| 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() |