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def predict_image(img): | |
img_4d=img.reshape(-1,299,299,3) | |
img_4d=img_4d/255 | |
prediction=model.predict(img_4d)[0] | |
#prediction = [1 if x>0.5 else 0 for x in prediction] | |
return {class_names[i]: float(prediction[i]) for i in range(1)} | |
import gradio as gr | |
image = gr.inputs.Image(shape=(299,299)) | |
label = gr.outputs.Label(num_top_classes=1) | |
gr.Interface(fn=predict_image, inputs=image, | |
outputs=label).launch(debug='False',share=True) |