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In this video, we are going to be taking steps

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to ensure that our backend project is ready

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for us to start developing our endpoints.

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Let's begin.

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First, we need to head into the root of our

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backend folder.

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Here we need to create a virtual environment.

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To do that, we need to open up our terminal.

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Let's go to backend.

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And here we can type the command to create

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our virtual environment.

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Once the environment is created, as you can

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see here we have a new folder, the .vmv

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folder

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that we specified in our command.

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We can now activate this environment.

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For that, you can use the following command

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on macOS or Linux.

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say source.vmv

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bain activate.

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And now, as indicated by this prefix to our

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command prompt,

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we are now in our virtual environment.

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So we can clear all this.

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This activation command is slightly different

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on Windows.

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You don't need to begin with source.

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And you simply replace the bain folder with

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a scripts folder.

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And just as I said earlier, once this virtual

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environment is

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activated, you can see it as the prefix to

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your command prompt.

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Next we'll need to install our dependencies.

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These are the dependencies that are listed

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in our requirements.txt

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file.

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We are going to be installing all of them

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with one single command.

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So let's head over to our command prompt and

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at the root of our backend folder, we'll

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We'll say pip install –r then we target our

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requirements.txt

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file.

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This will begin the installation and in no

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time, we should have all listed packages installed.

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Awesome.

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Now, our installation is done.

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Let's just click out of our terminal for now.

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The next thing we have to do is to have our

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Gemini API key in place and accessible to

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our backend application.

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API key is a very sensitive part of your application

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and you should always make sure it is securely

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stored and accessed.

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This is why we installed the python.env

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package in order to store and access our API

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key from

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an environment variable.

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So let's create a .env

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file at the root of our backend folder.

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Inside this folder, let us add the environment

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variable GeminiAPIKey.

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We'll then set the value of this variable

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to the API key we just created in Google's

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AI Studio.

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Perfect.

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Now save this file and make sure that it is

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ignored in the .gitignore

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file.

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We have already ignored it in the .gitignore

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file that comes with this seed project.

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So you're good on that front, but in your

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other projects, always make sure that you

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never push this .env

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file to a remote repository.

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Our next task is to create the entry point

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Python file to this backend application.

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We are going to be setting up this file so

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that in subsequent videos, we can start writing

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our endpoints right away.

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So let's create the file main.py

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at the root of the backend folder.

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Now inside this file, first, let us take care

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of our imports.

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We'll be starting with our FastAPI dependencies.

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First, let's say from FastAPI, import FastAPI

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file, upload file, and HTTP exception.

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We'll also be adding some middleware for calls

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so I'm going to say from first api.middleware.calls

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From there, we import the calls middleware

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model.

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I think we can put this aside for now.

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Next, let us import the dependencies for working

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with our Gemini API.

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to say from Google, import genai, and from

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Google.genai,

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import types.

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Here we have imported our Gemini Python SDK,

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and also

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some types on the API that we're going

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to be using along the course of building this

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project.

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Finally, let us get in some helpful system

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dependencies

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for loading our environment variables

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working with files.

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Come down here and say from .env,

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let us import the

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load.env

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function and also import the OS package.

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And while we're on that, let us

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call our load.env

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method to load all our environment variables

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from our .env

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file.

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Go down here and say load.env

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and call it.

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Perfect.

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The next thing we're

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going to do is to get a reference to our Gemini

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API key so that we can use it in

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our Gemini API requests.

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So let's do that.

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I'm going to go down a little bit,

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Here, I'm going to say Gemini API key and

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let us get it from the environment.

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After that, let us check if we have it.

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So if not Gemini API key, let us simply raise

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a value error.

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And here, we'll say Gemini API key not found

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in the .env

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file.

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Let us also define the Gemini model we'll

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be using.

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We're setting that using a constant.

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And for this course, we're going to be using

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Gemini 2.5

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Flash, a powerful, fast, multimodal model.

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And we'll wrap up our Gemini setup section

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by creating a new instance of our Gemini API

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client.

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Awesome.

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The next setup we're going to be doing

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is for our FastAPI server.

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So let us go ahead and start that

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by first creating a new instance of the FastAPI

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server.

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We say app equals FastAPI.

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simply passing a title for our application.

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We're going to be calling that the Travel

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Companion AVI.

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Next, let us enable some call settings for

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smooth communication with the front-end.

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down here by adding the middleware, say app.addMiddleware,

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we're passing causeMiddleware, and then impute

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our settings to allow the client to communicate

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with our server without being blocked by any

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calls rules.

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Need to put some quotes around that.

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Say allowCredentials.

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Set that to true.

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is allowMethods, we want to allowAllMethods,

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and finally, allowHeaders, and we also want

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to allowAllHeaders.

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Good.

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Now, this is for a demo application in production.

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You want to make sure that you have stricter

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rules for calls.

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But for this application, this is simply great.

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Now we can start writing our endpoints.

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So we'll be wrapping up this video by creating

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a simple base endpoint and running our backend

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application to see that it's good to go.

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So let's write a simple root endpoint that

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simply spits out a generic message.

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We'll just scroll down and here we'll say

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app.get,

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define our root endpoint and define

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the handler that is going to undo that.

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Say with root and here we can just simply

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return a message wrapped in a dictionary and

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And this will say Travel Companion API is

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running.

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Good.

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Now we have our route endpoint.

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But to run this application, we need to write

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the code that allows us to run our server

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through this file.

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So let us do that.

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We're going to be making use of UVicon.

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But first, let us make sure that we are checking

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that this file is what we are running directly

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from our terminal, say ifname="main", then

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we simply import uvconn and we can use uvconn

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to run this app and set the host to 0.0.0.0

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and our port to 8000.

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Awesome.

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Now, with this in place, we can go to our

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terminal and run our server.

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So let us pull up the terminal.

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Make sure that you are still at the root of

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the backend folder.

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And here we can simply run python main.py

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hit enter.

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And now that our server is running, let's

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simply visit our base URL in the browser.

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So here in the browser, we can simply say

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localhost.

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Our server is running at the port 8000.

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So we say colon 8000 and hit enter.

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And here you can see the message travel companion

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API is running.

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How smooth is that?

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I think we should also have a docs endpoint.

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So we say slash docs.

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And here as you can see, we have our root

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endpoint.

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Awesome.

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So we have our server running and we now have

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everything that we need to start building

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our application.