Streamlit,  an open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science projects

the most common way of deploying a data science project before streamlit,

is by hosting it into a python web framework such as Django, fastapi and flask.

Then, create a front end application using javascript framework to deliver to the user.

This make the data science project become boring and tedious to share the results with the end users.

Streamlit is here to solve the deployment problem.

It allows the deployment of data science projects such as machine learning model, graph visualization and text analytic result easily available to end-user via creating a beautiful web application with just a few lines of codes

One of the main features of streamlit is it provides you an intuitive jupyter-notebook like environment where your code is updated live as you save your script

To begin with streamlit, Install the library by using pip install streamlit

Now, You can start streamlit simply use streamlit run app.py

to run the server and you can work with your project in few  seconds

Streamlit provides tons of widget to let you handle user interaction, and these widgets are easily used with 1 line of code. Each widget is directly linked to the python code and this reduces the enormous time use to create an API and link the widget

For example, a file uploader can be create simply using st.file_uploader

Or St.slider to create a sliding widget that can pick the number accordingly.

Deploying the streamlit is easy with streamlit sharing. We just need to paste our GitHub repo. Then, our web application is ready!

You can also pick your host of choice, such as Heroku and google cloud to host your independent streamlit project or in your domain

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