Introduction to Streamlit What & Why

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.