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
- To build beautiful web applications using an open-source python package
- Used to build custom web applications using data and machine learning models
- 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
