Streamlit – Complete Practical Guide

Build data apps, dashboards, and AI tools using pure Python with minimal effort.

What is Streamlit?
When & Why Use Streamlit
Installation & Setup
Streamlit App Structure
Running a Streamlit App
Core Concepts
- Script Rerun Model
- State & Reactivity
Layout & Page Structure
Display Elements
Input Widgets
Data Display Components
Charts & Visualization
Forms
Session State
Caching & Performance
File Uploads & Downloads
Sidebar & Multipage Apps
Configuration & Theming
Deployment Options
Streamlit for AI / LLM Apps
Best Practices
Common Pitfalls
Mini Project Ideas

1️⃣ What is Streamlit?

Streamlit is an open-source Python framework used to build interactive web applications for:

Data analysis
Dashboards
Machine learning demos
AI / LLM-powered tools
Internal tools & prototypes

Key Idea

Write Python → Get a Web App

No frontend frameworks. No HTML/CSS/JS required.

2️⃣ When & Why Use Streamlit

✅ Use Streamlit when:

You want to quickly build a UI for Python logic
You are building AI tools (chatbots, summarizers, RAG apps)
You need internal dashboards
You want fast iteration

❌ Not ideal when:

You need heavy custom UI
You are building large consumer apps
SEO or fine-grained frontend control is critical

3️⃣ Installation & Setup

pip install streamlit

Check installation:

streamlit version

4️⃣ Streamlit App Structure

Minimal app:

import streamlit as st

st.title("Hello Streamlit")
st.write("My first Streamlit app")

📁 Typical structure:

app.py
requirements.txt
pages/
  1_Dashboard.py
  2_Settings.py

5️⃣ Running a Streamlit App

streamlit run app.py

App runs on: http://localhost:8501
Auto reloads on file save

6️⃣ Core Concepts (VERY IMPORTANT)

🔁 Script Rerun Model

👉 Streamlit re-runs the entire script from top to bottom on every user interaction

Example:

count = 0
if st.button("Click"):
    count += 1
st.write(count)

❌ This will always show 1

Why? Because the script restarts.

⚡ Reactivity

Widgets automatically trigger reruns.

name = st.text_input("Enter name")
st.write("Hello", name)

No event handlers required.

7️⃣ Layout & Page Structure

Titles & Text

st.title("Main Title")
st.header("Header")
st.subheader("Subheader")
st.text("Plain text")
st.markdown("**Markdown supported**")

Columns

col1, col2 = st.columns(2)

with col1:
    st.write("Left")

with col2:
    st.write("Right")

Containers

with st.container():
    st.write("Grouped content")

8️⃣ Display Elements

Metrics

st.metric("Revenue", "₹1,20,000", "+8%")

Status Messages

st.success("Success")
st.error("Error")
st.warning("Warning")
st.info("Info")

Progress Bar

import time

progress = st.progress(0)
for i in range(100):
    time.sleep(0.05)
    progress.progress(i + 1)

9️⃣ Input Widgets

Text Inputs

st.text_input("Name")
st.text_area("Description")

Numbers

st.number_input("Age", min_value=0)
st.slider("Rating", 0, 10)

Selection Widgets

st.selectbox("Country", ["India", "USA"])
st.multiselect("Skills", ["Python", "JS", "AI"])
st.radio("Gender", ["Male", "Female"])

Buttons

if st.button("Submit"):
    st.write("Clicked!")

🔟 Data Display Components

Tables & DataFrames

import pandas as pd

df = pd.DataFrame({
    "Name": ["A", "B"],
    "Score": [80, 90]
})

st.dataframe(df)
st.table(df)

JSON

st.json({"name": "Vinod", "role": "Engineer"})

1️⃣1️⃣ Charts & Visualization

Line Chart

st.line_chart(df)

Bar Chart

st.bar_chart(df)

Matplotlib

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot([1, 2, 3], [4, 5, 6])
st.pyplot(fig)

Plotly

import plotly.express as px

fig = px.line(df, x="Name", y="Score")
st.plotly_chart(fig)

1️⃣2️⃣ Forms (Prevent Multiple Reruns)

with st.form("login"):
    user = st.text_input("Username")
    pwd = st.text_input("Password", type="password")
    submitted = st.form_submit_button("Login")

if submitted:
    st.write("Logged in:", user)

1️⃣3️⃣ Session State (CRITICAL)

Used to persist data across reruns.

Initialize

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

Update

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

st.write(st.session_state.count)

1️⃣4️⃣ Caching & Performance

`st.cache_data`

Used for data loading

@st.cache_data
def load_data():
    return pd.read_csv("data.csv")

`st.cache_resource`

Used for models, DB connections

@st.cache_resource
def load_model():
    return model

1️⃣5️⃣ File Uploads & Downloads

Upload

file = st.file_uploader("Upload CSV")
if file:
    df = pd.read_csv(file)
    st.dataframe(df)

Download

st.download_button(
    "Download",
    data="Hello",
    file_name="hello.txt"
)

1️⃣6️⃣ Sidebar & Multipage Apps

Sidebar

st.sidebar.title("Menu")
option = st.sidebar.selectbox("Choose", ["Home", "Settings"])

Multipage Apps

📁 Structure:

app.py
pages/
  1_Home.py
  2_Settings.py

Streamlit auto-detects pages.

1️⃣7️⃣ Configuration & Theming

`.streamlit/config.toml`

[theme]
primaryColor="#4CAF50"
backgroundColor="#FFFFFF"
textColor="#000000"

1️⃣8️⃣ Deployment Options

Streamlit Community Cloud
AWS EC2
Docker
HuggingFace Spaces
Render / Railway

1️⃣9️⃣ Streamlit for AI / LLM Apps

Typical flow:

User input
Call LLM API
Show response

prompt = st.text_area("Ask")
if st.button("Generate"):
    response = call_llm(prompt)
    st.write(response)

Perfect for:

Chatbots
Summarizers
RAG systems
AI tutors

2️⃣0️⃣ Best Practices

✅ Use st.session_state ✅ Use forms for inputs ✅ Cache heavy operations ✅ Modularize logic ✅ Keep UI simple

2️⃣1️⃣ Common Pitfalls

❌ Forgetting rerun behavior ❌ Heavy computation without caching ❌ Global variables ❌ Blocking API calls

2️⃣2️⃣ Mini Project Ideas

AI Chatbot UI
Resume Analyzer
Text Summarizer
CSV Dashboard
LLM Playground
Admin Panel for ML Models

🧠 Final Thought

Streamlit turns Python into a UI superpower.

If you know Python, you already know Streamlit.

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