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Streamlit is a Python library that allows you to create and share custom web apps for machine learning and data science. It provides an intuitive way to build user interfaces for your models and data visualizations.
Description
how to use:
To use Streamlit, you need to install it using pip and then import the library in your Python script. You can then use Streamlit’s functions to create interactive widgets, generate plots, and display data. Finally, you can run your app using the ‘streamlit run’ command.
Core freatures:
The core features of Streamlit include:
– Easy and fast app creation using Python
– Interactive widgets for user input
– Automatic reactivity to update apps when data changes
– Built-in support for popular Python libraries like Pandas, Matplotlib, and Plotly
– Seamless deployment on various platforms
Use case:
Streamlit can be used for various use cases, including:
– Building interactive dashboards for exploratory data analysis
– Prototyping and showcasing machine learning models
– Creating data visualization apps
– Creating internal tools for data analysis or automation
FAQ list:
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