GSP1229

Overview

In this lab, you will learn how to build a generative AI application using the Gemini API in Vertex AI and deploying it on Cloud Run. You'll use the Streamlit framework to create an interactive interface for generating stories.

The lab involves running the application locally in Cloud Shell to test its functionality and then deploying it to Cloud Run for scalable serving. You'll gain practical experience integrating Gemini with a user interface and leveraging Cloud Run for efficient deployment.

Gemini

Gemini is a family of powerful generative AI models developed by Google DeepMind, capable of understanding and generating various forms of content, including text, code, images, audio, and video.

Gemini API in Vertex AI

The Gemini API in Vertex AI provides a unified interface for interacting with Gemini models. This allows developers to easily integrate these powerful AI capabilities into their applications. For the most up-to-date details and specific features of the latest versions, please refer to the official Gemini documentation.

Gemini Models

• Gemini Pro: Designed for complex reasoning, including:
  • Analyzing and summarizing large amounts of information.
  • Sophisticated cross-modal reasoning (across text, code, images, etc.).
  • Effective problem-solving with complex codebases.
• Gemini Flash: Optimized for speed and efficiency, offering:
  • Sub-second response times and high throughput.
  • High quality at a lower cost for a wide range of tasks.
  • Enhanced multimodal capabilities, including improved spatial understanding, new output modalities (text, audio, images), and native tool use (Google Search, code execution, and third-party functions).

Prerequisites

Before starting this lab, you should be familiar with:

• Basic Python programming.
• General API concepts.
• Running Python code in a Jupyter notebook on Vertex AI Workbench.

Objectives

In this lab, you will learn how to:

• Integrate Gemini API in Vertex AI with applications
• Build and deploy the developed sample application on Google Cloud Run
• Use the Streamlit framework to build a Cloud Run application

Setup and requirements

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources will be made available to you.

This hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that you use to sign in and access Google Cloud for the duration of the lab.

To complete this lab, you need:

• Access to a standard internet browser (Chrome browser recommended).

Note: Use an Incognito or private browser window to run this lab. This prevents any conflicts between your personal account and the Student account, which may cause extra charges incurred to your personal account.

• Time to complete the lab---remember, once you start, you cannot pause a lab.

Note: If you already have your own personal Google Cloud account or project, do not use it for this lab to avoid extra charges to your account.

How to start your lab and sign in to the Google Cloud console

1. Click the Start Lab button. If you need to pay for the lab, a pop-up opens for you to select your payment method. On the left is the Lab Details panel with the following:

   • The Open Google Cloud console button
   • Time remaining
   • The temporary credentials that you must use for this lab
   • Other information, if needed, to step through this lab

2. Click Open Google Cloud console (or right-click and select Open Link in Incognito Window if you are running the Chrome browser).

   The lab spins up resources, and then opens another tab that shows the Sign in page.

   Tip: Arrange the tabs in separate windows, side-by-side.

   Note: If you see the Choose an account dialog, click Use Another Account.

3. If necessary, copy the Username below and paste it into the Sign in dialog.

   {{{user_0.username | "Username"}}}

   You can also find the Username in the Lab Details panel.

4. Click Next.

5. Copy the Password below and paste it into the Welcome dialog.

   {{{user_0.password | "Password"}}}

   You can also find the Password in the Lab Details panel.

6. Click Next.

   Important: You must use the credentials the lab provides you. Do not use your Google Cloud account credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.

7. Click through the subsequent pages:

   • Accept the terms and conditions.
   • Do not add recovery options or two-factor authentication (because this is a temporary account).
   • Do not sign up for free trials.

After a few moments, the Google Cloud console opens in this tab.

Note: To view a menu with a list of Google Cloud products and services, click the Navigation menu at the top-left.

Task 1. Run the Application Locally (in Cloud Shell)

In this section, you will run the Streamlit application locally in Cloud Shell.

Clone the Repository

1. Open a new Cloud Shell terminal by clicking on the Cloud Shell icon in the top right corner of the Cloud console.

2. Run the following commands to clone the repo and navigate to gemini-streamlit-cloudrun directory in Cloud Shell using the following commands.

git clone https://github.com/GoogleCloudPlatform/generative-ai.git --depth=1 cd generative-ai/gemini/sample-apps/gemini-streamlit-cloudrun

To run the Streamlit Application, you will need to perform some additional steps.

Run the application

1. Setup the Python virtual environment and install the dependencies:

python3 -m venv gemini-streamlit source gemini-streamlit/bin/activate pip install -r requirements.txt

2. Your application requires access to two environment variables:

• GCP_PROJECT : This the Google Cloud project ID.
• GCP_REGION : This is the region in which you are deploying your Cloud Run app. For e.g. us-central1.

These variables are needed since the Vertex AI initialization needs the Google Cloud project ID and the region. The specific code line from the app.py function is shown here: vertexai.init(project=PROJECT_ID, location=LOCATION)

In Cloud Shell, execute the following commands:

GCP_PROJECT='{{{ project_0.project_id | Project ID}}}' GCP_REGION='{{{ project_0.default_region | Region }}}'

3. To run the application locally, execute the following command.

streamlit run app.py \ --browser.serverAddress=localhost \ --server.enableCORS=false \ --server.enableXsrfProtection=false \ --server.port 8080

Output:

Collecting usage statistics. To deactivate, set browser.gatherUsageStats to False. You can now view your Streamlit app in your browser. URL: http://localhost:8080

4. The application will startup and you will be provided a URL to the application. Click the link to view the application in the browser or use Cloud Shell's web preview function to launch the preview page.

5. Adjust the parameters for the story generation and click Generate my story.

6. Navigate back to Cloud Shell and "AUTHORIZE" the application to access the Gemini API when prompted. Once you have authorized the application, you can navigate back to the application to see the response.

Note: The application will prompt the Gemini API in Vertex AI and display the responses. In order for this to work, after selecting Generate my story you will need to navigate back to Cloud Shell to authorize the application to access the Gemini API.

6. After you have finished testing the application, you can stop the application by entering Ctrl + C in Cloud Shell.

Task 2. Build and Deploy the Application to Cloud Run

In this section, you will deploy the Streamlit Application in Cloud Run.

You will now build the Docker image for the application and push it to Artifact Registry. To do this, you will need one environment variable set that will point to the Artifact Registry name. The commands below will create this Artifact Registry repository for you.

Note: This step will take several minutes to complete.

1. In Cloud Shell, execute the following command:

AR_REPO='gemini-repo' SERVICE_NAME='gemini-streamlit-app' gcloud artifacts repositories create "$AR_REPO" --location="$GCP_REGION" --repository-format=Docker gcloud builds submit --tag "$GCP_REGION-docker.pkg.dev/$GCP_PROJECT/$AR_REPO/$SERVICE_NAME"

Output:

DONE -------------------------------------------------------------------------------- ID CREATE_TIME DURATION SOURCE IMAGES STATUS a601ffd1-c282-43d2-942c-53cc13f43bf2 2023-12-18T11:37:30+00:00 2M29S gs://qwiklabs-gcp-00-eb090e9513e8_cloudbuild/source/1702899440.87287-549e1a0cc5644b3c9535ff57f4a63d02.tgz us-central1-docker.pkg.dev/qwiklabs-gcp-00-eb090e9513e8/gemini-repo/gemini-streamlit-app (+1 more) SUCCESS

2. The final step is to deploy the service in Cloud Run with the image that we had built and had pushed to the Artifact Registry in the previous step.

gcloud run deploy "$SERVICE_NAME" \ --port=8080 \ --image="$GCP_REGION-docker.pkg.dev/$GCP_PROJECT/$AR_REPO/$SERVICE_NAME" \ --allow-unauthenticated \ --region=$GCP_REGION \ --platform=managed \ --project=$GCP_PROJECT \ --set-env-vars=GCP_PROJECT=$GCP_PROJECT,GCP_REGION=$GCP_REGION

On successful deployment, you will be provided a URL to the Cloud Run service. You can visit that in the browser to view the Cloud Run application that you just deployed.

Output:

⠼ Deploying new service... Done. ✓ Deploying new service... Done. Done. Service [gemini-streamlit-app] revision [gemini-streamlit-app-00001-srg] has been deployed and is serving 100 percent of traffic. Service URL: https://gemini-streamlit-app-hc2gb6hsia-uc.a.run.app

Choose the functionality that you would like to check out and the application will prompt the Gemini API in Vertex AI and display the responses.

Click Check my progress to verify the objective. Build and Deploy the Application to Cloud Run

Congratulations!

Congratulations! In this lab, you have learned how to integrate Gemini API in Vertex AI with applications and build and deploy the developed sample application on Google Cloud Run.

Next steps / learn more

Check out the following resources to learn more about Gemini:

• Gemini Overview
• Generative AI on Vertex AI Documentation
• Generative AI on YouTube
• Generative AI Official Repo
• Example Gemini Notebooks

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Manual Last December 27, 2024

Lab Last Tested October 24, 2024

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