import gradio as gr

import kornia as K
from kornia.core import Tensor

import numpy as np

def rescale_aa(file, height, width):
    
    img: Tensor = K.io.load_image(file.name, K.io.ImageLoadType.RGB32)
    img = img[None]

    img_rescale: Tensor = K.geometry.rescale(img, (float(height),float(width)),antialias=False)
    img_rescale_aa: Tensor = K.geometry.rescale(img, (float(height),float(width)),antialias=True)
    img_rescale = K.utils.tensor_to_image(img_rescale)
    img_rescale_aa = K.utils.tensor_to_image(img_rescale_aa)
    img_out = np.concatenate([img_rescale,img_rescale_aa],axis=1)
    
    # when antialiasing , some values are going greater than 1 i.e 1.00001 which is giving error while displaying the output image,so clipping the output values from 0 to 1
    return np.clip(img_out ,0,1)


examples = [
    ["examples/a.png",1,1],
    ["examples/iron_man.jpeg",1,1],
]

kornia_resizing_demo = gr.Interface(
    rescale_aa,
    [
        gr.inputs.Image(type="file"),
        gr.inputs.Slider(minimum=0.005, maximum=2, step=0.005, default=1, label="Height"),
        gr.inputs.Slider(minimum=0.005, maximum=2, step=0.005, default=1, label="Width")
    ],
    "image",
    examples=examples,
    live=False,
    enable_queue = True,
    allow_flagging = "never"
)

kornia_resizing_demo.launch()