1. Introduction to Streamlit

Streamlit is an open-source Python library that allows you to build interactive web applications for data science and machine learning projects—without needing any web development experience. It transforms Python scripts into beautiful, shareable web apps in just a few lines of code.

Key features of Streamlit:

• Designed for data scientists and developers who work mainly in Python.
• Requires no HTML, CSS, or JavaScript knowledge.
• Supports integration with popular libraries like Pandas, NumPy, Matplotlib, Plotly, and scikit-learn.
• Automatically updates the app when the underlying code changes.
• Can be deployed easily on platforms like Streamlit Community Cloud, Google Cloud Run, Heroku, or AWS.

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2. Why Use Streamlit?

Streamlit stands out among other frameworks because it focuses on simplicity and speed. Here’s why many developers prefer it:

1. Minimal Code, Maximum Output
   You can create a fully functional data app with just a few lines of Python. For example: import streamlit as st st.write("Hello, Streamlit!") Running this file instantly displays a live web app.
2. Instant Feedback Loop
   Streamlit runs in an interactive mode. When you edit the Python file, the app updates automatically.
3. Great for Data Visualization
   It can render charts, tables, and plots from libraries like:
   • Matplotlib
   • Plotly
   • Seaborn
   • Altair
   • Bokeh
   • PyDeck (for 3D map visualizations)
4. Widgets for Interactivity
   Streamlit provides a rich set of widgets—like sliders, buttons, checkboxes, and select boxes—that allow users to interact with your data.
5. Easy Deployment
   You can deploy your app publicly using Streamlit Community Cloud or deploy privately on your own infrastructure.

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3. Streamlit Installation and Setup

Before running Streamlit, ensure you have Python 3.8 or above installed on your system.

Step 1: Create a Virtual Environment (Recommended)

Creating a virtual environment helps isolate your dependencies.

On Windows:

python -m venv streamlit_env
streamlit_env\Scripts\activate

On macOS/Linux:

python3 -m venv streamlit_env
source streamlit_env/bin/activate

Step 2: Install Streamlit

Once your virtual environment is active, install Streamlit using pip:

pip install streamlit

To verify the installation:

streamlit --version

You should see an output like:

Streamlit, version 1.38.0

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4. Creating Your First Streamlit Application

Let’s create a simple “Hello Streamlit” app to understand the basics.

Step 1: Create a Python File

Create a file named app.py in your working directory.

Step 2: Add the Following Code

import streamlit as st

# Title of the app
st.title("Welcome to Streamlit!")

# Add a simple text message
st.write("Hello, Streamlit! This is your first web app.")

# Display a number input and show its square
number = st.number_input("Enter a number", min_value=0)
st.write("Square of the number is:", number ** 2)

Step 3: Run the App

In your terminal, run:

streamlit run app.py

Step 4: Open the Browser

Once you run the command, Streamlit automatically starts a local server and opens your default browser at:

http://localhost:8501

You will see your web app live and interactive!

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5. Understanding Streamlit’s Core Concepts

a. Writing Text

Streamlit provides multiple functions for text:

st.title("Main Title")
st.header("Section Header")
st.subheader("Subsection")
st.text("Plain text")
st.markdown("**Bold text using Markdown**")

b. Displaying Data

You can easily display Pandas DataFrames or charts:

import pandas as pd
import numpy as np

data = pd.DataFrame({
    'x': np.arange(10),
    'y': np.random.randn(10)
})

st.write("Data Table:", data)
st.line_chart(data)

c. Adding Widgets

Widgets make the app interactive:

name = st.text_input("Enter your name:")
age = st.slider("Select your age:", 1, 100, 25)
agree = st.checkbox("I agree to the terms")

if agree:
    st.write(f"Hello {name}, you are {age} years old!")

d. Displaying Images and Media

Streamlit can show images, videos, and audio directly.

from PIL import Image
image = Image.open("example.png")
st.image(image, caption="Example Image", use_column_width=True)

st.video("example.mp4")
st.audio("example.mp3")

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6. Running and Managing Streamlit Applications

Step 1: Running the App

As mentioned earlier, use:

streamlit run app.py

This starts a local development server.

Step 2: Stop the Server

To stop the app, press CTRL + C in your terminal.

Step 3: Run on a Specific Port

If port 8501 is busy, specify another port:

streamlit run app.py --server.port 8502

Step 4: View Logs

Use the --logger.level argument to see debug information:

streamlit run app.py --logger.level=debug

Step 5: Automatically Reload the App

Streamlit automatically reloads when you save changes to your script, so you can see updates in real time.

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7. Advanced Features

a. Session State (Maintaining User Data)

Streamlit provides st.session_state to persist data between reruns.

if 'count' not in st.session_state:
    st.session_state.count = 0

if st.button("Increment"):
    st.session_state.count += 1

st.write("Counter:", st.session_state.count)

b. Layout Management

Use columns and containers to structure your UI.

col1, col2 = st.columns(2)
col1.write("This is column 1")
col2.write("This is column 2")

with st.container():
    st.write("This is inside a container.")

c. Caching and Performance Optimization

To cache results of expensive computations:

@st.cache_data
def load_data():
    import time
    time.sleep(2)
    return {"data": [1, 2, 3]}

st.write(load_data())

Caching improves performance by storing function results and reusing them instead of re-running every time.

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8. Deployment of Streamlit Apps

Once your app is ready, you can deploy it.

Option 1: Streamlit Community Cloud

1. Push your code to GitHub.
2. Visit https://streamlit.io/cloud.
3. Connect your GitHub account.
4. Choose your repository and click “Deploy.”

Option 2: Using Docker

You can containerize your Streamlit app:

FROM python:3.10
WORKDIR /app
COPY . /app
RUN pip install streamlit pandas numpy
EXPOSE 8501
CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]

Then build and run:

docker build -t streamlit-app .
docker run -p 8501:8501 streamlit-app

Option 3: Deploy on Cloud Platforms

You can also deploy on:

• Google Cloud Run
• AWS Elastic Beanstalk
• Azure App Service
• Heroku (free tier limited)

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9. Common Errors and Troubleshooting

Problem	Cause	Solution
App not launching	Port already in use	Run on another port using --server.port
Image/Media not loading	Wrong file path	Use absolute path or os.path.join
Slow performance	Large dataset or computation	Use @st.cache_data to cache
Widgets not updating	Code reruns incorrectly	Use st.session_state for managing widget state

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10. Example: A Simple Data Dashboard

Here’s a small example combining all concepts:

import streamlit as st
import pandas as pd
import numpy as np

st.title("Sales Dashboard")

# Generate sample data
data = pd.DataFrame({
    "Month": pd.date_range("2025-01-01", periods=12, freq="M"),
    "Sales": np.random.randint(2000, 5000, 12)
})

# Filter by threshold
threshold = st.slider("Minimum Sales:", 2000, 5000, 3000)
filtered_data = data[data["Sales"] >= threshold]

# Display
st.write("Filtered Data", filtered_data)
st.line_chart(filtered_data.set_index("Month"))

When you run this script, you’ll see an interactive sales dashboard that lets you filter data using a slider.

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11. Conclusion

Streamlit is a powerful yet simple framework for transforming Python scripts into interactive web apps. It eliminates the complexity of frontend development and focuses purely on data and interactivity.

By mastering Streamlit, you can:

• Build custom dashboards for data analysis.
• Create ML model demos.
• Build prototypes for user feedback.
• Deploy production-ready data apps.

With just a few lines of Python, Streamlit empowers you to bring your ideas to life instantly.