WEBVTT

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Okay, so here I am within cursor in the terminal in cursor.

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It's a bit confusing.

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You can see that's this bottom half of the screen.

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And I'm in I was originally in the agents directory and I went into the foundations subdirectory.

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So that's where I am right now.

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And this is of course the same directory that has app dot pi.

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And now I type these two words gradio deploy.

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That's it.

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So when I run that it's going to create a new spaces repo in um for, for for me.

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And now we need to give it a title for the app.

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And we're going to call it career conversation.

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But I'm going to call it Career Conversations two because I already have a number one.

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Enter Gradio app file.

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Well, it's app dot Pi that is our Gradio app file.

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So we can just press enter to accept it as it is.

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Enter spaces hardware.

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So we want CPU, dash basic.

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That is the simple, cheap, free, not cheap.

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It's free version of Gradio spaces.

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You will not have to pay for this.

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Any spaces secrets.

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And now we have to say yes, there are spaces.

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Enter the secret name.

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So we have to type open AI API key and we have to get this right.

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If you get it wrong, there is a way to edit it later.

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But but try and not get it wrong.

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And now we have to enter the actual value the the OpenAI API key.

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And I'm of course not going to enter my API key right now.

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Otherwise all of you guys will see it right away.

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But I'll put in a fake key.

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We'll do SQ dash and enter a secret name.

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There are two more secrets that we need to add.

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Do you know what they are?

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They are, of course, the pushover user and whatever that is.

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And the pushover Token and whatever that is and that is it.

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And it says enter blank to to end.

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So I just press enter and that's it.

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Create a GitHub action to automatically up the space.

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And the answer is no.

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So I can just press enter to say no.

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And that is now running and it's done.

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The space is available at that URL just like that.

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So I should now be able to click on that and launch it and see my space in action open.

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And here it is.

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It's still building so it takes a minute to build.

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But luckily my old one is still running over here.

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So I'm just going to flip over to that.

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You can see this is the address Huggingface spaces, my user ID and then career conversation.

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And I can say hi there.

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And it's thinking about that and it says hello, how can I help you?

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I'm going to make sure my phone is on noisy so that we can experience this.

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Do you have a patent?

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Yes, I hold a patent.

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I'd love to hear more about the patent.

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Can I get in touch?

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Absolutely.

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Share your email address.

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I'm at Edward doner.com.

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Ha, ha.

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You like that?

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So there we go.

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And it's come through very nicely.

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And I'm happy to say that I've been alerted about my own email address.

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So hopefully that is clear for you.

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You now see that.

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Hang on.

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If I take this off, hopefully the other one has now deployed as well.

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Career conversations too.

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It's right there.

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There it is as well.

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So I now have two career conversations.

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So this this is how you can interact with a deployed app And also, hugging face gives you a great way

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that you can just embed this in your own website.

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So I have a number of hugging face spaces that that run on my on my website.

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Like I've got this this connect four game that you can play against different LMS.

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And this is just a hugging face space.

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But if you look it looks like it's just coming from my own personal website.

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You can do the same thing.

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The instructions are on the Hugging Face Spaces site, and that way you can have your web page having

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embedded within it your career conversation where people can come to your website.

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They can have a virtual conversation with your avatar about your career, about your interests.

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If they say something that that it doesn't know how to answer, it's going to send you a push notification

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with the question so that you know it right away.

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And if they're if they're willing to give their email address, then it's going to notify you with their

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email address so that you can get in touch.

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So that is a deployed app that you have running and that you can interact with and use it as your own

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personal career avatar.

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So I know this was a lot to take in, but I hope that you found it very satisfying.

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A real application that you can put to good use.

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The more information here about how you do that deployment with the instructions and now the exercises

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for you.

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And this is the the real end of week one exercises that you need to take seriously because these are

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important.

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First and foremost, of course, to state the obvious, you should build this yourself.

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It's a valuable tool.

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It's really cool.

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You should deploy it.

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It is the future of resumes, an interface you can chat with about yourself.

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And next, of course, you should improve the resources that are supplied to the chatbot.

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This is very vanilla, just a LinkedIn profile.

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You can add much more detail about your background.

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You can add in lots of of important stuff.

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And you could answer.

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You could give answers to all of the typical career y kinds of questions.

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So you can make sure that you've got a really robust knowledge base of data there.

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If you know how Rag works, you could implement Rag.

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You could put in a coma data store.

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If you've done my LM engineering course, then you know this stuff super well.

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You could build that in there, and you could have it doing something that has like a bunch of different

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documents that it's able to to refer to in giving good answers with relevant context.

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And then you should add in more tools.

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You could have a tool that can make a SQL query to a database to look at prior questions that can be

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answered, and a database where it could add in questions that require an answer.

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So you could have something that's quite interactive where where the LM is able to to add questions

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to a database and it can text you and then it'll send you a push notification, and then you can come

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in and add the answers, and it will then provide the answers later to the user.

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So you could add all of that stuff, which would be really interesting.

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And then the final exercise for you, of course, is that you may have noticed that whilst in the last

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lab we built an evaluator.

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I didn't actually include the evaluator here because it just felt like that was too, too much, too

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much for one lab.

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But you should now bring that back.

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Let's have the evaluator in there to make sure that all of the responses that people give very professional

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and crisp and do well.

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So these are all good exercises for you to get involved in.

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And there's also plenty of user interface stuff you could do to make this nicer.

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You can make that Gradio app look prettier for sure.

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If you could just ask ChatGPT to give you some advice on Gradio apps.

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And if you know how to stream back results again, if you've taken the the other course, then you should

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add that in of course too.

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And to wrap this up, I just want to talk about the commercial implications.

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I always do want to bring it back to that.

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I mean, the obvious point here is that this is commercially already useful because you can use it for

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yourself to attract clients and perhaps even future buses.

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But also you can extend this kind of thinking to any business situation where you are building AI assistants,

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which is one of the very most common use cases commercially for agentic AI.

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This ability for it to interact with the real world through tools is what really sets something from

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being just a vanilla chatbot on a website to being something that is actually commercially valuable.

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It could be something which doesn't just tell you about ticket prices to travel somewhere, but it can

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then actually go and interact and book the tickets.

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That's the kind of that's the leveling up that you get with the use of tools and using structured outputs

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and providing more resources in the prompt.

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So this is really the bigger commercial opportunity.

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Look for ways that you can have this kind of tool enabled AI assistant, and see how that could be applied

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in your day job.
