How to Build an Instant Machine Learning Web Application with Streamlit and FastAPI

Learn how to rapidly build your own machine learning web application using Streamlit for your frontend and FastAPI for your microservice.

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NVIDIA
•
Kurtis Pykes
•10 min read•intermediate•
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•View Original
BitbucketCORSCSSDockerFastAPIFlaskHerokuHTMLMachine LearningPythonREST APIStreamlitYAML

Overview

This article provides a comprehensive guide on building a machine learning web application using Streamlit for the frontend and FastAPI for the backend. It outlines the steps to create a prototype that can be deployed on internal servers, enabling stakeholders to interact with machine learning models effectively.

What You'll Learn

1

How to quickly build a machine learning web application using Streamlit and FastAPI

2

Why using Docker and Docker Compose simplifies deployment of web applications

3

How to create a user-friendly interface for machine learning models

4

When to use FastAPI for developing RESTful APIs in Python

Prerequisites & Requirements

  • Basic understanding of Python programming
  • Familiarity with Docker and Docker Compose(optional)

Key Questions Answered

How can I build a machine learning web application using Streamlit and FastAPI?
You can build a machine learning web application by creating a frontend with Streamlit and a backend with FastAPI. This involves setting up user input forms, processing requests, and deploying both components using Docker containers.
What are the key features of Streamlit and FastAPI?
Streamlit simplifies building web applications for machine learning with a focus on Python, allowing rapid development without needing frontend skills. FastAPI is a modern web framework that facilitates the creation of RESTful APIs with thorough documentation and easy deployment.
What input features are used in the car evaluation model?
The car evaluation model uses six input features: buying price, maintenance cost, number of doors, carrying capacity, luggage boot size, and estimated safety level to determine a car's acceptability.
How do I deploy the application using Docker?
To deploy the application, you need to create Dockerfiles for both the Streamlit frontend and FastAPI backend, then use a Docker Compose file to wire the containers together and run the application.

Technologies & Tools

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Frontend
Streamlit
Used to create the user interface for the machine learning web application.
Backend
Fastapi
Used to develop the RESTful API for processing requests from the frontend.
Containerization
Docker
Used to containerize the frontend and backend services for easier deployment.
Orchestration
Docker Compose
Used to wire the Docker containers together for the application.

Key Actionable Insights

1
Utilize Streamlit for rapid prototyping of machine learning applications to enhance stakeholder engagement.
By allowing stakeholders to interact with the model through a web interface, you can gather valuable feedback and improve future iterations of your project.
2
Leverage FastAPI for building RESTful APIs to ensure efficient communication between your frontend and backend services.
FastAPI's modern features and ease of use make it an excellent choice for developers familiar with Python, enabling quick development cycles.
3
Implement Docker and Docker Compose to streamline the deployment process of your machine learning applications.
Containerization helps manage dependencies and ensures that your application runs consistently across different environments.

Common Pitfalls

1
Neglecting to properly configure CORS in FastAPI can lead to issues with frontend requests.
Without proper CORS settings, your frontend may not be able to communicate with the backend, resulting in failed API calls.
2
Failing to manage Docker container dependencies can cause deployment failures.
Ensure that all required packages are listed in the requirements.txt files to avoid runtime errors when the containers are built.

Related Concepts

Microservices Architecture
Restful API Design
Containerization With Docker