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.
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.
There are three key parts to the Hugging Face platform: models, data, and space.
In this tutorial, we will use LangChain to implement an AI app that converts an uploaded image into an audio story.
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.
Finally, we can connect all these components together using Streamlit, a Python library that helps create user interfaces for Python code.
To recap, there are two key ways to use Hugging Face models:
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!