Image Control Net Preprocess with Runware API on New Contact from Twilio SendGrid API

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New Contact from the Twilio SendGrid API

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Image Control Net Preprocess with the Runware API

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Getting Started#

This integration creates a workflow with a Twilio SendGrid trigger and Runware action. When you configure and deploy the workflow, it will run on Pipedream's servers 24x7 for free.

Select this integration
Configure the New Contact trigger
1. Connect your Twilio SendGrid account
2. Configure timer
Configure the Image Control Net Preprocess action
1. Connect your Runware account
2. Configure Input Image
3. Optional- Select a Output Type
4. Optional- Select a Output Format
5. Optional- Configure Include Cost
6. Optional- Select a Preprocessor Type
7. Configure Height
8. Configure Width
9. Optional- Configure Low Threshold Canny
10. Optional- Configure High Threshold Canny
11. Optional- Configure Include Hands and Face OpenPose
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 Contact on Twilio SendGrid

Description:Emit new event when a new contact is created

Version:0.0.6

Key:sendgrid-new-contact

View on GitHub

Twilio SendGrid Overview#

The Twilio SendGrid API opens up a world of possibilities for email automation, enabling you to send emails efficiently and track their performance. With this API, you can programmatically create and send personalized email campaigns, manage contacts, and parse inbound emails for data extraction. When you harness the power of Pipedream, you can connect SendGrid to hundreds of other apps to automate workflows, such as triggering email notifications based on specific actions, syncing email stats with your analytics, or handling incoming emails to create tasks or tickets.

Trigger Code#

import orderBy from "lodash/orderBy.js";
import common from "../common/timer-based.mjs";

export default {
  ...common,
  key: "sendgrid-new-contact",
  name: "New Contact",
  description: "Emit new event when a new contact is created",
  version: "0.0.6",
  type: "source",
  dedupe: "unique",
  hooks: {
    async activate() {
      const currentTimestamp = Date.now();
      const state = {
        processedItems: [],
        lowerTimestamp: currentTimestamp,
        upperTimestamp: currentTimestamp,
      };
      this.db.set("state", state);
    },
  },
  methods: {
    ...common.methods,
    _maxDelayTime() {
      // There is no report from SendGrid as to how much time it takes
      // for a contact to be created and appear in search results, so
      // we're using a rough estimate of 30 minutes here.
      return 30 * 60 * 1000;  // 30 minutes, in milliseconds
    },
    _addDelayOffset(timestamp) {
      return timestamp - this._maxDelayTime();
    },
    _cleanupOldProcessedItems(processedItems, currentTimestamp) {
      return processedItems
        .map((item) => ({
          // We just need to keep track of the record ID and
          // its creation date.
          id: item.id,
          created_at: item.created_at,
        }))
        .filter((item) => {
          const { created_at: createdAt } = item;
          const createdAtTimestamp = Date.parse(createdAt);
          const cutoffTimestamp = this._addDelayOffset(currentTimestamp);
          return createdAtTimestamp > cutoffTimestamp;
        });
    },
    _makeSearchQuery(processedItems, lowerTimestamp, upperTimestamp) {
      const idList = processedItems
        .map((item) => item.id)
        .map((id) => `'${id}'`)
        .join(", ")
      || "''";
      const startTimestamp = this._addDelayOffset(lowerTimestamp);
      const startDate = this.toISOString(startTimestamp);
      const endDate = this.toISOString(upperTimestamp);
      return `
        contact_id NOT IN (${idList}) AND
        created_at BETWEEN
          TIMESTAMP '${startDate}' AND
          TIMESTAMP '${endDate}'
      `;
    },
    generateMeta(data) {
      const {
        item,
        eventTimestamp: ts,
      } = data;
      const {
        id,
        email,
      } = item;
      const slugifiedEmail = this.slugifyEmail(email);
      const summary = `New contact: ${slugifiedEmail}`;
      return {
        id,
        summary,
        ts,
      };
    },
    async processEvent(event) {
      // Transform the timer timestamp to milliseconds
      // to be consistent with how Javascript handles timestamps.
      const eventTimestamp = event.timestamp * 1000;

      // Retrieve the current state of the component.
      const {
        processedItems,
        lowerTimestamp,
        upperTimestamp,
      } = this.db.get("state");

      // Search for contacts within a specific timeframe, excluding
      // items that have already been processed.
      const query = this._makeSearchQuery(processedItems, lowerTimestamp, upperTimestamp);
      const {
        result: items,
        contact_count: contactCount,
      } = await this.sendgrid.searchContacts(query);

      // If no contacts have been retrieved via the API,
      // move the time window forward to possibly capture newer contacts.
      if (contactCount === 0) {
        const newState = {
          processedItems: this._cleanupOldProcessedItems(processedItems, lowerTimestamp),
          lowerTimestamp: upperTimestamp,
          upperTimestamp: eventTimestamp,
        };
        this.db.set("state", newState);
        return;
      }

      // We process the searched records from oldest to newest.
      const itemsToProcess = orderBy(items, "created_at");
      itemsToProcess
        .forEach((item) => {
          const meta = this.generateMeta({
            item,
            eventTimestamp,
          });
          this.$emit(item, meta);
        });

      // Use the timestamp of the last processed record as a lower bound for
      // following searches. This bound will be subjected to an offset so in
      // case older records appear in future search results, but have not
      // appeared until now, can be processed. We only adjust it if it means
      // moving forward, not backwards. Otherwise, we might start retrieving
      // older and older records indefinitely (and we're all about *new*
      // records!)
      const newLowerTimestamp = Math.max(
        lowerTimestamp,
        Date.parse(itemsToProcess[0].created_at),
      );

      // If the total contact count is less than 100, it means that during the
      // next iteration the search results count will most likely be less than
      // 50. In that case, if we extend the upper bound of the search time range
      // we might be able to retrieve more records.
      const newUpperTimestamp = contactCount < 100
        ? eventTimestamp
        : upperTimestamp;

      // The list of processed items can grow indefinitely.
      // Since we don't want to keep track of every processed record
      // ever, we need to clean up this list, removing any records
      // that are no longer relevant.
      const newProcessedItems = this._cleanupOldProcessedItems(
        [
          ...processedItems,
          ...itemsToProcess,
        ],
        newLowerTimestamp,
      );

      // Update the state of the component to reflect the computations
      // made above.
      const newState = {
        processedItems: newProcessedItems,
        lowerTimestamp: newLowerTimestamp,
        upperTimestamp: newUpperTimestamp,
      };
      this.db.set("state", newState);
    },
  },
};

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
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
Twilio SendGrid	`sendgrid`	`app`	This component uses the Twilio SendGrid app.
	`timer`	`$.interface.timer`

Trigger Authentication#

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

About Twilio SendGrid#

Send marketing and transactional email through the Twilio SendGrid platform with the Email API, proprietary mail transfer agent, and infrastructure for scalable delivery.

Action#

Image Control Net Preprocess on Runware

Description:Request an image control net preprocess task to be processed by the Runware API. [See the documentation](https://docs.runware.ai/en/image-editing/controlnet-tools).

Version:0.0.1

Key:runware-image-control-net-preprocess

View on GitHub

Action Code#

import { v4 as uuid } from "uuid";
import app from "../../runware.app.mjs";
import constants from "../../common/constants.mjs";

export default {
  key: "runware-image-control-net-preprocess",
  name: "Image Control Net Preprocess",
  description: "Request an image control net preprocess task to be processed by the Runware API. [See the documentation](https://docs.runware.ai/en/image-editing/controlnet-tools).",
  version: "0.0.1",
  type: "action",
  props: {
    app,
    inputImage: {
      propDefinition: [
        app,
        "inputImage",
      ],
    },
    outputType: {
      propDefinition: [
        app,
        "outputType",
      ],
    },
    outputFormat: {
      propDefinition: [
        app,
        "outputFormat",
      ],
    },
    includeCost: {
      propDefinition: [
        app,
        "includeCost",
      ],
    },
    preProcessorType: {
      type: "string",
      label: "Preprocessor Type",
      description: "The preprocessor to be used.",
      optional: true,
      options: [
        "canny",
        "depth",
        "mlsd",
        "normalbae",
        "openpose",
        "tile",
        "seg",
        "lineart",
        "lineart_anime",
        "shuffle",
        "scribble",
        "softedge",
      ],
    },
    height: {
      propDefinition: [
        app,
        "height",
      ],
    },
    width: {
      propDefinition: [
        app,
        "width",
      ],
    },
    lowThresholdCanny: {
      type: "integer",
      label: "Low Threshold Canny",
      description: "Defines the lower threshold when using the Canny edge detection preprocessor. The recommended value is `100`.",
      optional: true,
    },
    highThresholdCanny: {
      type: "integer",
      label: "High Threshold Canny",
      description: "Defines the high threshold when using the Canny edge detection preprocessor. The recommended value is `200`.",
      optional: true,
    },
    includeHandsAndFaceOpenPose: {
      type: "boolean",
      label: "Include Hands and Face OpenPose",
      description: "Include the hands and face in the pose outline when using the OpenPose preprocessor.",
      optional: true,
    },
  },
  async run({ $ }) {
    const {
      app,
      outputType,
      outputFormat,
      includeCost,
      inputImage,
      preProcessorType,
      height,
      width,
      lowThresholdCanny,
      highThresholdCanny,
      includeHandsAndFaceOpenPose,
    } = this;

    const response = await app.post({
      $,
      data: [
        {
          taskType: constants.TASK_TYPE.IMAGE_CONTROL_NET_PREPROCESS.value,
          taskUUID: uuid(),
          outputType,
          outputFormat,
          inputImage,
          includeCost,
          height,
          width,
          preProcessorType,
          lowThresholdCanny,
          highThresholdCanny,
          includeHandsAndFaceOpenPose,
        },
      ],
    });

    $.export("$summary", `Successfully requested image control net preprocess task with UUID \`${response.data[0].taskUUID}\`.`);
    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
Runware	`app`	`app`	This component uses the Runware app.
Input Image	`inputImage`	`string`	Specifies the input image to be processed. The image can be specified in one of the following formats: An UUID v4 string of a previously uploaded image or a generated image. A data URI string representing the image. The data URI must be in the format `data:<mediaType>;base64,` followed by the base64-encoded image. For example: `data:image/png;base64,iVBORw0KGgo...`. A base64 encoded image without the data URI prefix. For example: `iVBORw0KGgo...`. A URL pointing to the image. The image must be accessible publicly. Supported formats are: PNG, JPG and WEBP.
Output Type	`outputType`	`string`	Select a value from the drop down menu:`base64DatadataURIURL`
Output Format	`outputFormat`	`string`	Select a value from the drop down menu:`PNGJPGWEBP`
Include Cost	`includeCost`	`boolean`	If set to `true`, the cost to perform the task will be included in the response object. Defaults to `false`.
Preprocessor Type	`preProcessorType`	`string`	Select a value from the drop down menu:`cannydepthmlsdnormalbaeopenposetileseglineartlineart_animeshufflescribblesoftedge`
Height	`height`	`integer`	Used to define the height dimension of the generated image. Certain models perform better with specific dimensions. The value must be divisible by 64, eg: `512`, `576`, `640` ... `2048`.
Width	`width`	`integer`	Used to define the width dimension of the generated image. Certain models perform better with specific dimensions. The value must be divisible by 64, eg: `512`, `576`, `640` ... `2048`.
Low Threshold Canny	`lowThresholdCanny`	`integer`	Defines the lower threshold when using the Canny edge detection preprocessor. The recommended value is `100`.
High Threshold Canny	`highThresholdCanny`	`integer`	Defines the high threshold when using the Canny edge detection preprocessor. The recommended value is `200`.
Include Hands and Face OpenPose	`includeHandsAndFaceOpenPose`	`boolean`	Include the hands and face in the pose outline when using the OpenPose preprocessor.