This lab helps us understand how Streamlit ( an open-source Python library) can be used to build and create custom web applications for machine learning and data science.

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

Advantages:

• Free and Open-Source
• Less coding and easy to build beautiful web application in less time
• It embraces Python scripting language
• Data caching simplifies and speeds up computation pipelines
• No callbacks are needed
• Works with TensorFlow, Keras, PyTorch, Pandas, Numpy, Matplotlib, Seaborn, Altair, Plotly, Bokeh, Vega-Lite, and more

Experimental Setup

Prerequisites:

pip install streamlit

Test Cases

• Start the app by running streamlit run demo.py
• Streamlit run app.py

Lessons Learned

1. To build beautiful web applications using an open-source python package
2. Used to build custom web applications using data and machine learning models
3. Can be done without having any prior knowledge of web development

References

• https://discuss.streamlit.io/t/streamlit-cheat-sheet/4912
• https://github.com/streamlit/demo-uber-nyc-pickups
• https://github.com/streamlit/demo-self-driving
• https://docs.streamlit.io/en/stable/tutorial/create_a_data_explorer_app.html
• https://www.streamlit.io/gallery