Develop an app with Gemini assistance

Summary

In this video, a developer utilizes Gemini, an AI assistant integrated into VS Code, to streamline the process of building and deploying a containerized inventory app on Google Cloud. With Gemini's guidance, the developer selects Cloud Run for its ease of use and receives step-by-step instructions to set up a sample app. The AI breaks down Docker files and Flask app code, assists in generating sample JSON data, and helps write a function to access this data. After local testing with Cloud Run's emulator, the app is successfully deployed to the cloud, showcasing Gemini's ability to enhance productivity in unfamiliar platforms.

Takeaways

• 💻 The developer utilizes Gemini within VS Code to streamline the development process and minimize context switching.
• 🚀 Gemini provides a concise breakdown of tooling and platforms for container deployment, aiding in decision-making.
• 🌐 The developer opts for Google Cloud Run due to its managed infrastructure, which reduces the need for direct management.
• 🔍 Gemini offers step-by-step instructions for setting up a Cloud Run app using Google Cloud's Cloud Code IDE extension.
• 📚 The script includes a Dockerfile, which Gemini breaks down layer by layer for better understanding.
• 🛠️ Gemini helps in understanding the application code, particularly the 'hello' function within a Flask app.
• 🔑 The developer learns about accessing environment variables in Cloud Run through Gemini's explanations.
• 📝 Gemini generates sample JSON data with specified attributes to assist in adding inventory functionality.
• 🔄 The developer integrates the generated data directly into the project, enhancing workflow efficiency.
• 🔑 Gemini assists in writing a function to access inventory data within the Flask app, using natural language prompts.
• 📡 Gemini provides guidance on using a local emulator for Cloud Run to test the app with local environment variables.
• 🚀 Final deployment to Cloud Run is achieved following Gemini's instructions, resulting in a public URL for the app.

Q & A

What is the primary goal of the developer in the script?

The developer aims to build and deploy a simple inventory app to Google Cloud using containers, with the help of Gemini to enhance productivity.

Why does the developer choose to use Gemini in their development process?

Gemini is integrated into the developer's local IDE, VS Code, which helps reduce context switching and provides immediate assistance in building and deploying the app.

What platform does the developer decide to use for deploying the app based on Gemini's response?

The developer chooses Google Cloud Run because it allows for container deployment without the need to manage infrastructure.

How does Gemini assist the developer in understanding the Docker file in the example?

Gemini provides a layer-by-layer breakdown of the Docker file's contents, explaining each part and highlighting the entry point of the application.

What is the role of the 'app.py' file in the context of the Docker file?

The 'app.py' file is identified as the entry point of the application within the Docker file, which is crucial for the app's execution.

How does Gemini help the developer understand the Flask application code?

Gemini uses natural language explanations to shed light on the 'hello' function in the Flask app, including details about environment variables used in Cloud Run.

What feature does the developer want to add to the Flask app template?

The developer wants to add inventory functionality to the Flask app template to manage inventory data.

How does Gemini assist in generating sample data for the inventory functionality?

Gemini generates JSON data with specified attributes based on the developer's requirements and allows the data to be directly added to a new file in the project.

What method does Gemini use to help the developer write a function to access inventory data in the Flask app?

The developer uses comments to prompt Gemini to generate the necessary code, which is then reviewed and integrated into the Flask app.

How does the developer test the app locally before deploying to Cloud Run?

The developer uses a local emulator for Cloud Run to test the app, ensuring that it works correctly with the specified environment variables before cloud deployment.

What final step does the developer take to deploy the app to the cloud using Cloud Run?

The developer follows Gemini's instructions to deploy the app to Cloud Run, resulting in a public URL where the app can be accessed.

Outlines

00:00

💻 Developing with Gemini in VS Code

A developer seeks to build and deploy an inventory app on Google Cloud using containers, a standard adopted by their team. They utilize Gemini, an AI assistant integrated into VS Code, to streamline the development process. Gemini provides a concise breakdown of tooling and platforms for container deployment, highlighting Cloud Run as an ideal choice due to its infrastructure management capabilities. The developer requests an example to bootstrap the project and receives step-by-step instructions from Gemini, including setting up a local environment and understanding the Docker file and application code with the help of Gemini's explanations.

05:02

🚀 Rapid Deployment with Gemini's Assistance

The developer continues to enhance the app by adding inventory functionality, requesting sample JSON data from Gemini with specific attributes. Gemini generates the data, which the developer seamlessly integrates into a new file. They then ask Gemini to assist in writing a function to access this data within their Flask app, using comments to prompt code generation. After testing the app locally with the help of a Cloud Run emulator, the developer successfully deploys the app to the cloud, following Gemini's clear instructions, and is able to access it via a public URL. The developer reflects on their productivity, having gone from concept to deployment in an unfamiliar platform within minutes, all within the IDE.

Keywords

💡Developer

A developer is a professional who designs, codes, and maintains applications or systems. In the video's context, the developer is the main character who is building an inventory app. The script mentions the developer's need to build and deploy the app, showcasing the role's responsibilities in software creation.

💡Google Cloud

Google Cloud is a suite of cloud computing services offered by Google. It is the platform the developer chooses for deploying the inventory app. The script highlights Google Cloud's role as the hosting environment, emphasizing the importance of cloud services in modern application deployment.

💡Gemini

Gemini appears to be an AI assistant or tool within the video that aids the developer in building and deploying the app. It provides guidance, explanations, and code generation, illustrating the utility of AI in enhancing productivity and reducing the learning curve for new technologies.

💡VS Code

VS Code stands for Visual Studio Code, a popular source-code editor developed by Microsoft. The script mentions it as the local IDE where the developer interacts with Gemini, indicating the prevalence of VS Code in the developer's workflow and its integration with AI tools like Gemini.

💡Containers

Containers are a standard unit of software that packages code and all its dependencies so the application runs quickly and reliably from one computing environment to another. The developer's team has standardized on containers, which is why the script discusses building and deploying the app in a containerized format.

💡Cloud Run

Cloud Run is a serverless compute platform on Google Cloud that automatically scales up or down depending on the traffic, allowing developers to focus on code rather than infrastructure management. The script highlights Cloud Run as the chosen platform for deploying the app due to its serverless nature.

💡Cloud Code

Cloud Code is a Google Cloud extension for VS Code that simplifies the process of building, deploying, and managing applications on Google Cloud. The script refers to it as a tool for bootstrapping the Cloud Run app, demonstrating the integration of development tools with cloud services.

💡Dockerfile

A Dockerfile is a text document that contains all the commands a user could call on the command line to assemble an image. In the script, the Dockerfile is mentioned as the tool used to build the container for the inventory app, showing the importance of containerization in modern app deployment.

💡Flask

Flask is a lightweight WSGI web application framework in Python that is used to develop web applications and APIs. The script mentions 'app.py', which is likely a Flask application, as the entry point of the container, indicating the use of Flask to create the web server for the inventory app.

💡Environment Variables

Environment variables are a set of dynamic values that can affect the way running processes will behave on a computer. In the video, the developer learns about accessing useful information stored in environment variables in Cloud Run, illustrating how configuration can be managed outside the application code.

💡JSON

JSON (JavaScript Object Notation) is a lightweight data-interchange format that is easy for humans to read and write and easy for machines to parse and generate. The script describes the developer requesting JSON sample data with specific attributes, showing JSON's role in data representation and API communication.

Highlights

Developer utilizes Gemini within VS Code to build and deploy an inventory app on Google Cloud, reducing context switching.

Team has standardized on containers, prompting the developer to seek Gemini's help for container-based deployment.

Gemini provides a concise breakdown of tooling and platforms for container deployment, highlighting Cloud Run.

Developer chooses Cloud Run due to its managed infrastructure, avoiding the need for direct management.

Gemini offers step-by-step instructions for deploying a Cloud Run app using Cloud Code, Google Cloud's IDE extension.

Developer clones a sample app and uses Gemini to understand the deployment process from a readme page.

Gemini breaks down the Docker file contents layer by layer, explaining the entry point as 'app.py'.

Developer requests Gemini to explain the 'hello' function in the Flask app, gaining insights into code structure.

Gemini's explanation helps the developer learn about accessing useful environment variables in Cloud Run.

Developer seeks Gemini's assistance to add inventory functionality to the app template.

Gemini generates sample JSON data with specified attributes, which the developer integrates directly into a new file.

Developer asks Gemini to write a function to access the inventory data within the Flask app.

Gemini generates code for a route in the Flask app, specifying the GET method and returning inventory data.

Developer tests the app locally, seeking Gemini's guidance on using a local emulator for Cloud Run.

Gemini provides clear steps to deploy the app to the Cloud Run local emulator, which the developer successfully follows.

Developer confirms the app is running locally on port 8080, with the emulator setting the Cloud Run environment variable to 'local'.

Gemini's generated code successfully returns inventory data, which the developer verifies in the local app.

Developer follows Gemini's instructions to deploy the app to Cloud Run in the cloud, achieving a public URL for the app.

Developer recaps the productivity achieved with Gemini, going from idea to deployment in an unfamiliar platform within minutes, all within the IDE.

Transcripts

I'm a developer who needs to build and

deploy a simple inventory app to Google Cloud.

So let's see how I can use Gemini to be more productive.

The first thing I notice is that chatting with Gemini is available right here in my

local IDE VS Code, which helps reduce context switching.

Now I don't know much about Google Cloud but

I do know that my team has standardized on containers.

So let's see if Gemini can help me figure out how to build and

deploy this app in a container.

I love that Gemini's response breaks down tooling and platforms for building and

deploying containers packed with just enough information for

me to be able to make a decision.

Cloud run jumps off the page for me in Gemini's response as I don't want to have

to manage any infrastructure so let's go with that.

Since I don't know cloud run, I'd actually love an example to use to bootstrap and

to stay in context as much as possible.

Let's see if Gemini can tell me how to get a Cloud Run app running using

Google Cloud's IDE extension Cloud Code.

We get step-by-step instructions back from Gemini.

So let's step through them.

[MUSIC]

Okay, looks like I got the sample cloned to my environment and

I've landed on a readme page that talks about deployment.

But first lets see if Gemini can help me quickly understand the contents

of the example.

I know containers are often built using a Docker file, and I see one here.

So let's actually ask Gemini to break it down.

[MUSIC]

By highlighting the contents of the Docker file, right-clicking, and

selecting Explain this.

It looks like Gemini gave me a layer by layer breakdown of what is in the Docker

file and I see in Gemini's response that our entry point is actually app py.

So let's jump over to see that file and

see if Gemini can help us understand the application code.

Now, I know there are already comments in the example Docker file, but

I was still pretty happy with Gemini's explanation and breakdown.

So lets see if it can actually shed some light

on the hello function in this flask app.

Sweet, with Gemini's explanation, I'm able to quickly learn about the code using

natural language, including learning about some useful info that's stored in

environment variables that my app can access in Cloud Run.

Now, I want to start experimenting with adding inventory functionality to this

template.

And since I know I'll need to write logic to access inventory data,

let's ask Gemini for some sample data.

I gave Gemini two specifications that it should be JSON with two specific

attributes.

And not only was Gemini able to generate this data, but

I'm also able to take that data from the Gemini interface and

add it directly to this new file in the spirit of speeding up my workflow.

Now I'm wondering if Gemini can actually help me write a function to access this

data directly from my Flask app.

Ill start by modifying my import statements in the flask app for

what I think we might need.

I read that I can use comments to prompt Gemini to generate code for me, so

im going to paste in my requirements, highlighting them and

select generate code.

Ok I think this is what I wanted.

The route looks good, were specifying the get method and

were returning the inventory data using jsonify.

I guess the next thing to do is test the app out.

I'll test it locally first.

And since there are some Cloud Run environment variables that we're

accessing, I actually want to see if there's a local emulator for Cloud Run.

Lets ask Gemini.

Yep there is.

Now can Gemini tell me how to use it?

Just what I needed.

Gemini responded with a couple of clear steps to follow to deploy to the cloud run

local emulator.

So lets follow them.

Seems to have completed I think the app is now running and

exposed on my local machine at port 8080.

Let's open this up in a simple browser, and the app is up and running.

I can see that the emulator has specified the cloud run environment variable to be

local which makes sense.

Now let's see if the code that Gemini gave us to return

inventory data is working and it looks good to me.

The last thing I want to do is figure out how I can actually deploy this to

the cloud in cloud run.

And now let's follow Gemini's instructions on how to do this.

[MUSIC]

Looks like I now have a public URL where I can access the app running in

the cloud on cloud run and it looks good to me.

To recap, with Gemini I was able to be incredibly productive going from idea to

deployment in a platform I was not all too familiar with,

all in a matter of minutes and without ever leaving my ide.