Streamlit

Streamlit Study Notes

Streamlit is a Python framework designed for creating interactive web applications for data analysis, visualization, and machine learning.

Step-by-Step Guide

1. Installation and Setup

• Recommended: Use a virtual environment to keep your project dependencies isolated.

Creating and activating a virtual environment (Python ≥ 3.8 recommended):

python -m venv streamlit_env

source streamlit_env/bin/activate  # macOS/Linux

• Install Streamlit:

pip install streamlit

Check your installation by typing:

streamlit hello

This opens a demo app in your browser.

2. Creating Your First Streamlit App

Create a new Python file, e.g., app.py:

# Simple Streamlit App

import streamlit as st

st.title("My First Streamlit App")
st.write("Hello, Streamlit! 🎉")

• Run the app:

streamlit run app.py

3. Building an Interactive App: A Professional Example

Here is a more advanced app demonstrating common functionalities educators or analysts might use:

Example: Interactive Data Exploration App (app.py):

import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt

# Load dataset
@st.cache_data
def load_data():
    df = pd.read_csv("https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv")
    return df

df = load_data()

st.title("Interactive Iris Dataset Explorer")

st.header("Dataset Preview")
st.write(df.head())

# Sidebar widgets
st.sidebar.header("Filter Data")

species = st.sidebar.multiselect(
    "Select Species",
    options=df["species"].unique(),
    default=df["species"].unique()
)

filtered_df = df[df["species"].isin(species)]

st.header("Filtered Data")
st.write(filtered_df)

# Visualization
st.header("Data Visualization")

fig, ax = plt.subplots()
ax.scatter(filtered_df["sepal_length"], filtered_df["sepal_width"], c='blue', alpha=0.5)
ax.set_xlabel("Sepal Length")
ax.set_ylabel("Sepal Width")
ax.set_title("Sepal Length vs. Sepal Width")

st.pyplot(fig)

• Run the app again:

streamlit run app.py

This provides an interactive dashboard for exploring Iris dataset features, filtering, and visualization.

4. Streamlit Cloud Deployment

Streamlit apps can be easily deployed using Streamlit Cloud:

Step-by-step deployment process:

• Prerequisites:
• Create a GitHub account.
• Push your project code (app.py) to GitHub.
• Directory structure for your project:

CopyEdit
streamlit-iris-app/
├── app.py
├── requirements.txt

• Create requirements.txt:

pip freeze > requirements.txt

This captures dependencies for deployment.

5. Deploying to Streamlit Cloud

1. Visit https://streamlit.io/cloud, sign in, and select "New app".
2. Connect your GitHub account and select your repo.
3. Choose the branch (usually main) and specify your Python file (default: app.py).
4. Click "Deploy".

Your app will be live in minutes!

Key Streamlit Concepts

• Widgets: Create interactive UI elements like sliders, dropdowns, buttons, checkboxes, and multiselect.
• Layouts: Use st.sidebar, st.columns, and st.tabs to organize UI elements.
• Caching: Optimize app performance with @st.cache_data decorator to avoid repeated computations or data reloads.

Streamlit Widgets Quick Reference

Common Deployment Issues

• Dependency management: Always use requirements.txt for dependencies.
• Large files or datasets: Use external storage solutions (e.g., GitHub raw URL, AWS S3).