Hugging Face Acquires Gradio, A Customizable UI Components Library For Python

Gradio is joining Hugging Face! By acquiring Gradio, a machine learning startup, Hugging Face will offer users, developers, and data scientists the tools needed to get to high-level results and create better models and tools…

The above paragraph about acquisitions is so common that an algorithm could have written it. In fact, one did! This first paragraph was written with the Acquisition Post Generator, a machine learning demo on Hugging Face Spaces. You can run it yourself in your browser: provide the names of any two companies, and you'll get a reasonable-sounding start to an article announcing their acquisition. 

The Acquisition Post Generator was built using our open-source Gradio library -- it is just one of our recent collaborations with Hugging Face. 

And I'm excited to announce that these collaborations are culminating in Hugging Face's acquisition of Gradio (so yes, that first paragraph might have been written by an algorithm, but it's true!)

As one of the founders of Gradio, Abubakar Abid, previously CEO of Gradio, said, " I envisioned a tool that could make it super simple for machine learning engineers to build and share demos of computer vision models, which in turn would lead to better feedback and more reliable models."

The first version of Gradio was released in 2019 and later expanded to cover more areas of machine learning, including text, speech, and video. “As interdisciplinary teams in the industry, from startups to public companies were building models and needed to debug them internally or showcase them externally, Gradio could help with both. The first library was released with more than 300,000 demos have been built with Gradio,” Abid said in his blog.

Demos and GUIs built with Gradio give more people the power of machine learning because they allow non-technical users to access, use, and give feedback on models. Acquisition by Hugging Face is the next step in this ongoing journey of accessibility. Hugging Face has already radically democratized machine learning so that any software engineer can use state-of-the-art models with a few lines of code.