What do you want to automate

with Unsplash and Google Cloud Document AI?

Prompt, edit and deploy AI agents that connect to Unsplash, Google Cloud Document AI and 2,500+ other apps in seconds.

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Get Photo with the Unsplash API

Get a specific photo from Unsplash. See the documentation

 
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Search Photos with the Unsplash API

Get a single page of photo results for a query. See the documentation

 
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Integrate the Unsplash API with the Google Cloud Document AI API
Setup the Unsplash API trigger to run a workflow which integrates with the Google Cloud Document AI API. Pipedream's integration platform allows you to integrate Unsplash and Google Cloud Document AI remarkably fast. Free for developers.

Overview of Unsplash

The Unsplash API provides programmatic access to a vast library of high-quality, royalty-free images. This enables developers to search for and retrieve photos based on keywords, collections, or featured content. With Pipedream's integration, you can automate tasks like updating social media banners, populating website content with dynamic images, or even analyzing photo metadata for insights. By leveraging Unsplash's API within Pipedream, you can create workflows that trigger on various events, process image data, and interact with countless other apps available on the platform, streamlining your digital asset management and content creation processes.

Connect Unsplash

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import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    unsplash: {
      type: "app",
      app: "unsplash",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://api.unsplash.com/me`,
      headers: {
        Authorization: `Bearer ${this.unsplash.$auth.oauth_access_token}`,
      },
    })
  },
})

Connect Google Cloud Document AI

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import { DocumentProcessorServiceClient } from '@google-cloud/documentai/build/src/v1/index.js';
import { promises as fs } from 'fs';
import { get } from 'https';
import { writeFile } from 'fs/promises';
import { join } from 'path';

export default defineComponent({
  props: {
    google_cloud_document_ai: {
      type: "app",
      app: "google_cloud_document_ai",
    }
  },
  async run({ steps, $ }) {
    //Sample pdf file to process by Google Document AI API
    const url = 'https://www.learningcontainer.com/wp-content/uploads/2019/09/sample-pdf-file.pdf';
    const filePath = join('/tmp', 'my_document.pdf');

    const downloadFile = async () => {
      const res = await new Promise((resolve) => get(url, resolve));
      const chunks = [];

      for await (const chunk of res) {
        chunks.push(chunk);
      }

      await writeFile(filePath, Buffer.concat(chunks));
      console.log(`File downloaded successfully to ${filePath}`);
    };

    await downloadFile();

    const projectId = this.google_cloud_document_ai.$auth.project_id;
    const location = this.google_cloud_document_ai.$auth.location;
    const processorId = this.google_cloud_document_ai.$auth.processor_id;

    // Instantiates a client
    // apiEndpoint regions available: eu-documentai.googleapis.com, us-documentai.googleapis.com (Required if using eu based processor)
    // const client = new DocumentProcessorServiceClient({apiEndpoint: 'eu-documentai.googleapis.com'});
    const client = new DocumentProcessorServiceClient();

    async function testRequest() {
      // The full resource name of the processor, e.g.:
      // projects/project-id/locations/location/processor/processor-id
      // You must create new processors in the Cloud Console first
      const name = `projects/${projectId}/locations/${location}/processors/${processorId}`;

      // Read the file into memory.		
      const imageFile = await fs.readFile(filePath);

      // Convert the image data to a Buffer and base64 encode it.
      const encodedImage = Buffer.from(imageFile).toString('base64');

      const request = {
        name,
        rawDocument: {
          content: encodedImage,
          mimeType: 'application/pdf',
        },
      };

      // Recognizes text entities in the PDF document
      const [result] = await client.processDocument(request);
      const { document } = result;

      // Get all of the document text as one big string
      const { text } = document;

      // Extract shards from the text field
      const getText = textAnchor => {
        if (!textAnchor.textSegments || textAnchor.textSegments.length === 0) {
          return '';
        }

        // First shard in document doesn't have startIndex property
        const startIndex = textAnchor.textSegments[0].startIndex || 0;
        const endIndex = textAnchor.textSegments[0].endIndex;

        return text.substring(startIndex, endIndex);
      };

      // Read the text recognition output from the processor
      const [page1] = document.pages;
      const { paragraphs } = page1;
      let concatenatedText = "";
      for (const paragraph of paragraphs) {
        const paragraphText = getText(paragraph.layout.textAnchor);
        concatenatedText += paragraphText;
      }
      return concatenatedText;
    }

    return await testRequest();
  }
})

Trusted by 1,000,000+ developers from startups to Fortune 500 companies

Adyen logo
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SAS logo
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Webflow logo
Warner Bros. logo
Adyen logo
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Checkr logo
ChartMogul logo
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LinkedIn logo
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