import gradio as gr
import cv2

import torch
import kornia as K

def load_torch_image(fname):
    img = K.image_to_tensor(fname, False).float() / 255.
    img = K.color.bgr_to_rgb(img)
    return img


def enhance(file, brightness, contrast, saturation, gamma, hue):
    fname = file.name
    im = cv2.imread(fname)                 
    img = load_torch_image(im)

    x_out: torch.Tensor = K.enhance.adjust_brightness(img, float(brightness))
    x_out = K.enhance.adjust_contrast(x_out, float(contrast))
    x_out = K.enhance.adjust_saturation(x_out, float(saturation))
    x_out = K.enhance.adjust_gamma(x_out, float(gamma))
    x_out = K.enhance.adjust_hue(x_out, float(hue))

    return K.utils.tensor_to_image(x_out)


examples = [
    ["examples/ninja_turtles.jpg", 0, 1, 1, 1, 0],
    ["examples/kitty.jpg", 0, 1, 1, 1, 0],
]

iface = gr.Interface(
    enhance,
    [
        gr.inputs.Image(type="file"),
        gr.inputs.Slider(minimum=0, maximum=1, step=0.1, default=0, label="Brightness"),
        gr.inputs.Slider(minimum=0, maximum=4, step=0.1, default=1, label="Contrast"),
        gr.inputs.Slider(minimum=0, maximum=4, step=0.1, default=1, label="Saturation"),
        gr.inputs.Slider(minimum=0, maximum=1, step=0.1, default=1, label="Gamma"),
        gr.inputs.Slider(minimum=0, maximum=4, step=0.1, default=0, label="Hue"),
    ],
    "image",
    examples=examples,
    live=True
)

iface.launch()