gradio

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cff-version: 1.2.0
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message: Please cite this project using these metadata.
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title: "Gradio: Hassle-free sharing and testing of ML models in the wild"
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abstract: >-
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  Accessibility is a major challenge of machine learning (ML).
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  Typical ML models are built by specialists and require
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  specialized hardware/software as well as ML experience to
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  validate. This makes it challenging for non-technical
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  collaborators and endpoint users (e.g. physicians) to easily
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  provide feedback on model development and to gain trust in
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  ML. The accessibility challenge also makes collaboration
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  more difficult and limits the ML researcher's exposure to
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  realistic data and scenarios that occur in the wild. To
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  improve accessibility and facilitate collaboration, we
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  developed an open-source Python package, Gradio, which
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  allows researchers to rapidly generate a visual interface
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  for their ML models. Gradio makes accessing any ML model as
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  easy as sharing a URL. Our development of Gradio is informed
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  by interviews with a number of machine learning researchers
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  who participate in interdisciplinary collaborations. Their
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  feedback identified that Gradio should support a variety of
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  interfaces and frameworks, allow for easy sharing of the
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  interface, allow for input manipulation and interactive
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  inference by the domain expert, as well as allow embedding
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  the interface in iPython notebooks. We developed these
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  features and carried out a case study to understand Gradio's
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  usefulness and usability in the setting of a machine
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  learning collaboration between a researcher and a
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  cardiologist.
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authors:
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  - family-names: Abid
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    given-names: Abubakar
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  - family-names: Abdalla
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    given-names: Ali
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  - family-names: Abid
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    given-names: Ali
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  - family-names: Khan
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    given-names: Dawood
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  - family-names: Alfozan
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    given-names: Abdulrahman
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  - family-names: Zou
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    given-names: James
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doi: 10.48550/arXiv.1906.02569
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date-released: 2019-06-06
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url: https://arxiv.org/abs/1906.02569
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