#8 Streamlit interactive dashboards

🎯 Streamlit for Data Apps — Build Dashboards in 10 Lines of Python

Tired of HTML and JS? Streamlit lets you create interactive dashboards with pure Python — no frontend code required! From charts to file uploads to ML model demos — it’s all possible in minutes. Let’s dive into the world of Streamlit! 🚀

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1️⃣ Install Streamlit

!pip install streamlit

Then launch the app with: streamlit run app.py

Just check in your terminal prompt cmd: python -m streamlit hell

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2️⃣ Your First Streamlit App (10 Lines Only)

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

st.title("🚀 My First Streamlit Dashboard")
st.write("Hello, Data Scientists! 👋")

data = pd.DataFrame(np.random.randn(10, 2), columns=["A", "B"])
st.line_chart(data)

Run it and open the browser link — boom 💥 an interactive chart in seconds!

It it does not work, try in your project folder cmd prompt: python -m pip install --upgrade streamlit && python -m streamlit run t1.py

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3️⃣ Adding Widgets

import streamlit as st

st.title("🎛️ Widgets in Action")

name = st.text_input("Enter your name:")
age = st.slider("Select your age", 10, 60, 25)
clicked = st.button("Greet Me")

if clicked:
    st.success(f"Hey {name}, you are {age} years old! 😄")

👉 Streamlit automatically handles UI, state, and interactivity — no callbacks needed!

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4️⃣ Displaying DataFrames and Charts

import streamlit as st
import pandas as pd
import plotly.express as px

st.title("📈 Visualizing Data with Plotly")

df = px.data.gapminder().query("year == 2007")
continent = st.selectbox("Choose Continent", df["continent"].unique())

filtered_df = df[df["continent"] == continent]
fig = px.bar(filtered_df, x="country", y="gdpPercap", color="country", title=f"GDP per Capita in {continent}")
st.plotly_chart(fig)

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5️⃣ File Upload Example

import streamlit as st
import pandas as pd

st.title("📂 CSV File Uploader")

file = st.file_uploader("Upload your CSV file", type=["csv"])
if file:
    df = pd.read_csv(file)
    st.write("Preview:")
    st.dataframe(df.head())

Perfect for quick data exploration dashboards 🔍

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6️⃣ Real-Time Metrics Dashboard

import streamlit as st
import numpy as np
import time

st.title("📊 Real-Time Metrics Dashboard")

progress = st.progress(0)
status = st.empty()

for i in range(100):
    progress.progress(i + 1)
    status.text(f"Processing... {i + 1}%")
    time.sleep(0.05)

st.success("✅ Done!")

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Part of Result 

7️⃣ Deploy to the Web (Free!)

🚀 Host your app free on Streamlit Cloud — just connect your GitHub repo and click “Deploy”. It auto-runs your streamlit run app.py on the cloud 🌐

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✅ What You Learned

• 💡 Create dashboards in pure Python
• 🎨 Add charts, forms, and widgets easily
• 📦 Upload and visualize data
• 🚀 Deploy in minutes with Streamlit Cloud

Bro, now you can build a full-fledged **AI or ML dashboard** in under 30 lines of code 😎 Next step? Try connecting Streamlit with your trained ML model for live predictions!