WEBVTT

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And welcome to week two, day two, as we continue our adventure into the realm of LMS.

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Uh, so today, a very special day that I'm really looking forward to.

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Uh, quick recap.

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Of course, you can now describe Transformers as well, and you can talk about six top frontier models.

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You can confidently use OpenAI's API along with Anthropic and Google's.

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But today, changing topic, we are going to be talking about Gradio.

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And I realized I have gone on about Gradio a bit, but you're going to see why.

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It's really terrific and we're going to have fun with it.

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We're going to create a simple UI using radio and then hook it up to Frontier Models.

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And as I say, it's going to be easy.

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Uh, so why make such a fuss about user interfaces?

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Because it allows us, as data scientists, as LM engineers, to do more quickly, to be able to build

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prototypes, expose them to our audience, to our business sponsors, the the, the people that need

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our LMS and do so very quickly indeed.

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If you are a front end person or you've dabbled in front end and you know what it's like to stand up

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a react app or something like that, you know that there's a lot of boilerplate code that goes into

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getting things up and running, and it turns out that we don't need to do that with models.

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We can build a user interface super quickly, and that's what we'll be doing today.

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So Gradio is in fact, uh, a part of Hugging Face.

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It was a startup that was acquired by Hugging Face a couple of years ago.

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So Gradio is part of the Hugging Face family.

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Uh, and as it says on the landing page there, it lets you build and share delightful machine learning

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apps.

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And I think you will be delighted by it.

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Uh, so I promised you it was easy.

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It really is easy, as you will see.

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What it comes down to is there is this magical line import Gradio as GR, which is the way people do

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it.

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You write a function, any function, a function to do a task.

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In this case, the function that they've written here is greet takes a name and it replies hello name.

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And then you can create a user interface based on that function, give it inputs and outputs, and you're

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going to get a user interface built for you just like that.

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And that is what we're going to do.

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So what we're going to do now is create a UI for API calls to GPT and Claude and Gemini, so that you

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can see how to expose this.

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We are then going to go ahead and create a UI for the brochure that we built in the last week's lectures.

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And so that's going to allow us to really package up our application into a nice business app with prototype

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screens.

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And of course, we'll throw into the mix streaming and markdown into a UI, since we're pretty good

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with that already, that's going to be the plan.

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Let's go over to the lab and get on with it.