Integrate the Eden AI API with the Python API

Setup the Eden AI API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate Eden AI and Python remarkably fast. Free for developers.

Popular Eden AI and Python Actions#

Analyze Sentiment in Text with the Eden AI API

Analyzes sentiment in the provided text. See the documentation

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Run Python Code with the Python API

Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.

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Detect AI Content with the Eden AI API

Detects AI content in the provided text. See the documentation

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Generate Audio From Text with the Eden AI API

Generates an audio from the provided text. See the documentation

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Generate Image with the Eden AI API

Generates an image from the provided description. See the documentation

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Overview of Eden AI#

Eden AI provides a simple API for integrating a variety of AI services from different providers. On Pipedream, you can leverage Eden AI to automate tasks that involve natural language processing, computer vision, and other AI capabilities. By connecting Eden AI with other apps, you can create powerful workflows for data enrichment, content moderation, or sentiment analysis without extensive AI expertise.

Connect Eden AI#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    eden_ai: {
      type: "app",
      app: "eden_ai",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://api.edenai.run/v2/ocr/custom_document_parsing_async`,
      headers: {
        Authorization: `Bearer ${this.eden_ai.$auth.api_key}`,
        "Accept": `application/json`,
        "Content-Type": `application/json`,
      },
    })
  },
})

Overview of Python#

Develop, run and deploy your Python code in Pipedream workflows. Integrate seamlessly between no-code steps, with connected accounts, or integrate Data Stores and manipulate files within a workflow.

This includes installing PyPI packages, within your code without having to manage a requirements.txt file or running pip.

Below is an example of using Python to access data from the trigger of the workflow, and sharing it with subsequent workflow steps:

Connect Python#

def handler(pd: "pipedream"):
  # Reference data from previous steps
  print(pd.steps["trigger"]["context"]["id"])
  # Return data for use in future steps
  return {"foo": {"test":True}}