Introduction
to Streamlit: What
& Why?
What is Streamlit?
An open-source Python framework for building interactive web applications. Designed for data scientists and ML engineers—no front-end expertise needed. Turns Python scripts into web apps with minimal effort. Uses a declarative approach—write Python, and Streamlit handles the UI.
Why Use Streamlit?
Simplicity: Create web apps with just a few lines of Python.
Interactive Widgets: Sliders, buttons, file uploads, and more.
Rapid Prototyping: Ideal for showcasing ML models and data visualizations.
No Frontend Needed: No HTML, CSS, or JavaScript required.
Deployment Friendly: Easily share apps via Streamlit Cloud or other platforms.
Key Features of Streamlit
Easy UI Creation: Simple API to add elements (st.title(), st.button()).
Live Code Updates: Auto-reload changes without restarting.
Built-in Caching: Optimizes performance with @st.cache_data.
Supports Popular Libraries: Works with Pandas, Matplotlib, Plotly, and TensorFlow. Authentication & Deployment: Streamlit Cloud and third-party hosting support.
How Streamlit Works? (Workflow)
1.Install Streamlit → pip install streamlit
2.Write a Python Script (Use st.write(), st.slider(), etc.)
3.Run the App → streamlit run app.py
4.Interact & Share: Modify code and share links instantly.
When to Use Streamlit?
✅ Building Interactive Data Dashboards
✅ Quick Prototyping for ML & AI Models
✅ Creating Custom Web Apps for Data Science
✅ Showcasing Research & Analytical Reports
✅ Internal Tools for Teams & Businesses
Conclusion
Streamlit simplifies web app development for data scientists and ML engineers.
No frontend knowledge needed—just Python!
Fast prototyping for interactive data apps and AI/ML model deployment. Seamless integration with Pandas, Matplotlib, Plotly, and more. Easy deployment & sharing via Streamlit Cloud or other platforms.