How to Quickly Deploy Machine Learning Models with Streamlit

by

This article guides you through deploying a simple machine learning model for regression using Streamlit, a platform designed to streamline the deployment of machine learning systems as web services.

Overview of the Deployment Process

In this tutorial, we’ll focus on getting a machine learning model up and running in the cloud within minutes. We’ll create and deploy a straightforward linear regression model to predict house prices based on just one attribute: house size in square feet.

Starting Point – The Code

Here’s the code we will use:

import streamlit as st
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
import plotly.express as px

# Generate synthetic data for training the model
def generate_house_data(n_samples=100):
    np.random.seed(42)
    size = np.random.normal(1500, 500, n_samples)
    price = size * 100 + np.random.normal(0, 10000, n_samples)
    return pd.DataFrame({'size_sqft': size, 'price': price})

# Train the linear regression model
def train_model():
    df = generate_house_data()
    X = df[['size_sqft']]
    y = df['price']
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    model = LinearRegression()
    model.fit(X_train, y_train)
    return model

# Streamlit User Interface
def main():
    st.title('🏠 Simple House Pricing Predictor')
    st.write('Enter house size to predict sale price')
    model = train_model()

    size = st.number_input('House size (square feet)', min_value=500, max_value=5000, value=1500)

    if st.button('Predict price'):
        prediction = model.predict([[size]])
        st.success(f'Estimated price: ${prediction[0]:,.2f}')

        # Visualization
        df = generate_house_data()
        fig = px.scatter(df, x='size_sqft', y='price', title='Size vs Price Relationship')
        fig.add_scatter(x=[size], y=[prediction[0]], mode='markers', marker=dict(size=15, color='red'), name='Prediction')
        st.plotly_chart(fig)

if __name__ == '__main__':
    main()

Key Aspects of the Code

  • The main() function initializes the Streamlit interface for user interaction.
  • It trains the model and provides an input field for users to enter the house size and get a price prediction.
  • Upon clicking the button, Streamlit processes the prediction and displays the results alongside a visualization.

Deployment Process

  1. Save Code: Copy the above code into a .py file and set up a GitHub repository including a requirements.txt file with:
   streamlit==1.29.0
   pandas==2.1.4
   numpy==1.26.2
   scikit-learn==1.3.2
   plotly==5.18.0
  1. Deploy on Streamlit Cloud: Create an account on Streamlit Cloud, click on “Create App”, and select the option to deploy from GitHub. Provide your repository URL, branch, and file information, then click deploy.
  2. Access your App: Your deployed model will now be accessible through the provided URL.

Conclusion

Congratulations! You’ve successfully deployed a machine learning model using Streamlit.

Generated Images for Explanation

Here is an image of a tutorial layout showcasing the deployment of a simple machine learning model using Streamlit. It includes elements such as code snippets for Python, an image of a web interface with ML predictions, and illustrations of data generation and model training.

A tutorial layout showcasing the deployment of a simple machine learning model using Streamlit.

Here is an infographic illustrating the process of deploying a machine learning model on Streamlit. It includes steps such as writing code in Python, creating a GitHub repository, uploading a requirements.txt file, and the deployment interface of Streamlit Cloud.

An infographic illustrating the process of deploying a machine learning model on Streamlit.

Feel free to reach out if you have any more questions or need further assistance!

Leave a Comment