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In this lesson, we are going to talk about some basic, uh, concepts around Streamlit.

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So the first thing we need to highlight is that when a we are working with level two applications,

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we are going to be using code editors, not Jupyter notebooks.

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So if you remember in the A in the previous phase when we were building level one applications, we

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were using Jupyter notebooks.

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Jupyter notebooks are very useful tools for us because they allow us to write code and also explanatory

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text in a in a very easy way.

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So they are very good, you know, for explaining a new, new, new things to, to students.

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But when we start progressing towards more professional applications, we are going to use more the

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code editors and less the Jupyter notebooks.

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So in this block of a level two applications, we are going to use a code editor a in our case we use

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Visual Studio Code.

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This is a free editor, free code editor from Microsoft.

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And this is the one we recommend you because a a a a.

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First is the most used, uh, overall in the in the, in the real world.

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So it is very probable that if you are applying for a job in, in, in a company, they are using Visual

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Studio Code.

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And the second reason why we recommend Visual Studio Code is that Visual Studio Code has now some advantages.

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Uh, in a compared with other editors.

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Uh, especially in the area of artificial intelligence assistance.

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So if you remember, we have been talking about ChatGPT before and the value it is going to have for

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you in this stage, in this stage, in this learning stage.

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Uh, as an code assistant and general assistant, if you want, we use GPT four on a daily basis.

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But there are other artificial intelligence tools that software engineers use while working on a daily

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basis, and one of the most popular ones is GitHub, a co-pilot.

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So GitHub Copilot in some cases is like an alternative to ChatGPT for.

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So some people use ChatGPT for, some people use GitHub Copilot.

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But the more professional you are and the more a, uh, hardcore, um, software engineer you are,

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usually the more you are going to use GitHub copilot, unless you are going to use ChatGPT for.

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So just because of that, we recommend Visual Studio Code, okay.

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Because you are going to see that in your job interviews.

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It's going to be better for you to have experience with Visual Studio Code.

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And because it is extensible.

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So it can use other tools like a GitHub copilot.

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So remember, we don't have any kind of sponsor or sponsorship deal or whatever with any brand.

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We only recommend you the tools and the brands we think are the best for you in terms of quality and

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in terms of cost.

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Okay.

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So in this case our recommendation is going to be Visual Studio Code.

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Visual Studio Code is free.

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You can download it on the internet.

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You have any doubt or problems.

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Just go to ChatGPT four and ask there, uh, whatever.

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Second important thing about the Streamlit apps we are going to build in this stage.

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The structure of these apps is extremely simple.

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They only have three files.

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They have one file where you, uh, store the main logic of the application, and you will see that

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this main file is going to be very similar to the the notebooks you have been writing in the, in the

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level one applications.

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Uh, in this case, uh, instead of having a notebook, we are going to have a, uh, file.

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It's going to be a Python file.

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And the name of the Python file is going to be main dot pi.

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Pi pi Pi is the ending we use for the Python files.

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Okay.

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So you open your Visual Studio Code and you create a new file called main dot pi.

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You will not have to do this for the exercises.

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For the exercises, you just open your Visual Studio code and open the the exercises files we provide

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you.

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So you will see in each of the apps you have a main.py, a file, a requirements.txt file, and a dot

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m file.

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Well, in case the.

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In this case, the dot m file is not going to be in each of the exercises, the dot m file should be

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in the root folder, and you will have to create your own dot m file with your own credentials.

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We have provided a sample of the dot m file in the GitHub repository, but as you remember, this is

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just a sample a only with the name of the of the credentials, but not with the credentials itself,

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but just for you to have a sense of the the format that you will have to use.

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So about the structure of the project.

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He's going to have a Streamlit application is going to have three files the Dotenv, the Main.py and

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the requirements dot txt.

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The last one, the requirements.txt, is where we are going to enter the name of the packages that it

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will need to download in order to make the level two application work.

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Okay, so instead of a installing the modules, first we include the modules in the requirements.txt

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file.

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And when you execute the Streamlit application these modules are automatically loaded.

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Okay.

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So in this file requirements.txt is where we can be more or less careful.

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If we are less careful as we are whenever we work with a demo is something that we are not going to

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dedicate a lot of time, we just write the name of the package.

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So if we want to, uh, if we want to, uh, load the long chain OpenAI package, we will say long chain

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OpenAI.

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But here in the requirements.txt, you can be more careful, more specific, and include exactly the

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version of the package you want.

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Okay, so this is what we do in the professional applications.

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We are very careful about the version of each package we load in our application, because we don't

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want to use different, uh, versions of our packages because this may break your application.

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Okay.

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But in the case of our exercises, these are just very simple demos.

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We are just going to use the name of each package as you will see.

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So remember in this exercises the structure of the level two application is going to have two files.

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Because the dot m file is supposed to be in the root folder.

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Okay a but you will need to have a dot m file for most of these level two applications, because you

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will need the OpenAI K in the in most of the cases.

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And this OpenAI K is your OpenAI K.

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And you should store this OpenAI K in a dot m file that you create.

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So remember basic structure that open file requirements.txt and the main.py which is the the file with

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the main logic of the application.

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Don't worry.

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We are going to see this in practice in the next lessons.

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But this just a quick introduction about the basic structure of a Streamlit application.

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Second thing about the Streamlit operations.

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In the attached document, we are going to provide the URL where you can find the quick introduction

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that extremely provides.

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And this is the best way, uh, for you to have your initial experience with Streamlit there.

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You will learn that extremely.

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It is very simple, very basic, very easy and quick to to learn.

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And you will see that the main things we are going to use in our applications are just 4 or 5 things

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we are going to use some titles we are going to use some sidebars, we are going to use some forms and

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some buttons and that's it.

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So you will see good examples in a very, very easy and quick tutorial in Streamlit.

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In the URL we provide.

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You will see there as well that you can run your Streamlit application locally in your computer and

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also in the cloud.

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In the Streamlit Cloud, the Streamlit cloud is free and is very convenient because it's where you can

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share your Streamlit application with others, so you can provide the URL of the Streamlit application

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in the cloud, and any other person you know from wherever they are.

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They can access this Streamlit app from the internet.

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Right.

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So this is this is very good.

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So you will see that there is a way to execute an extremely application from your computer.

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And there is a different way to execute a Streamlit application when it is in the cloud.

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We will tell you a little bit more about that later, but you are going to see this in the Streamlit

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tutorial and we give you this link in purpose instead of explaining all the details, uh, ourselves.

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Why?

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Because this is the way and, uh, artificial intelligence engineer is going to work in the real world.

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So the same, uh, way that we recommended you to work with GPT four.

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And in many cases, we are going to tell you, you have any doubt or any problem, just go to chat GPT

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four and ask.

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We are recommending you in this moment to go to the Streamlit documentation page and take a look at

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their own tutorial.

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You will see it's very basic, very easy to understand, but you will also realize the way we artificial

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intelligence engineers work every day.

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So whenever you every day, every day, we are faced with new tools, uh, with new ways to solve problems,

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with new lessons that we need to learn, new frameworks, etc..

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And one of the things we do on a daily basis is know new frameworks, new new devices, go to their

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documentation page and understand how they work.

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So this is something you are going to do on a daily basis.

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So it's a very good occasion for you to start, uh, trying uh, this process with extremely.

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A some very quick notes about terminal operations with Streamlit application.

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So if you execute your Streamlit application from your computer and the the main instructions to do

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so will be from the terminal.

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Remember we have been talking about the terminal in a previous lesson in this case a to run a Streamlit

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application, you only need 3 or 4 uh instructions in the terminal.

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First you are going to create a virtual environment.

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This is optional but highly recommended.

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Then you are going to install the packages that are in the requirements.txt file.

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And you will see that this is very easy.

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You go to the folder where with where the Streamlit application is and you just write pip install a

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requirements.txt in your terminal and that's it.

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Well you will see the, the, the, the instructions in the attached document is pip install hyphen

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r requirements.txt.

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Exactly.

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And finally you will execute the application by writing a Streamlit run.

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Main.py.

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That's it.

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So super simple as always your operations in terminal.

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Finally, a quick note about loading the Streamlit app in the cloud so extremely it provides a free

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hosting service for you.

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These hosting services is good but is not excellent.

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Why do we say that?

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Because this a cloud hosting is like temporary.

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So for example, when you uh, when you visit, uh, some of the apps we provide in this section.

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You will see that we are going to provide a link so you can check these applications online.

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And in some cases you will see that when you try to access the application in the Streamlit cloud,

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you are going to see some kind of a screen telling you that the app is sleeping.

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Why is that?

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Because Streamlit has a very nasty way to, uh uh, drop your apps when, uh, they have a low number

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of users.

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And if they don't have a lot of activity, which is in most cases when you are working with demos,

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uh, your user is going to find this screen saying, oh, you know, this app is sleeping.

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If you want to wake up this app, click here.

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So it's not very professional, but it's the way they have, uh, to save, you know, uh, space and

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computing power etc..

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So the service is free.

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So it's it's okay.

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Right.

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So in order to publish your app on Streamlit Cloud, after you have built a Streamlit app, a to show

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it off to the world, you can use the Streamlit Community Cloud to deploy, manage and share your application

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for free.

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And you will see it works in three simple steps.

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First, you put your app in a public GitHub repository.

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Second, you sign into the Streamlit Community Cloud.

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And finally, you just click deploy an app and then paste in your GitHub URL.

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Okay, so very simple.

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You just load your application from your computer to your GitHub repository.

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You just sign in into the Streamlit Community Cloud.

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You integrate your GitHub account with the Streamlit account.

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And then you can select whatever project, uh, to deploy into the Streamlit cloud from your GitHub

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repository.

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Very easy.

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In case of doubt, you you will have very clear, uh, step by step instructions in the Streamlit tutorial.

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And also remember there you have ChatGPT for to help you with whatever doubt you may have in the process.

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So in this lesson, we have been talking briefly about Streamlit and the initial concepts around Streamlit.

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In the next lesson, we are going to provide you the links to the live applications, the Live the Life

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level two applications on Streamlit.

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We are also going to provide you the GitHub links to these level two applications code.

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And finally we will go one by one.

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2%.

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The code and the demo for each of these level two applications.