Streamlit Flashcards

Posted on by uplatzblog

This page gives you a quick, practical overview of Streamlit—a lightweight
Python app builder that turns scripts into interactive web apps in minutes.
You’ll see where it fits in data science and how to launch your first dashboard quickly.

In short, you write normal Python, sprinkle in UI calls, and the framework handles layout, state,
and hot reloading. Moreover, it supports rich charts, file uploads, and caching so your app stays fast.
Because everything lives in one script, teams iterate rapidly without heavy frontend code.

Streamlit architecture: Python script, widgets, and reactive reruns powering a data app
Typical flow: run a Python script, add widgets, and let reactive reruns update the UI.

If you plan to ship an AI or analytics tool, start small: add controls, cache heavy steps, and deploy to a host.
For an end-to-end walkthrough, see our guide on
building and deploying a Streamlit data app.
In addition, the official docs provide component examples and deployment tips.

Streamlit dashboard with charts, filters, and file uploader widgets
Dashboards combine charts, filters, and uploads—without writing custom frontend code.

Scroll to the flashcards for a compact refresher. Then, use the links at the end to explore templates and examples.

🚀 Streamlit Flashcards
💡 What is it?
An open-source Python framework that turns data scripts into interactive web apps.
⚙️ How does it work?
Scripts run top to bottom and re-run on each user interaction to keep the UI reactive.
📊 Can it display charts?
Yes—use Matplotlib, Plotly, Altair, and other Python viz libraries.
🔘 What are widgets?
Sliders, buttons, and select boxes let users explore data and trigger logic.
📁 How do you upload files?
Use st.file_uploader() to import CSVs, images, and other formats.
🔒 What about auth?
Use Streamlit Community Cloud’s built-in options or integrate external providers.
🧰 How do you deploy?
Host on Streamlit Community Cloud or deploy to Heroku, AWS, GCP, and similar platforms.
🧪 What is caching?
Decorate expensive functions with @st.cache_data or @st.cache_resource.
🎯 Can it run ML models?
Absolutely—load Scikit-learn, TensorFlow, or PyTorch models and add interactive inputs.
📦 How do you install it?
Run pip install streamlit to get started in your environment.

To begin, prototype a small dashboard, add a couple of widgets, and cache slow data loads.
Next, add authentication and environment secrets before sharing. Finally, profile runtime and memory
so the app stays responsive as your data grows.

Read our step-by-step tutorial:
Building and Deploying a Streamlit Data App.
For official guides and component references, visit the
Streamlit documentation.