Hugging Face and LangChain Tutorial

In today's tech-savvy world, if you are building AI applications, it's crucial to acquaint yourself with Hugging Face, one of the top AI companies valued at over $2 billion with more than 16,000 GitHub followers. This blog post will guide you through a quick, step-by-step tutorial on how to leverage Hugging Face and LangChain to create an AI app, all within 5 minutes! So, let's dive in.

Why Hugging Face?

Hugging Face offers a plethora of AI models, ranging from image to text, text to speech, and even PAX to image. Its products are utilized by tech giants like Google, Amazon, Microsoft, and Metta. It hosts more than 200,000 different AI models, making it an invaluable resource for AI app development.

Exploring the Hugging Face Platform

There are three key parts to the Hugging Face platform: models, data, and space.

  1. Models: This is where you find different AI models to use. It allows you to preview and test AI models directly on their hosted version, saving you the trouble of downloading them to your local machine.
  2. Data: Here, you can find datasets to train your own model. While you might not use this section often unless you are training your own model, it's a valuable resource for building custom AI solutions.
  3. Space: This is designed for developers to showcase and share the AI apps they've built. It also allows you to explore other AI apps for inspiration and learning.

Integrating Hugging Face with LangChain

In this tutorial, we will use LangChain to implement an AI app that converts an uploaded image into an audio story.

Implementing the AI App

The AI app we are going to build consists of three components: an image-to-text model, a language model, and a text-to-speech model.

  1. Image-to-Text Model: This model helps the machine understand the scenario based on the uploaded image. For this, we'll use Hugging Face's image-to-text model called 'Clip'. After creating a Hugging Face account and generating an access token, we will use Hugging Face's transformers library to download the model to our local machine.
  2. Language Model: This model generates a short story based on the scenario derived from the image. Here, we'll use LangChain to generate the story. It will require the addition of the OpenAI key.
  3. Text-to-Speech Model: This final model converts the generated text into speech. You can use Hugging Face's Text-to-Speech models for this purpose, with the popular 'Wav2Vec2' model being a great choice.

Finally, we can connect all these components together using Streamlit, a Python library that helps create user interfaces for Python code.

Wrapping Up

To recap, there are two key ways to use Hugging Face models:

  1. Use the Inference API to access the hosted version directly.
  2. Use the pipeline to download the models to your local machine.

To explore different types of tasks and models Hugging Face supports, visit Hugging Face Tasks.

Lastly, I would like to highlight Relevance AI, a platform built by the low-code AI team. It provides an image-to-text model out of the box, allowing you to create an image-to-speech app rapidly.

So, that's it! Now you are ready to build exciting AI apps using Hugging Face and LangChain. Don't forget to explore, experiment, and keep learning!