Gradio is a surefire strategy to impress each technical and non-technical stakeholders — Why aren’t more Data Scientists and MLEs using it?

PowerPoint presentations suck.

At the very least, bad ones do.

Bad PowerPoints create distracted audiences (who turn off their cameras and multitask), they usually make it easy for presenters to get away with bad habits like using an excessive amount of technical jargon and waffling on for too long.

So why do Data Scientists use PowerPoint a lot?

On a recent Reddit thread on this topic, respondents working in DS reported spending anywhere between 10–60% of their time making slide decks or giving presentations. I realise that’s not a really robust statistic, but whatever the true distribution, the sentiment chimes true for a lot of us working in Data Science: we use PowerPoint – A LOT – to showcase all the things from model cards to screenshots of ROC curves and Shapley values.

Prefer it or not, , and it’s not going anywhere.

Or is it?

In this text, I’m going to introduce you to Gradio, a free tool which helps you to:

1. via your browser or Jupyter Notebook
2. via interactive, easy-to-understand visualisations
3. Test your models and and have importances

I don’t have any affiliation with Gradio and I’m not attempting to sell you anything — I simply want to indicate you a tool which has worked well for me in my job as a Data Scientist, .

Within the developers’ own words,

Gradio is the fastest strategy to demo your machine learning model with a friendly web interface in order that anyone can use it, anywhere!

How does this work? It’s surprisingly easy.

First, install Gradio via pip.

pip install gradio

Next, import Gradio and define a function that may take an input. Then, wrap your model in a ‘gradio.Interace()’ class, and – hey presto – your model is given a friendly, interactive interface which might be embedded in a notebook or webpage. Here’s an example using a quite simple “Hello {user}!” function:

import gradio as gr
def greet(name):
return "Hello " + name + "!"
demo = gr.Interface(fn=greet, inputs="text", outputs="text")
demo.launch()   

When you run that in a Jupyter Notebook, the demo above will appear routinely in a recent cell. When you run it in a script, the demo will appear in your browser at http://localhost:7860. When you like, you too can “routinely generate a public link you may share with colleagues that lets them interact with the model in your computer remotely from their very own devices” (docs).

To take a rather more complex example, let’s say we had an ML model which could recognise hand-drawn images. Using Gradio, we could create a sketch pad which accepts user input…

import gradio as gr
def sketch_recognition(img):
pass# Implement your sketch recognition model here...
gr.Interface(fn=sketch_recognition, inputs="sketchpad", outputs="label").launch()

… giving us a nifty strategy to draw sketches, pass them to the model, and demo the model in live-time:

Note: to maintain this text to a manageable length, I’m not including details on the model itself; should you’re searching for models, you would possibly want to ascertain out HuggingFace, an important repository of pre-trained models which might be easily loaded right into a Jupyter notebook or Python script and used with Gradio.

With just a couple of lines of code, Gradio makes it easy to showcase your models in an interactive way that anyone can understand. Take into consideration the probabilities in your team – how much easier could this make it to showcase your models to your team or stakeholders?