WEBVTT 00:00.680 --> 00:08.540 And welcome to week two, day two, as we continue our adventure into the realm of LMS. 00:08.780 --> 00:14.630 Uh, so today, a very special day that I'm really looking forward to. 00:14.720 --> 00:15.830 Uh, quick recap. 00:15.860 --> 00:22.250 Of course, you can now describe Transformers as well, and you can talk about six top frontier models. 00:22.250 --> 00:27.920 You can confidently use OpenAI's API along with Anthropic and Google's. 00:27.920 --> 00:33.080 But today, changing topic, we are going to be talking about Gradio. 00:33.350 --> 00:37.040 And I realized I have gone on about Gradio a bit, but you're going to see why. 00:37.040 --> 00:40.490 It's really terrific and we're going to have fun with it. 00:40.640 --> 00:46.070 We're going to create a simple UI using radio and then hook it up to Frontier Models. 00:46.070 --> 00:49.130 And as I say, it's going to be easy. 00:49.580 --> 00:53.420 Uh, so why make such a fuss about user interfaces? 00:53.420 --> 01:00.890 Because it allows us, as data scientists, as LM engineers, to do more quickly, to be able to build 01:00.920 --> 01:08.510 prototypes, expose them to our audience, to our business sponsors, the the, the people that need 01:08.540 --> 01:12.140 our LMS and do so very quickly indeed. 01:12.140 --> 01:17.000 If you are a front end person or you've dabbled in front end and you know what it's like to stand up 01:17.030 --> 01:22.370 a react app or something like that, you know that there's a lot of boilerplate code that goes into 01:22.400 --> 01:26.750 getting things up and running, and it turns out that we don't need to do that with models. 01:26.750 --> 01:30.380 We can build a user interface super quickly, and that's what we'll be doing today. 01:30.380 --> 01:35.030 So Gradio is in fact, uh, a part of Hugging Face. 01:35.030 --> 01:38.510 It was a startup that was acquired by Hugging Face a couple of years ago. 01:38.510 --> 01:41.600 So Gradio is part of the Hugging Face family. 01:41.780 --> 01:48.260 Uh, and as it says on the landing page there, it lets you build and share delightful machine learning 01:48.290 --> 01:48.620 apps. 01:48.620 --> 01:51.620 And I think you will be delighted by it. 01:51.830 --> 01:54.530 Uh, so I promised you it was easy. 01:54.560 --> 01:57.080 It really is easy, as you will see. 01:57.110 --> 02:02.790 What it comes down to is there is this magical line import Gradio as GR, which is the way people do 02:02.790 --> 02:03.270 it. 02:03.570 --> 02:07.500 You write a function, any function, a function to do a task. 02:07.500 --> 02:13.380 In this case, the function that they've written here is greet takes a name and it replies hello name. 02:13.380 --> 02:19.890 And then you can create a user interface based on that function, give it inputs and outputs, and you're 02:19.890 --> 02:24.120 going to get a user interface built for you just like that. 02:24.120 --> 02:26.430 And that is what we're going to do. 02:26.970 --> 02:35.430 So what we're going to do now is create a UI for API calls to GPT and Claude and Gemini, so that you 02:35.430 --> 02:37.380 can see how to expose this. 02:37.410 --> 02:45.870 We are then going to go ahead and create a UI for the brochure that we built in the last week's lectures. 02:45.900 --> 02:52.050 And so that's going to allow us to really package up our application into a nice business app with prototype 02:52.050 --> 02:52.860 screens. 02:52.860 --> 02:57.510 And of course, we'll throw into the mix streaming and markdown into a UI, since we're pretty good 02:57.510 --> 03:00.630 with that already, that's going to be the plan. 03:00.630 --> 03:03.060 Let's go over to the lab and get on with it.