Deploy a Machine Learning Model using Streamlit Library

Last Updated : 27 May, 2025

Streamlit is a Python tool that helps you build websites for your data projects or machine learning models. It’s easy to use and lets you create interactive apps with very little code. You don’t need to know how to build a website from scratch. With Streamlit you don’t need to worry about backend development or handling HTTP requests.

In this article, we’ll learn how to deploy a machine learning model using the Streamlit library in a step-by-step manner.

Steps to Deploy a Machine Learning Model Using Streamlit

Let’s train a machine learning model to classify Iris flowers and then deploy it with Streamlit. Firstly we need to install the following:

1. Importing Libraries and Dataset

We'll import pandas and scikit learn library and then import Iris dataset which contains data on three species of Iris flowers. Each entry includes measurements of the flowers' sepal length, sepal width, petal length and petal width. You can download the dataset from here. Iris Dataset

Python

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score  df = pd.read_csv("iris.csv")

2. Training the Model

We’ll start by loading and preparing the dataset. Since the goal of this article is deployment we will do only basic preprocessing but feel free to make changes. We will use a Random Forest Classifier for this example but other classifiers like Logistic Regression or Support Vector Machine can also be used.

python

df.drop('Id', axis=1, inplace=True)  X = df.drop('Species', axis=1) y = df['Species']  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)  model = RandomForestClassifier() model.fit(X_train, y_train)  y_pred = model.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print(f"Model accuracy: {accuracy * 100:.2f}%")

We get an accuracy of 95.55% which is pretty good. Now in order to use this model to predict other unknown data, we need to save it. We can save it by using pickle which is used for serializing and deserializing a Python object structure.

3. Saving the Model

Now that the model is trained we need to save it so it can be used for predictions later. We can use the pickle library to serialize the model and save it as .pkl file.

python

import pickle with open("classifier.pkl", "wb") as model_file: pickle.dump(model, model_file)

This will create a classifier.pkl in your working directory which contains the trained model.

4. Deploying with Streamlit

Next let’s deploy the model using Streamlit. Create a new Python file like app.py and add the following code

python

import streamlit as st import pickle import numpy as np  with open("classifier.pkl", "rb") as model_file:     model = pickle.load(model_file)  st.title("Iris Species Classifier") st.write("Enter the flower measurements to classify the species.")  sepal_length = st.slider("Sepal Length (cm)", min_value=4.0, max_value=8.0, step=0.1) sepal_width = st.slider("Sepal Width (cm)", min_value=2.0, max_value=5.0, step=0.1) petal_length = st.slider("Petal Length (cm)", min_value=1.0, max_value=7.0, step=0.1) petal_width = st.slider("Petal Width (cm)", min_value=0.1, max_value=2.5, step=0.1)  if st.button("Predict"):     features = np.array([[sepal_length, sepal_width, petal_length, petal_width]])     prediction = model.predict(features)     st.write(f"Predicted Iris Species: {prediction[0]}")

This code creates a simple web app where users can input flower measurements and the model will predict the Iris species based on those values

5. Running the App

To run the app open your terminal and type:

streamlit run app.py

This will launch the Streamlit app in your browser. You can enter flower measurements, click "Predict," and the model will output the predicted species

Output:

Deploying machine learning models with Streamlit is fast, simple and perfect for creating interactive applications. With just a few lines of code you can turn your machine learning model into a user-friendly web application. For more detail refer to:

Deploy a Machine Learning Model using Streamlit Library

AashalKamdar

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Deploy a Machine Learning Model using Streamlit Library

Steps to Deploy a Machine Learning Model Using Streamlit

1. Importing Libraries and Dataset

2. Training the Model

3. Saving the Model

4. Deploying with Streamlit

5. Running the App

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