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with Hugging Face and Python?
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Popular Hugging Face and Python Actions#
Want to have a nice know-it-all bot that can answer any question?. This action allows you to ask a question and get an answer from a trained model. See the docs
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
This task reads some image input and outputs the likelihood of classes. This action allows you to classify images into categories. See the docs
This task is well known to translate text from one language to another. See the docs
This task reads some image input and outputs the likelihood of classes and bounding boxes of detected objects. See the docs
Overview of Hugging Face#
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.
Connect Hugging Face#
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
hugging_face: {
type: "app",
app: "hugging_face",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://huggingface.co/api/whoami-v2`,
headers: {
Authorization: `Bearer ${this.hugging_face.$auth.access_token}`,
},
})
},
})
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}}Trusted by 1,000,000+ developers from startups to Fortune 500 companies