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# import gradio as gr
# import numpy as np
# def gennerateImage(input_array,eps):
# eps = eps / 100
# mask = np.random.random(input_array.shape)
# mask = mask * 2 - 1 # 将mask的值转换为-1到1之间
# noise_img = (input_array * (1 + mask * eps)).astype(np.uint8) % 255
# return noise_img
# demo = gr.Interface(
# gennerateImage,
# inputs=[
# gr.Image(),
# gr.Slider(1, 10, 3)
# ],
# outputs="image"
# )
# demo.launch(share=True)
import gradio as gr
import numpy as np
import time
# define core fn, which returns a generator {steps} times before returning the image
def my_generator(steps):
for i in range(steps):
time.sleep(1)
yield i
yield steps
demo = gr.Interface(my_generator, inputs=gr.Slider(1, 10, 3), outputs="number")
# define queue - required for generators
demo.queue()
demo.launch(share=True)
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