Learn to make interactive, beautiful machine learning app using Streamlit :)

What you’ll learn:

1. Brief introduction about Streamlit
2. Installation procedure
3. Basic examples using Streamlit
4. How to save machine learning model?
5. Iris flower classification app
6. Step by step code explanation + video demo :)

Hi everyone :)

Recently I participated in a webinar of learning about Streamlitin my local community and thought, let’s make a tutorial on it and share it with the open source community :)

As a beginner, I believe we all want to make cool stuff using machine learning as quickly as possible!

Developers of Streamlit have created a very simple way for us :)

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Before wasting any time let’s get started.

What is Streamlit?

Streamlit is an open-source framework to create an interactive, beautiful visualization app. All in python!

Streamlit provides many useful features that can be very helpful in making visualizations for data-driven projects.

Why should I use Streamlit?

• Simple and easy way to create an interactive user interface
• Requires zero development experience
• It’s fun making use of different function in your data-driven projects :)
• Comprehensive documentation

Face-GAN explorer using Streamlit

This Streamlit app demonstrates

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Installation Procedure

For Linux

You just need to write below command in your terminal to install Streamlit

pip install streamlit

That’s all :)

For Windows

You can find installation instructions for windows :

Install streamlit for windows

Now let’s explore Streamlit

Drawing content

Type below lines of code and save it as .py extension

import streamlit as st 
x = 4
st.write(x, 'squared is', x * x)

This is how you run the python script using Streamlit:

Open a terminal and make sure you are in the same working directory in which file is saved.

Type below command and press enter:

streamlit run filename.py

Output:

This is what you will see in your default web browser

Okay now let’s make it more interactive by including slider widget in our code:

x = st.slider('x')  # 👈 this is a widget
#st.write(x, 'squared is', x * x)

Output:

Using Slider Widget in Streamlit

Notice: Whenever you modify code, you can see rapid changes in your project.

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Okay now let’s get back to our main objective!

Iris Classification App

Problem statement: classifying Iris flower species from its features.

Iris features: Sepal, Petal, lengths, and widths

This classification problem is also known as the **Hello World **of supervised machine learning!

First, let’s explore iris dataset:

from sklearn.datasets import load_iris

iris= load_iris()
# Store features matrix in X
X= iris.data
#Store target vector in 
y= iris.target

Features

# Names of features/columns in iris dataset
print(iris.feature_names)

Output : ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']

Target/Iris species

# Names of target/output in iris dataset
print(iris.target_names)

Output : ['setosa' 'versicolor' 'virginica']

Types of Iris flower

Dimensions/Size of Iris dataset

# size of feature matrix
print(iris.data.shape)

Output: (150, 4)
# size of target vector
print(iris.target.shape)
Output : (150,)

So, we have 150 rows/observations and 4 columns/features.

Next step is to work on machine learning algorithm :)

Just to get started we will use K Nearest Neighbour.

KNN is like:

“ _Show me who your friends are, and I will tell you what you are _”

Following are KNN steps:

Give new data:

1. Compute the distance between new data and each observation/row in a dataset
2. Get the k number of observations that are nearest to new data

Nearest: Observations with minimum distances (Ascending order)

3. Get the output labels of k number of observations and classify new data based on these most represented/popular output labels

Let’s apply KNN using Scikit learn library!

#beginner #application #how-to #streamlit #deep learning