Summary:
Today, a lot more ML, data science & NLP apps are being developed by companies or being performed as a project from data science, ML enthusiasts. To make the apps visible or functionality of the app visible, it has to come to deployment phase. There's GUI required so that normal people can make use of it.
What actually happens?
while designing such apps, they have to take assistance of flask or react and do heavy coding and then make it usable. even when they are not satisfied, they have make lot more changes in code. Its total mess of all files. Like we have to make one file and link it with other GUI file.
solution:
So ,using streamlit library can be best option in which we can develop both the GUI and whole functionality code. It has lot of functionalities and purely supports python.
Session outline:
1] What is streamlit?
2] benefits?
3] installation
4]Hands-on for "NLP task visualizer app"
time: around 40-50 minutes
Pre-requisites:
1] Basic python knowledge
2] laptop with python 3.6 or higher
3] internet connection
talk date : anytime according to community
Empowering ML webapps with Streamlit.pdf
Summary:
Today, a lot more ML, data science & NLP apps are being developed by companies or being performed as a project from data science, ML enthusiasts. To make the apps visible or functionality of the app visible, it has to come to deployment phase. There's GUI required so that normal people can make use of it.
What actually happens?
while designing such apps, they have to take assistance of flask or react and do heavy coding and then make it usable. even when they are not satisfied, they have make lot more changes in code. Its total mess of all files. Like we have to make one file and link it with other GUI file.
solution:
So ,using streamlit library can be best option in which we can develop both the GUI and whole functionality code. It has lot of functionalities and purely supports python.
Session outline:
1] What is streamlit?
2] benefits?
3] installation
4]Hands-on for "NLP task visualizer app"
time: around 40-50 minutes
Pre-requisites:
1] Basic python knowledge
2] laptop with python 3.6 or higher
3] internet connection
talk date : anytime according to community
Empowering ML webapps with Streamlit.pdf