, we're also importing numpy and pandas for working with sample data.

    import streamlit as st
    import numpy as np
    import pandas as pd
   

7. Run your app. A new tab will open in your default browser. It’ll be blank for now. That’s OK.

    $ streamlit run first_app.py
   

8. You can kill the app at any time by typing Ctrl+c in the terminal.

9. Create first app: https://docs.streamlit.io/en/stable/getting_started.html

10. Put on GitHub https://docs.streamlit.io/en/stable/deploy_streamlit_app.html *Make sure it’s in a public folder and that you have a requirements.txt file

*If you need to generate a requirements file, try using pipreqs

    pip install pipreqs
    pipreqs /home/project/location

running $ pipreqs in terminal in the active directory for the app will create a requirements.txt file that only has the libraries you pip installed.

NOTE

You should only include packages in requirements.txt that are not distributed with a standard Python installation (i.e. only packages that need to be installed with pip or conda). If you include any of these modules from base Python in your requirements.txt file, you will get an error when you try to deploy.

If you have requirements for apt-get, add them to packages.txt, one package name per line. See our streamlit-apps demo repo for an example packages.txt file.

11. Log in to share.streamlit.io The first thing you’ll see is a button to login with GitHub. Click on the button to login with the primary email associated with your GitHub account.

IMPORTANT

If the email you originally signed-up with isn’t the primary email associated with your GitHub account, just reply to your invite email telling us your primary Github email so we can grant access to the correct account.

12. Deploy your app Click “New app”, then fill in your repo, branch, and file path, and click “Deploy”. Your app will take a minute or two to deploy and then you’ll be ready to share!

If your app has a lot of dependencies it may take some time to deploy the first time. But after that, any change that does not touch your dependencies should show up immediately.

That’s it — you’re done! Your app can be found at:

https://share.streamlit.io/[user name]/[repo name]/[branch name]/[app path]