Run Recognition with Plate Recognizer API on New Batch Completed from OpenAI (ChatGPT) API

Pipedream makes it easy to connect APIs for Plate Recognizer, OpenAI (ChatGPT) and 2,700+ other apps remarkably fast.

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New Batch Completed from the OpenAI (ChatGPT) API

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Run Recognition with the Plate Recognizer API

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Getting Started#

This integration creates a workflow with a OpenAI (ChatGPT) trigger and Plate Recognizer action. When you configure and deploy the workflow, it will run on Pipedream's servers 24x7 for free.

Select this integration
Configure the New Batch Completed trigger
1. Connect your OpenAI (ChatGPT) account
2. Configure timer
Configure the Run Recognition action
1. Connect your Plate Recognizer account
2. Configure Image Path or URL
3. Optional- Configure Regions
4. Optional- Configure Camera ID
5. Optional- Configure MMC
6. Optional- Configure Config
Deploy the workflow
Send a test event to validate your setup
Turn on the trigger

Details#

This integration uses pre-built, source-available components from Pipedream's GitHub repo. These components are developed by Pipedream and the community, and verified and maintained by Pipedream.

To contribute an update to an existing component or create a new component, create a PR on GitHub. If you're new to Pipedream component development, you can start with quickstarts for trigger span and action development, and then review the component API reference.

Trigger#

New Batch Completed on OpenAI (ChatGPT)

Description:Emit new event when a new batch is completed in OpenAI. [See the documentation](https://platform.openai.com/docs/api-reference/batch/list)

Version:0.0.9

Key:openai-new-batch-completed

View on GitHub

OpenAI (ChatGPT) Overview#

OpenAI provides a suite of powerful AI models through its API, enabling developers to integrate advanced natural language processing and generative capabilities into their applications. Here’s an overview of the services offered by OpenAI's API:

Use Python or Node.js code to make fully authenticated API requests with your OpenAI account:

Trigger Code#

import common from "../common/common.mjs";
import sampleEmit from "./test-event.mjs";

export default {
  ...common,
  key: "openai-new-batch-completed",
  name: "New Batch Completed",
  description: "Emit new event when a new batch is completed in OpenAI. [See the documentation](https://platform.openai.com/docs/api-reference/batch/list)",
  version: "0.0.9",
  type: "source",
  dedupe: "unique",
  methods: {
    ...common.methods,
    _getLastCompleted() {
      return this.db.get("lastCompleted") || 0;
    },
    _setLastCompleted(lastCompleted) {
      this.db.set("lastCompleted", lastCompleted);
    },
    getMeta(batch) {
      return {
        id: batch.id,
        summary: `Batch Completed with ID ${batch.id}`,
        ts: batch.completed_at * 1000,
      };
    },
    async getAndProcessItems(max) {
      const lastCompleted = this._getLastCompleted();
      let maxCompleted = lastCompleted;
      const results = this.openai.paginate({
        resourceFn: this.openai.listBatches,
        max,
      });
      const batches = [];
      for await (const batch of results) {
        if (batch.completed_at && batch.completed_at > lastCompleted) {
          batches.push(batch);
          maxCompleted = Math.max(batch.completed_at, maxCompleted);
        }
      }
      this._setLastCompleted(maxCompleted);
      batches.reverse().forEach((item) => this.$emit(item, this.getMeta(item)));
    },
  },
  sampleEmit,
};

Trigger Configuration#

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI and CLI.

Label	Prop	Type	Description
OpenAI (ChatGPT)	`openai`	`app`	This component uses the OpenAI (ChatGPT) app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
	`timer`	`$.interface.timer`

Trigger Authentication#

OpenAI (ChatGPT) uses API keys for authentication. When you connect your OpenAI (ChatGPT) account, Pipedream securely stores the keys so you can easily authenticate to OpenAI (ChatGPT) APIs in both code and no-code steps.

About OpenAI (ChatGPT)#

OpenAI is an AI research and deployment company with the mission to ensure that artificial general intelligence benefits all of humanity. They are the makers of popular models like ChatGPT, DALL-E, and Whisper.

Action#

Run Recognition on Plate Recognizer

Description:Triggers a recognition process using the Plate Recognizer SDK.

Version:0.1.0

Key:platerecognizer-run-recognition

View on GitHub

Plate Recognizer Overview#

The Plate Recognizer API provides robust tools for converting images of vehicle license plates into text data. Using Pipedream, you can harness this capability to automate various tasks involving vehicle identification and monitoring. This integration is particularly useful in scenarios involving security, parking management, and logistics optimization, where automated plate recognition can streamline operations significantly.

Action Code#

import { getFileStream } from "@pipedream/platform";
import platerecognizer from "../../platerecognizer.app.mjs";

export default {
  key: "platerecognizer-run-recognition",
  name: "Run Recognition",
  description: "Triggers a recognition process using the Plate Recognizer SDK.",
  version: "0.1.0",
  type: "action",
  props: {
    platerecognizer,
    imageFileOrUrl: {
      type: "string",
      label: "Image Path or URL",
      description: "The image to be recognized. Provide either a file URL or a path to a file in the `/tmp` directory (for example, `/tmp/myImage.jpg`)",
    },
    regions: {
      type: "string[]",
      label: "Regions",
      description: "Regions to select specific license plate patterns. [See further details here](https://guides.platerecognizer.com/docs/other/country-codes/#country-codes)",
      optional: true,
    },
    cameraId: {
      type: "string",
      label: "Camera ID",
      description: "The ID of the camera that took the image.",
      optional: true,
    },
    mmc: {
      type: "boolean",
      label: "MMC",
      description: "Whether to detect vehicle make, model, and color.",
      optional: true,
    },
    config: {
      type: "object",
      label: "Config",
      description: "Additional configuration. [See further details here](https://guides.platerecognizer.com/docs/snapshot/api-reference/#engine-configuration)",
      optional: true,
    },
  },
  async run({ $ }) {

    const stream = await getFileStream(this.imageFileOrUrl);
    const chunks = [];
    for await (const chunk of stream) {
      chunks.push(chunk);
    }
    const buffer = Buffer.concat(chunks);

    const response = await this.platerecognizer.runRecognition({
      $,
      data: {
        upload: buffer.toString("base64"),
        regions: this.regions,
        camera_id: this.cameraId,
        mmc: this.mmc,
        config: this.config,
      },
    });

    $.export("$summary", "Recognition process triggered successfully");
    return response;
  },
};

Action Configuration#

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI.

Label	Prop	Type	Description
Plate Recognizer	`platerecognizer`	`app`	This component uses the Plate Recognizer app.
Image Path or URL	`imageFileOrUrl`	`string`	The image to be recognized. Provide either a file URL or a path to a file in the `/tmp` directory (for example, `/tmp/myImage.jpg`)
Regions	`regions`	`string[]`	Regions to select specific license plate patterns. See further details here
Camera ID	`cameraId`	`string`	The ID of the camera that took the image.
MMC	`mmc`	`boolean`	Whether to detect vehicle make, model, and color.
Config	`config`	`object`	Additional configuration. See further details here