with Hugging Face and Google Gemini?
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
Generates content from text input using the Google Gemini API. See the documentation
This task reads some image input and outputs the likelihood of classes. This action allows you to classify images into categories. See the docs
Generates content from both text and image input using the Gemini API. See the documentation
This task is well known to translate text from one language to another. See the docs
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.
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}`,
},
})
},
})
The Google Gemini API is a cutting-edge tool from Google that enables developers to leverage AI models like Imagen and MusicLM to create and manipulate images and music based on textual descriptions. With Pipedream, you can harness this API to automate workflows that integrate AI-generated content into a variety of applications, from generating visuals for social media posts to composing background music for videos. Pipedream's serverless platform allows you to connect Google Gemini API with other apps to create complex, event-driven workflows without managing infrastructure.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
google_gemini: {
type: "app",
app: "google_gemini",
}
},
async run({steps, $}) {
const data = `{{your_promptt}}`;
//E.g. {"contents":[{"parts":[{"text":"Write a story about a magic backpack"}]}]}
return await axios($, {
method: "POST",
url: `https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent`,
headers: {
"Content-Type": "application/json",
},
params: {
key: `${this.google_gemini.$auth.api_key}`,
},
data
})
},
})