Introducing Gradio Clients
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Interface
gradio.Interface(···)Description
Interface is Gradio's main high-level class, and allows you to create a web-based GUI / demo around a machine learning model (or any Python function) in a few lines of code. You must specify three parameters: (1) the function to create a GUI for (2) the desired input components and (3) the desired output components. Additional parameters can be used to control the appearance and behavior of the demo.
Example Usage
import gradio as gr
def image_classifier(inp):
return {'cat': 0.3, 'dog': 0.7}
demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label")
demo.launch()Initialization
Parameters
Demos
Methods
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gradio.Interface.launch(···)Description
Launches a simple web server that serves the demo. Can also be used to create a public link used by anyone to access the demo from their browser by setting share=True. <br>
Example Usage
import gradio as gr
def reverse(text):
return text[::-1]
demo = gr.Interface(reverse, "text", "text")
demo.launch(share=True, auth=("username", "password"))Parameters
load
gradio.Interface.load(block, ···)Description
This listener is triggered when the Interface initially loads in the browser.
Parameters
from_pipeline
gradio.Interface.from_pipeline(pipeline, ···)Description
Class method that constructs an Interface from a Hugging Face transformers.Pipeline or diffusers.DiffusionPipeline object. The input and output components are automatically determined from the pipeline.
Example Usage
import gradio as gr
from transformers import pipeline
pipe = pipeline("image-classification")
gr.Interface.from_pipeline(pipe).launch()Parameters
integrate
gradio.Interface.integrate(···)Description
A catch-all method for integrating with other libraries. This method should be run after launch()
Parameters
queue
gradio.Interface.queue(···)Description
By enabling the queue you can control when users know their position in the queue, and set a limit on maximum number of events allowed.
Example Usage
demo = gr.Interface(image_generator, gr.Textbox(), gr.Image())
demo.queue(max_size=20)
demo.launch()Parameters