app.py

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)