Wire It, Run It, Deploy It: AI Workflows in Gradio
huggingface ·
The article introduces gr.Workflow, a Gradio feature for building AI applications as graphs of typed nodes rather than manually wiring pipeline steps in Python. It provides a drag-and-drop canvas where inputs, operators, and outputs can be run and inspected, while also making the same workflow available as a REST API and deployable to Hugging Face Spaces. Demonstrated workflows include image editing with Qwen-Image-Edit, a media studio combining image generation, background removal, text-to-speech, and language-model calls, and parallel artwork generation. Another example analyzes Hugging Face datasets through several independent operators that produce summaries, previews, statistics, and charts. Python function nodes can also run models locally in a Space using ZeroGPU, as shown by an animation demo using LTX-Video and Diffusers. Each workflow output receives a REST endpoint that can be called with the Gradio client or curl, with Hugging Face tokens required for endpoints using models or Spaces. Developers can start by duplicating a demo or binding a Python function with gr.Workflow, with a full guide available in the Gradio documentation.
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