Gradio is joining Hugging Face!

Summary

Hugging Face has acquired Gradio, the open-source machine learning library designed for building and sharing interactive model interfaces. Gradio originated in 2019 when its founder struggled to share a medical computer vision model with a physician collaborator who did not write Python. Co-founded alongside Ali Abdalla, Ali Abid, and Dawood Khan, the project expanded from computer vision into text, speech, and video modalities. Over 300,000 demos have been built using Gradio, allowing interdisciplinary industry teams and researchers to debug models internally and showcase them externally to non-technical users. The acquisition unites Gradio with Hugging Face to broaden browser-based machine learning accessibility and expand hiring efforts across the joint team.

Context

In 2019, a Stanford PhD student struggled to share a medical computer vision model with a physician collaborator who did not know Python and could not run the model on his own images. Machine learning practitioners lacked easy tools to let non-technical stakeholders test models, debug issues, and provide feedback.

Approach / What changed

Gradio created an open-source library allowing engineers to build graphical user interfaces for models spanning computer vision, text, speech, and video. Following collaborative projects such as the Acquisition Post Generator demo on Hugging Face Spaces, Hugging Face acquired Gradio to integrate and expand its accessibility tools.

Takeaways

• Gradio was created in 2019 by Stanford housemates to help machine learning engineers share interactive model demos with non-technical collaborators who do not know Python.
• The open-source Gradio library expanded from computer vision to text, speech, and video, reaching over 300,000 created demos across research and industry.
• Hugging Face acquired Gradio to advance model accessibility, enabling users with an internet connection and a web browser to test, debug, and provide feedback on machine learning models.

Related reading

• huggingface.co · May 25, 2021

  Using & Mixing Hugging Face Models with Gradio 2.0

  The Hugging Face Model Hub hosts more than 10,000 user-submitted machine learning models spanning natural language processing, image classification, and audio processing. Machine learning developers increasingly need to demo models to interdisciplinary collaborators or non-programmers to uncover biases and failure points. The Gradio 2.0 library enables users to create graphical user interfaces and load almost any Hugging Face model using a single line of code. By default, these interfaces query Hugging Face's hosted Inference API without requiring an API key, though users can also run computations locally via the transformers library. Gradio interfaces can be customized through the Interface class and composed to load multiple models in parallel or chain them in series.

  Abubakar Abid

• huggingface.co · May 16, 2022

  Gradio 3.0 is Out!

  Gradio 3.0 introduces a ground-up redesign of the Gradio library to improve machine learning demo development and browser performance. The frontend has been rebuilt using modern technologies such as Svelte, resulting in smaller payloads, faster page load times, and cleaner visual designs suitable for embedding in blog posts. Component updates include revamping the Dataframe component to support CSV drag-and-drop, introducing new UI elements like Gallery, and adding the TabbedInterface class to group demos under distinct tabs. Developers also gain access to Gradio Blocks, a low-level Python API enabling custom layouts, multi-step interfaces, dynamic component property modifications, and complex data flows. A competition called the Gradio Blocks Party has been organized through the end of May to encourage community adoption.