app.py

import gradio as gr
# project
from exposure_enhancement import enhance_image_exposure

# inputs, fn, and ouputs
inputs=[
        gr.Image(type="numpy"),
        gr.Slider(minimum=0, maximum=1, value=0.6, label="Gamma", info="The gamma correction parameter."),
        gr.Slider(minimum=0, maximum=1, value=0.15, label="Lambda", info="The weight for balancing the two terms in the illumination refinement optimization objective."),
        gr.Number(value=3, minimum=0, label="Sigma", info="Spatial standard deviation for spatial affinity based Gaussian weights.")
]
outputs=["image"]
examples=[
    ["demo/1.jpg", 0.6, 0.15, 3],
    ["demo/2.bmp", 0.6, 0.15, 3]
]

def enhance_image(image, gamma, lambda_, sigma, lime=True, bc=1, bs=1, be=1, eps=1e-3):
    # enhance image
    enhanced_image = enhance_image_exposure(image, gamma, lambda_, not lime, sigma=sigma, bc=bc, bs=bs, be=be, eps=eps)
    
    return enhanced_image


iface = gr.Interface(
        fn=enhance_image, 
        inputs=inputs,
        outputs=outputs,
        title=title,
        description=description,
        examples=examples
    )


if __name__ == "__main__":
    iface.launch(share=True)