Use any AI Model on PubNub with HuggingFace API

In this updated guide, we’ll walk through how to incorporate AI models from HuggingFace into a globally distributed and secure application using PubNub Functions. By combining these powerful tools, developers can create dynamic, real-time interactions and process data with AI capabilities. We’ll cover setting up accounts on HuggingFace and PubNub, deploying an AI model, and managing secure access.

Quick Start with HuggingFace

HuggingFace, often referred to as the GitHub for AI, hosts AI model training code and simplifies AI model deployment. To get started:

1. Sign Up: Create an account on huggingface.co to access and deploy AI models.
2. Choose a Model: Search the model repository for a suitable model. Example models include distilbert-base-uncased-finetuned-sst-2-english for sentiment analysis and gpt2 for text generation.
3. API Key: Generate an API key in your account settings under API keys. This key is crucial for making requests from PubNub to HuggingFace.

Setting Up PubNub

PubNub provides real-time APIs for messaging, notifications, and updates, allowing devices to exchange data. Here’s how to integrate PubNub:

1. Create PubNub Account: Sign up if you haven’t already.
2. New App & Keyset: Create an app in the PubNub Dashboard, generating a publish and subscribe key.
3. Create a Function: Within your app, set up a new function to connect with HuggingFace AI APIs, processing messages in real-time.

Writing & Deploying the PubNub Function

The function will process messages with AI models. For logging purposes, we’ll just use console.log() for now. Here’s a sample code structure:

// Ensure your HUGGINGFACE_API_KEY is added to MY SECRETS in the PubNub Functions console

const http = require('xhr');
const vault = require('vault');

export default async (request) => {
    let message = request.message.text;
    let model = 'your-model-here'; // Choose your HuggingFace model
    let response = await query(message, model);
    console.log('Response:', response);

    // Options to block or alter the message
    // return request.abort();
    // request.message.text = "Altered message";
    return request.ok();
};

async function query(text, model) {
    const apiKey = await vault.get("HUGGINGFACE_API_KEY");
    const response = await http.fetch(`https://api-inference.huggingface.co/models/${model}`, {
        "method": "POST",
        "headers": { "Authorization": `Bearer ${apiKey}` },
        "body": JSON.stringify({inputs: text})
    });
    return await response.json();
}
        

Deployment Steps:

• Configure Models and API Key: Insert your chosen AI model into the code and securely store your HuggingFace API key in the PubNub Function vault.
• Start Module: Deploy the function by clicking “Start Module” in the PubNub Dashboard. Your function will now automatically process messages.
• Testing: Use the “Test Payload” feature to send messages and see the AI-processed log response.

With HuggingFace’s AI models and PubNub’s real-time messaging, developers can seamlessly integrate advanced AI functionalities into their applications. From sentiment analysis in social feeds to dynamic chatbots, the possibilities are extensive. Always secure your API keys and monitor API usage in line with HuggingFace’s policies for optimal performance and security. This setup guarantees a smooth, real-time AI experience in your apps.