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Hello guys!

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Now we have developed so many different applications.

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Now I will just try to show you how you can go ahead and deploy this kind of applications.

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So from all these examples you can take up any example that you want.

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But I will go ahead and take this particular search engine which I had actually developed.

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So I will go into its file.

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Right.

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So when I probably go over here you'll be able to see all these files I have actually arranged in this

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way.

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So this is my V and V folder and this is my requirement dot txt.

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Now to start the work over here.

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What I am actually going to do is that I will take this requirement dot txt I will copy this inside

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this particular folder.

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And this is what is my search engine right now.

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Let me just go ahead and deploy this entire application in a Streamlit web app or Streamlit cloud.

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Okay, so since this is a Streamlit web app, I will try to go ahead and show you first the deployment

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with Streamlit Cloud.

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So I will just go ahead and type Streamlit Cloud okay.

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And I will go ahead and hit enter.

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Let me just go ahead and write over here Streamlit cloud.

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And now here you'll be able to see that Cloud Streamlit a faster way to build and share data apps.

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But before that, let me go to my GitHub account.

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Now, inside this particular GitHub account, I hope everybody has this particular account itself.

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I will go ahead and create my new repository.

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Right.

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So let me just go ahead and click on New Repository here I'm going to go ahead and write my search engine

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okay.

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Search engine LM.

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I will just go ahead and write like this.

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Then I will add a readme file, and let's say I'm going to add a license of General Public License.

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You can select any one that you like.

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I will go ahead and click on the repository.

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One thing you should understand the reason why I'm using this Streamlit cloud.

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Because you will be able to deploy any number of apps over here.

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And, uh, you can probably go ahead with the specific deployment.

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Now over here inside this particular search engine, I will just go ahead and open my folder.

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This is one okay.

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I will quickly go ahead and select all these files.

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Drag and drop it inside this okay.

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So quickly I will go ahead and drag and drop it okay.

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And I know I don't even require this ipynb because I just require this app.py and requirement dot txt.

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So let me just close this and let me commit the changes.

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So once I commit the changes now I have my entire URL.

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Now quickly let's go to the Streamlit cloud and I will go ahead.

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And first of all sign in here.

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You will be able to deploy any kind of Streamlit web app in a much more easier way.

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Let it be an generative AI application, let it be a machine learning application.

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Anything as such.

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So here you can see some of the examples that I have already uploaded.

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Okay.

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Um, there are a lot of advantages of using this because anyhow, you don't have to pay anything, uh,

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with respect to the deployment.

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And you'll also be able to get one kind of URL.

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But at the end of the day, you'll be having the streamlit.io at the end.

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Okay.

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So I will go ahead and click on create app.

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And uh, it will say that hey, do you already have an app.

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So over here it has an option to integrate with the GitHub.

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So I will go ahead and click this since I have uploaded in the GitHub.

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And as soon as you probably click on GitHub right, it will first of all ask you for the authentication

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okay.

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So please make sure that you do the authentication with your GitHub account.

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You need to have a GitHub account for that.

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Now after this I will just go ahead and search for my GitHub URL over here in my repository.

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So here you can see Krish Nayak zero six search engine LM and this is my branch main.

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I'm in my branch main.

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My main file is app dot p y.

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So I will just go ahead and click over here and hit on app dot p y.

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Please make sure that you need to have a requirement dot.

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TXT file over here.

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Otherwise it will not understand what all uh environment variables or are.

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What?

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All libraries it basically requires, right?

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So you need to have this specific thing installed.

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Now I will go over here and this is the domain that it is showing me.

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I will be able to use with this particular domain.

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I'll be able to use my web app.

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Now along with this, let's say you also need to add some kind of key value pairs, right?

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Let's say you want to add some secret keys.

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You can just go ahead and give a secret key name is equal to whatever name you have.

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You can probably go ahead and display it in this particular manner once you upload it by default.

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Here you can see you are having Python three version 3.11.

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I'll be using this particular version.

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And along with this, uh, any key value pairs like how we have key value pairs over here.

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Right.

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Like OpenAI API key, my lang chain API key lang chain project access token.

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I can also give this particular key by just copying and pasting it over there.

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But in order to access this, you know, in my API, if I'm using this key anywhere, I just need to

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use s t dot secret.

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Okay, I have to use something like this s t dot secret and give my secret key name over here.

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Right?

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With respect to any keys that I'm actually using, which is put inside my Streamlit cloud.

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Let's say if I'm using a chef token, right, I may copy this entire key over here.

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I may paste it over here.

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Okay.

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And then what I will do, I will go ahead and save this.

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Now, you know I'm using chef underscore token.

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Let's say if I go ahead and save this.

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Now here I've just added one secret.

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Now in order to access it in my app Dot Pi, I will just go ahead and write SD dot secrets okay.

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And this how I will be able to access this.

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Okay, so if you really want to know more about it just go ahead and search for Streamlit secrets.

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Okay.

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And here you will be able to see secret management SD dot secrets.

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Right.

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If you probably go ahead and use this.

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So let's say you are given me a open API.

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Open API key.

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You just need to write SD dot secrets with respect to, uh, your open API key.

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Write open API key over here.

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So in your Streamlit app the following values would be true.

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Right?

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Which is equal to your open API key.

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So just by using this particular code you'll be able to use it.

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But right now the application that I've actually created, you'll be able to see.

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And let me just do one thing before I run this.

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Also, I'll remove it because here I don't require any secret key because from the front end I will

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be putting my hugging face API token or any token that is required.

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Okay.

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And here I'm actually using this hugging face endpoint also right to access any kind of things.

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Um, so this is my entire, uh, okay.

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This, uh, I'm in the wrong tool.

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Okay.

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This is my API, right?

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So in this particular case, you'll be able to see that I'm not using any, any, any kind of secret

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key.

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I'm just typing it from the front end.

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Okay.

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My grok API key.

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So now what I'll do I'll just go ahead and deploy this okay.

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So once I deploy this you'll be seeing that in the back end.

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First of all it will go ahead and install all the requirement dot txt first of all right.

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So the deployment is very much simple with the help of Streamlit app.

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Right.

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So now here you'll be able to see that the deployment is basically happening.

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And with respect to the deployment over here you will be also seeing that the requirement dot txt will

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get installed in the back end.

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Okay.

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So once it gets installed you will be able to see your entire application okay.

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So this is the most easiest way to probably do it.

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Uh for before that what I will do quickly I will go to my env file.

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Now let me go back to my env file.

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I will take this grok API key because this is what I will be required in my application.

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Right.

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So again based on the requirement dot txt it will take some amount of time.

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And let's wait for that time to, you know, do the installation of all my libraries and packages that

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I required for that particular project.

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And once it does, my project will be ready.

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Now you can see the requirement dot txt has run.

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And here is my entire Streamlit app right now.

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This is how amazing and easy it is.

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So I'll just go ahead and copy paste my API key over here.

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Once I press enter, I should be able to ask any question.

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Okay, tell me about machine learning.

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Now here I have created my search engine using agents.

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Right.

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So here you can see Wikipedia is just thinking okay then Wikipedia invalid tool name some invalid tool

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name here it is basically coming.

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Search arXiv.

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None is a valid tool.

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Try one.

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So finally you'll be able to see that from search and from RC.

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We are not able to get this information, so it will try to give me the information from uh, machine

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learning.

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Right.

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So machine learning is a field of study in artificial intelligence.

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Lets me do one more thing.

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Let me just go ahead and search for attention is all you need.

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Research paper.

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So here you can see the number.

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It is nothing.

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But, uh, I will just search for this.

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Ah.

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If this particular code.

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Okay, so if I go away and search for it, I should be able to get my answer.

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So now it is going to my arXiv.

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It is probably communicating over here okay.

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And here you can see again uh it is probably and finally, you get this particular input, right?

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Oh, tell me about generative AI.

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I will just go ahead and ask this particular question and see how the cloud is probably providing this

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entirely, completely for free.

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So you can go ahead and probably do this right.

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So here you can see machine learning, generative AI, Wikipedia.

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All the communication is specifically happening.

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Again, we do not have to probably go to arXiv also because this tool will be more than sufficient with

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respect to giving your output right.

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And to know more details about it here you can see the user is asking about machine learning and generative

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AI.

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So both this information, the chat history is also basically being used.

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Right.

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And here you can probably see everything as such.

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Right.

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Um, so I hope uh, you were able to understand this deployment technique, uh, one more, uh, deployment

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mechanism that I would definitely like to show is from hugging Face, which is nothing but this specific

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spaces.

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Okay, so I will show you how you can go ahead and create your own space.

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Now space, uh, hugging face space is something very amazing.

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Hugging face.

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It provides some amount of face with respect.

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Uh, some amount of space or deployment, uh, server for you all so that you will be able to deploy

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your entire Application, and you'll be able to use it completely over here and deploy with the URL

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itself.

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Right.

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So let's say there are so many spaces over here.

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If I go ahead and click on any one of them, right.

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Let's say I will go ahead and click on this.

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Open a leaderboard instead of using a leaderboard.

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Let's see right now everything is running on zero.

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So if I just go ahead and click this right.

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So here you can see ID documentation recognition.

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You can drop this particular image over here right like this.

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And I can probably go ahead and click on Check Document Details.

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And I will be able to get all the information.

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So this entire deployment is specifically happening in the hugging face space itself.

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Right.

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So similarly what I will do is that I will take this entire application and deploy it over there itself,

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and let's see what exactly we'll be able to get it or not Okay.

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But again, if I talk about hugging face spaces, uh, you can read more about it.

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Hugging face spaces.

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Okay, so here you will be able to discover and many people usually upload this.

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Right.

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And you'll also be able to deploy your entire application.

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So that is what we are going to see in the next video.

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So I hope you like this particular video with respect to the deployment.

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I will see you all in the next video.

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Thank you.

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Take care.

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Have a great day.