Text Classification with Hugging Face API on New Item in Feed from RSS API

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New Item in Feed from the RSS API

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Text Classification with the Hugging Face API

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Getting Started#

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

Select this integration
Configure the New Item in Feed trigger
1. Connect your RSS account
2. Configure timer
3. Configure Feed URL
4. Configure Published After
Configure the Text Classification action
1. Connect your Hugging Face account
2. Select a Model ID
3. Configure Text
4. Optional- Configure Additional Fields
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 Item in Feed on RSS

Description:Emit new items from an RSS feed

Version:1.2.7

Key:rss-new-item-in-feed

View on GitHub

RSS Overview#

The RSS app allows users to automatically fetch and parse updates from web feeds. This functionality is pivotal for staying abreast of content changes or updates from websites, blogs, and news outlets that offer RSS feeds. With Pipedream, you can harness the RSS API to trigger workflows that enable a broad range of automations, like content aggregation, monitoring for specific keywords, notifications, and data synchronization across platforms.

Trigger Code#

import { defineSource } from "@pipedream/types";
import rss from "../../app/rss.app.mjs";
import rssCommon from "../common/common.mjs";
export default defineSource({
    ...rssCommon,
    key: "rss-new-item-in-feed",
    name: "New Item in Feed",
    description: "Emit new items from an RSS feed",
    version: "1.2.7",
    type: "source",
    dedupe: "unique",
    props: {
        ...rssCommon.props,
        url: {
            propDefinition: [
                rss,
                "url",
            ],
        },
        publishedAfter: {
            type: "string",
            label: "Published After",
            description: "Emit items published after the specified date in ISO 8601 format .e.g `2022-12-07T12:57:10+07:00`",
            default: new Date(Date.now() - 24 * 60 * 60 * 1000).toISOString(), // default to one day ago
        },
    },
    hooks: {
        async activate() {
            // Try to parse the feed one time to confirm we can fetch and parse.
            // The code will throw any errors to the user.
            await this.rss.fetchAndParseFeed(this.url);
        },
    },
    methods: {
        ...rssCommon.methods,
        generateMeta: function (item) {
            return {
                id: this.rss.itemKey(item),
                summary: item.title,
                ts: Date.now(),
            };
        },
    },
    async run() {
        const items = await this.rss.fetchAndParseFeed(this.url);
        for (const item of items.reverse()) {
            const publishedAfter = +new Date(this.publishedAfter);
            const ts = this.rss.itemTs(item);
            if (Number.isNaN(publishedAfter) || publishedAfter > ts) {
                continue;
            }
            const meta = this.generateMeta(item);
            this.$emit(item, meta);
        }
    },
});

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
RSS	`rss`	`app`	This component uses the RSS app.
	`timer`	`$.interface.timer`	How often you want to poll the feed for new items
Feed URL	`url`	`string`	Enter the URL for any public RSS feed
Published After	`publishedAfter`	`string`	Emit items published after the specified date in ISO 8601 format .e.g `2022-12-07T12:57:10+07:00`

Trigger Authentication#

The RSS API does not require authentication.

About RSS#

Real Simple Syndication

Action#

Text Classification on Hugging Face

Description:Usually used for sentiment-analysis this will output the likelihood of classes of an input. This action allows you to classify text into categories. [See the docs](https://huggingface.co/docs/huggingface.js/inference/classes/HfInference#textclassification).

Version:0.0.1

Key:hugging_face-text-classification

View on GitHub

Hugging Face Overview#

The Hugging Face API provides access to a vast range of machine learning models, primarily for natural language processing (NLP) tasks like text classification, translation, summarization, and question answering. It lets you leverage pre-trained models and fine-tune them on your data. Using the API within Pipedream, you can automate workflows that involve language processing, integrate AI insights into your apps, or respond to events with AI-generated content.

Action Code#

import { ConfigurationError } from "@pipedream/platform";
import app from "../../hugging_face.app.mjs";
import utils from "../../common/utils.mjs";
import tag from "../common/tag.mjs";

export default {
  key: "hugging_face-text-classification",
  name: "Text Classification",
  description: "Usually used for sentiment-analysis this will output the likelihood of classes of an input. This action allows you to classify text into categories. [See the docs](https://huggingface.co/docs/huggingface.js/inference/classes/HfInference#textclassification).",
  type: "action",
  version: "0.0.1",
  props: {
    app,
    modelId: {
      propDefinition: [
        app,
        "modelId",
        () => ({
          tagFilter: tag.TEXT_CLASSIFICATION,
        }),
      ],
    },
    text: {
      type: "string",
      label: "Text",
      description: "The text to use for classification.",
    },
    additionalFields: {
      type: "object",
      label: "Additional Fields",
      description: "Additional fields to include in the request in case the model requires them. E.g you can set `parameters` as a key and `{\"candidate_labels\":[\"love\",\"like\"]}` as a value. Or Just something like `my_field` as a key and `my_value` as a value.",
      optional: true,
    },
  },
  async run({ $: step }) {
    const {
      modelId,
      text,
      additionalFields,
    } = this;

    if (additionalFields && typeof(additionalFields) !== "object" && additionalFields !== null) {
      throw new ConfigurationError("Additional Fields property must be an object.");
    }

    const response = await this.app.inference({
      step,
      modelId,
      data: {
        inputs: text,
        ...utils.parseFields(additionalFields),
      },
    });

    step.export("$summary", "Successfully classified text");

    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
Hugging Face	`app`	`app`	This component uses the Hugging Face app.
Model ID	`modelId`	`string`	Select a value from the drop down menu.
Text	`text`	`string`	The text to use for classification.
Additional Fields	`additionalFields`	`object`	Additional fields to include in the request in case the model requires them. E.g you can set `parameters` as a key and `{"candidate_labels":["love","like"]}` as a value. Or Just something like `my_field` as a key and `my_value` as a value.

Action Authentication#

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

To retrieve your Access Tokens,

Navigate to your Hugging Face account and sign in
Go to “Settings” > “Access Tokens”

About Hugging Face#

Build, train and deploy state of the art models powered by the reference open source in machine learning.

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