Gradio: The Perfect Tool for Building ML Model UIs Quickly and Easily

Introduction

Machine learning models are powerful, but without an intuitive user interface (UI), they often remain out of reach for those without technical backgrounds. This is where Gradio comes in. Indeed, if you are looking to create an accessible, interactive UI for your ML model, it is the tool you need. In this article, we’ll explore why Gradio is the best choice for ML UIs, offering ease of use, rapid setup, and integrations with popular ML libraries.

Overview

Gradio is built for machine learning, making it ideal for creating UIs for ML models like LGBM. Indeed, it lets you set up an interactive demo with just a few lines of code.

While Streamlit is also simple and Python-friendly, Gradio stands out with its specialized ML components and easy model integration. If you’re curious about Streamlit, check out this article on using it with DVC.

Set up your first Gradio Interface in minutes

Gradio’s minimal setup allows you to build an interactive app, easily and quickly, with just a few lines of code. Thus, you define your model and specify the input and output components, and it generates the interface instantly.

In just a few lines, you’ve got a fully functional app that takes user input, runs it through the model, and displays the result. Here is a visual of the interface created with the code above:

Use Gradio Interactive Components for an Engaging User Experience

Gradio offers several interactive components specifically designed for ML applications, making it easy to set up effective UIs without having to custom-build each element, for instance:

• Input Types. Gradio supports text, image (which you can even edit with a brush), audio, video, and file inputs, making it ideal for a range of ML models, from NLP and computer vision to audio processing.
• Audio and File Uploads. Supports tasks like audio classification or file-based processing.
• Output Options. Display outputs in formats like text, images, and interactive charts, so users can understand model predictions easily.

Here is one example, of adding text, slider and checkbox inputs leading to text and number outputs:

Thus, the user can easily interact with the input components to obtain the desired output.

Customise your Gradio Interface

While Gradio’s Interface API is fantastic for quick demos, the Blocks API offers extensive customisation and flexibility for more complex applications.

Add more flexibility to your Gradio Interface by using Blocks

Blocks enable you to build multistep workflows, complex layouts, and multipage applications, with precise control over UI components.

Gradio’s event-driven functions let you define actions on interactions such as button clicks or data changes. These functions make it possible to design highly interactive applications with conditional responses.

Each function is custom to process inputs or data as needed, increasing flexibility in handling complex input-output combinations. Thus, with blocks, you can construct an even more complex interface :

A multistep interface using Blocks

The Blocks API is ideal for building interactive, multipart applications that can handle complex processes or conditional steps within the UI.

⚠️ While Gradio shines in quick setups, it's worth noting that managing the code, especially with the Blocks API, can become challenging as applications grow in complexity. Developers may need to adopt best practices for code organisation to handle larger projects effectively.

Make the app user-friendly by using warnings and errors

Gradio also supports handling warnings and errors directly within the interface, enhancing user experience and making debugging easier.

By using functions to display warnings or errors, you can inform users when an input is invalid or when an issue arises during processing. For example, if an uploaded image is in the wrong format or if the entered text exceeds a certain length, a clear error message can appear, guiding the user to correct their input. This error-handling capability makes Gradio especially useful for production applications, ensuring smoother interactions and more robust applications.

However, Gradio currently lacks built-in support for user authentication and access control. Developers needing these features may need to implement custom solutions or integrate Gradio apps within a larger web framework.

Deploy and Share your App

It is easy to deploy and share Gradio interfaces. Indeed, it provides robust deployment options, making it easy to share your applications or scale them up as needed.

• Simple Public Sharing. With the share=True parameter, Gradio generates a public, shareable link for your app. This option is ideal for quickly sharing prototypes or demos with stakeholders or collaborators. ⚠️ Be aware that when sharing it like that, the model will run locally where you launch the Gradio app.
• Hosting on Hugging Face Spaces. Gradio integrates easily with Hugging Face Spaces, enabling you to deploy your application for free on a scalable platform. This is particularly useful for ML models already hosted on Hugging Face.
• Custom Server Deployments. For production-ready applications, Gradio apps can be deployed on custom servers, ensuring reliable access and performance.

Once you deploy your app, you need to manage several users. Gradio has an exciting feature to handle multiple users, named queues. It ensures your app can handle multiple users without crashing or timing out. By default, all actions in Gradio operate in queues. This is particularly valuable for models requiring extensive processing time or deploying models across CPUs and GPUs. You can configure separate queues with unique limits based on available resources—unlimited requests for CPUs and limited requests for GPUs, for example.

Last but not least, you can use almost any Gradio app as an API! Indeed, Gradio supports HTTP requests, allowing you to integrate Gradio applications into custom websites or other web applications.

A Dynamic and Actively Maintained Library

Gradio is constantly evolving to meet the needs of its user base, and recent updates show a strong commitment to improvement.

An Active Development Team

Gradio’s team is highly responsive, frequently addressing bugs and implementing new features. This attention to detail makes it a reliable choice for production applications. They often release new features!

A Thriving Community

Gradio has built a substantial community with over 2 million monthly users. This vibrant community provides support, shares best practices, and contributes to Gradio’s ongoing growth and enhancement.

Conclusion

For building UIs for machine learning models, Gradio is unmatched. Its ease of use, flexibility, deployment options, and robust support for multiple users make it the best choice for creating interactive, accessible applications. Whether developing a prototype, deploying to a production server, or sharing an ML model demo, Gradio provides everything you need to bring your model to life.

However, it's important to consider the specific needs of your project. For applications requiring extensive customisation, advanced data visualization, or built-in security features, other frameworks like Streamlit might be more suitable.

We encourage you to try Gradio for your next project and experience first-hand how it can streamline the development of ML model UIs. With its active community and ongoing improvements, Gradio continues to evolve as a valuable tool in the machine-learning ecosystem.

Are you looking for machine learning experts? Don’t hesitate to contact us!