In this chapter we will be learning about how to build interactive demos for your machine learning models.
Why build a demo or a GUI for your machine learning model in the first place? Demos allow:
- Machine learning developers to easily present their work to a wide audience including non-technical teams or customers
- Researchers to more easily reproduce machine learning models and behavior
- Quality testers or end users to more easily identify and debug failure points of models
- Diverse users to discover algorithmic biases in models
We’ll be using the Gradio library to build demos for our models. Gradio allows you to build, customize, and share web-based demos for any machine learning model, entirely in Python.
Here are some examples of machine learning demos built with Gradio:
- A sketch recognition model that takes in a sketch and outputs labels of what it thinks is being drawn:
- An extractive question answering model that takes in a context paragraph and a quest and outputs a response and a probability score (we discussed this kind of model in Chapter 7):
- A background removal model that takes in an image and outputs the image with the background removed:
This chapter is broken down into sections which include both concepts and applications. After you learn the concept in each section, you’ll apply it to build a particular kind of demo, ranging from image classification to speech recognition. By the time you finish this chapter, you’ll be able to build these demos (and many more!) in just a few lines of Python code.
If you want to put the knowledge from this chapter to good use, come join the Gradio blocks party! This is a community event that’s hosted by Hugging Face on May 16-31. During this event, you’ll build cool machine learning demos with Gradio and be in the running to win Hugging Face swag and prizes!
Check out the event description for details on how to participate - we can’t wait to see what you’ll build 🤗!