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import numpy as np
def image_classifier(inp):
confidence_scores = np.random.rand(2)
confidence_scores /= np.sum(confidence_scores)
classes = ['cats', 'dogs']
result = {classes[i] : confidence_scores[i] for i in range(2)}
return result
import gradio as gr
demo = gr.Interface(fn = image_classifier, inputs="image", outputs="label")
demo.launch() |