Gradio
Gradio builds open-source Python tools for creating web interfaces and shareable demos for machine-learning models, APIs, and arbitrary Python functions. Its stated aim is to make machine learning accessible to more people by letting developers deploy and share apps without needing JavaScript, CSS, or web-hosting expertise.
At a Glance
- ML and AI researchers
- Software engineers and data scientists
- Startups and public-company AI teams
- Enterprise product, engineering, and model-evaluation teams
- +1 more
AI Tools by Gradio
(2)
Gradio
ML Demo UI Builder Python
FastRTC
Python Library for Real-Time WebRTC
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Latest News
Gradio 6.28.0 released with workflow UI, navigation, component-input, and streaming fixes and improvements.
Gradio 6 is highlighted on the official site as the current major release, with updated documentation and MCP-related capabilities.
Gradio 6.0 released with breaking-change migration guidance, improved queue performance, Spaces hot reloading, custom HTML components, and API-doc improvements.
Gradio 6's gr.HTML gained custom templates, scoped CSS, and JavaScript interactivity for one-shot web-app creation.
Products & Services
Open-source Python package for quickly building web demos and applications around ML models, APIs, or arbitrary Python functions, with local embedding and shareable deployment links.
High-level API that wraps a Python function or model with declared input and output components to create a web GUI in a few lines.
Lower-level layout and event framework for composing more customized multi-component ML applications, including rows, columns, tabs, themes, and event handlers.
Specialized interface for conversational applications, with chatbot, multimodal input, streaming, and API-oriented capabilities.
Market Position
Gradio is positioned as a Python-first, open-source alternative to more general app/dashboard frameworks such as Streamlit, with specialized components and workflows for ML demos, model interaction, streaming, chat, and Hugging Face Spaces deployment. Its differentiation is low-code interface generation for arbitrary Python functions and models plus deep integration with the Hugging Face ecosystem.
Leadership
Founders
Abubakar (Abu) Abid
Stanford machine-learning PhD student and researcher; he conceived Gradio after struggling to let a non-Python-speaking doctor test a medical computer-vision model. He is identified by Cowboy Ventures and Crunchbase as co-founder and CEO.
Ali Abdalla
Stanford housemate and co-founder; the founding team is described by Pear VC as having built technical products at companies including Tesla and Google.
Ali Abid
Stanford housemate and co-founder; Crunchbase identifies him as Gradio co-founder and lead engineer, and Pear VC describes the founding team as having deep technical-product experience at Tesla and Google.
Dawood Khan
Stanford housemate and co-founder; he was part of the founding engineering team that released the first Gradio version in 2019.
Executive Team
Abubakar Abid
Founder & ML Team Lead (Hugging Face)
Former CEO of Gradio, now leading the Gradio team at Hugging Face.
Ahsen Khaliq
ML Advocate
Key team member focusing on community growth and demo showcases.
Board of Directors
Founding Story
While a Stanford PhD student, Abubakar Abid needed a doctor collaborator to test a medical computer-vision model, but the collaborator could not easily run Python or the model on his own images. Abid recruited his Stanford housemates Ali Abdalla, Ali Abid, and Dawood Khan to build a simple way for ML engineers to create and share model demos; the initial project expanded from computer vision to text, speech, and video.
Business Model
Revenue Model
Gradio is an open-source, Apache-2.0-licensed developer tool distributed as a Python package. The core library is free to run locally; hosting and compute economics are primarily associated with Hugging Face Spaces and related Hugging Face services rather than a separately documented Gradio subscription.
Target Markets
- ML and AI researchers
- Software engineers and data scientists
- Startups and public-company AI teams
- Enterprise product, engineering, and model-evaluation teams
- Education, research, and open-source communities
- Rapid prototyping and demonstration of machine-learning models
- Computer-vision model testing and human feedback
- Natural-language, speech, audio, video, and generative-AI applications
- Internal model debugging and evaluation by interdisciplinary teams
- Public showcases and shareable AI demos
- Embedding model or app APIs into other Python or JavaScript applications
- Wells Fargo
- TD Bank