wehub-resource-sync
108 lines
4.7 KiB
Markdown
108 lines
4.7 KiB
Markdown
# Mesop application using Gemini API in Vertex AI on Cloud Run
|
|
|
|
| | |
|
|
| --------- | --------------------------------------------- |
|
|
| Author(s) | [Hussain Chinoy](https://github.com/ghchinoy) |
|
|
|
|
<!-- markdownlint-disable MD036 -->
|
|
|
|
**YouTube Video: How to build a Gemini powered Mesop app**
|
|
|
|
<!-- markdownlint-enable MD036 -->
|
|
|
|
<!-- markdownlint-disable MD033 -->
|
|
<a href="https://www.youtube.com/watch?v=KUfPiSUJrwE&list=PLIivdWyY5sqJio2yeg1dlfILOUO2FoFRx" target="_blank">
|
|
<img src="https://img.youtube.com/vi/KUfPiSUJrwE/maxresdefault.jpg" alt="How to build a Gemini powered Mesop app" width="500">
|
|
</a>
|
|
<!-- markdownlint-enable MD033 -->
|
|
|
|
This application demonstrates a [Mesop](https://github.com/google/mesop) UI framework application running on Cloud Run.
|
|
|
|
Sample screenshots and video demos of the application are shown below:
|
|
|
|
## Application screenshots
|
|
|
|
|
|
|
|
## Run the Application locally (on Cloud Shell)
|
|
|
|
> NOTE: **Before you move forward, ensure that you have followed the instructions in [SETUP.md](../SETUP.md).**
|
|
> Additionally, ensure that you have cloned this repository and you are currently in the `gemini-mesop-cloudrun` folder. This should be your active working directory for the rest of the commands.
|
|
|
|
To run the Mesop application locally (on Cloud Shell), we need to perform the following steps:
|
|
|
|
1. Set up the Python virtual environment and install the dependencies:
|
|
|
|
In Cloud Shell, execute the following commands:
|
|
|
|
```bash
|
|
python3 -m venv gemini-mesop
|
|
. gemini-mesop/bin/activate
|
|
pip install -r requirements.txt
|
|
```
|
|
|
|
2. Your application requires access to two environment variables:
|
|
|
|
- `GOOGLE_CLOUD_PROJECT` : This the Google Cloud project ID.
|
|
- `GOOGLE_CLOUD_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.
|
|
|
|
In Cloud Shell, execute the following commands:
|
|
|
|
```bash
|
|
export GOOGLE_CLOUD_PROJECT=$(gcloud config get project) # this will populate your current project ID
|
|
export GOOGLE_CLOUD_REGION='us-central1' # If you change this, make sure the region is supported.
|
|
```
|
|
|
|
3. To run the application locally, execute the following command:
|
|
|
|
In Cloud Shell, execute the following command:
|
|
|
|
```bash
|
|
mesop --port 8080 main.py
|
|
```
|
|
|
|
The application will start up and you will be provided a URL to the application. Use Cloud Shell's [web preview](https://cloud.google.com/shell/docs/using-web-preview) function to launch the preview page. You may also visit that in the browser to view the application. Choose the functionality that you would like to check out and the application will prompt the Gemini API in Vertex AI and display the responses.
|
|
|
|
## Build and Deploy the Application to Cloud Run
|
|
|
|
To deploy the Mesop Application in [Cloud Run](https://cloud.google.com/run/docs/quickstarts/deploy-container), we need to perform the following steps:
|
|
|
|
1. Your Cloud Run app requires access to two environment variables:
|
|
|
|
- `GOOGLE_CLOUD_PROJECT` : This the Google Cloud project ID.
|
|
- `GOOGLE_CLOUD_REGION` : This is the region in which you are deploying your Cloud Run app. For e.g. us-central1.
|
|
|
|
These variables are needed since the Vertex AI initialization needs the Google Cloud project ID and the region.
|
|
|
|
In Cloud Shell, execute the following commands:
|
|
|
|
```bash
|
|
export GOOGLE_CLOUD_PROJECT=$(gcloud config get project) # Use this or manually change this
|
|
export GOOGLE_CLOUD_REGION='us-central1' # If you change this, make sure the region is supported.
|
|
```
|
|
|
|
2. Build and deploy the service to Cloud Run:
|
|
|
|
In Cloud Shell, execute the following command to name the Cloud Run service:
|
|
|
|
```bash
|
|
export SERVICE_NAME='mesop-gemini' # this is the name of our Application and Cloud Run service. Change this if you'd like to.
|
|
```
|
|
|
|
In Cloud Shell, execute the following command:
|
|
|
|
```bash
|
|
gcloud run deploy $SERVICE_NAME \
|
|
--source . \
|
|
--port=8080 --allow-unauthenticated \
|
|
--project=$GOOGLE_CLOUD_PROJECT --region=$GOOGLE_CLOUD_REGION \
|
|
--set-env-vars=GOOGLE_CLOUD_PROJECT=$GOOGLE_CLOUD_PROJECT \
|
|
--set-env-vars=GOOGLE_CLOUD_REGION=$GOOGLE_CLOUD_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. 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.
|
|
|
|
Congratulations!
|