Set Up Google Cloud Account
- Create a Google Cloud account if you haven’t done so. Access the Google Cloud Platform Console at Google Cloud Console.
- Enable billing to make use of Google Cloud AI services like Vision, Translate, or NLP APIs.
- Navigate to the "APIs & Services" section and enable the specific AI API you plan to integrate with Miro.
Configure API Keys
- In "APIs & Services", go to "Credentials". Click "Create Credentials" and choose "API Key".
- Restrict your API keys by setting up application restrictions and API restrictions for enhanced security.
- Copy and securely store the generated API Key. You'll need it to authenticate requests from Miro to Google Cloud.
Set Up Miro Developer Account
- Create a Miro account and access the Miro Developer Platform.
- Click on "Create App" and enter details like app name, app ID, and redirect URL.
- Configure required permissions that allow integration with board activities as per your project requirements.
Integrate Miro with Google Cloud AI
- In your Miro app, navigate to the "Web-plugin" section. Here, you will add functionality to communicate with Google Cloud APIs.
- Use JavaScript to interact with Google Cloud APIs. You'll make HTTP requests using the API Key you created earlier.
- Handle responses from Google Cloud and display relevant information in Miro, such as using stickers or shapes.
async function callGoogleCloudAI(text) {
const apiKey = 'YOUR_GOOGLE_CLOUD_API_KEY';
const response = await fetch(`https://language.googleapis.com/v1/documents:analyzeEntities?key=${apiKey}`, {
method: 'POST',
headers: {
'Content-Type': 'application/json'
},
body: JSON.stringify({
document: {
type: 'PLAIN_TEXT',
content: text
},
encodingType: 'UTF8'
})
});
const data = await response.json();
return data;
}
Process and Display Data in Miro
- Use Miro SDK to update board elements based on data obtained from Google Cloud. For instance, add tags or draw connections based on AI analysis.
- Implement logic to capture user interactions and synchronize with Google Cloud processing results.
- Ensure to handle errors gracefully and provide useful feedback for a seamless user experience.
miro.onReady(() => {
document.getElementById('analyzeButton').addEventListener('click', async () => {
const text = await miro.board.widgets.get({type: 'TEXT'});
const result = await callGoogleCloudAI(text[0].text);
console.log(result);
// Further process and integrate the results with Miro board
});
});
Deploy and Test Integration
- Deploy your Miro app with Google Cloud AI integration by finalizing environment settings and configurations.
- Test the integration extensively to ensure both systems work harmoniously and data flows as expected.
- Iterate based on user feedback and deploy updates to improve functionality.
