app.py

import gradio as gr
import numpy as np
import colorizers as c

from colorizers.util import postprocess_tens, preprocess_img

def interface(image, model: str = "eccv16"):
    if model == "eccv16":
        img = c.eccv16(pretrained=True).eval()
    else:
        img = c.siggraph17(pretrained=True).eval()
    oimg = np.asarray(image)
    if(oimg.ndim == 2):
        oimg = np.tile(oimg[:,:,None], 3)
    (tens_l_orig, tens_l_rs) = preprocess_img(oimg)

    output_img = postprocess_tens(
        tens_l_orig,
        img(tens_l_rs).cpu()
    )
    return output_img

css='''
.Box {
	background-color: var(--color-canvas-default);
	border-color: var(--color-border-default);
	border-style: solid;
	border-width: 1px;
	border-radius: 6px;
}
.d-flex {
	display: flex !important;
}
.flex-md-row {
	flex-direction: row !important;
}
.flex-column {
	flex-direction: column !important;
}
'''
title = "Image Colorization Using AI Models"
description = r"""<center>An automatic colorization functionality for Real-Time User-Guided Image Colorization with Learned Deep Priors,ECCV16 & SIGGRAPH 2017 Models!<br>
Practically the algorithm is used to COLORIZE your **old BLACK & WHITE / GRAYSCALE photos**.<br>
To use it, simply just upload the concerned image.<br>
"""
article = r"""
<p style='text-align: '>
Given a grayscale photograph as input, this demo attacks the problem of hallucinating a plausible color version of the photograph. This problem is clearly underconstrained, so previous approaches have either relied on significant user interaction or resulted in desaturated colorizations. A fully automatic approach has been proposed that produces vibrant and realistic colorizations. The underlying uncertainty of the problem was embraced by posing it as a classification task and use class-rebalancing at training time to increase the diversity of colors in the result. The system is implemented as a feed-forward pass in a CNN at test time and is trained on over a million color images. The algorithm is evaluated using a "colorization Turing test," asking human participants to choose between a generated and ground truth color image. The method used here successfully fools humans on 32% of the trials, significantly higher than other methodology used by the other photo automation tools. Moreover, the colorization can be a powerful pretext task for self-supervised feature learning, acting as a cross-channel encoder. This approach results in state-of-the-art performance on several feature learning benchmarks.
</p>
<img src="https://camo.githubusercontent.com/8f849fd53753e61659664d37849703d949209ec93957d1a2fd529be9772f7030/687474703a2f2f726963687a68616e672e6769746875622e696f2f636f6c6f72697a6174696f6e2f7265736f75726365732f696d616765732f746561736572342e6a7067" alt="Teaser Image" data-canonical-src="http://richzhang.github.io/colorization/resources/images/teaser4.jpg" style="max-width: 100%;">
<p>
<img class="round" style="height:275px" src="http://richzhang.github.io/colorization/resources/images/net_diagram.jpg">
</p>

<div class="footer">
                    <p>Built by <a href="https://twitter.com/jadejadushyant" style="text-decoration: underline;" target="_blank">Dushyantsinh Jadeja</a> & the Model being used in here is from <a href="https://github.com/richzhang/colorization" style="text-decoration: underline;" target="_blank">richzhang</a> - Gradio Demo by 🤗 Hugging Face
                    </p>
                </div>
                <div class="acknowledgments">
                    <p><h4>LICENSE</h4>
                    
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<br>
<center><img src='https://visitor-badge.glitch.me/badge?page_id=dj_colorization_eccv16siggraph17' alt='visitor badge'></center>
"""

#with gr.Interface(css=css) as mainBody:
gr.HTML("""<style>
{css}
</Style>""")
  
mainBody = gr.Interface(
    interface,
    [
        gr.components.Image(type="pil", label="image"),
        gr.components.Radio(
            ["eccv16", "siggraph17"],
            type="value",
            label="model"
        )
    ],
    [
        gr.components.Image(label="output")
    ],
    gr.HTML(css=css),
    #inputs="sketchpad",
    #outputs="label",
    theme="huggingface",
    title=title,
    description=description,
    article=article,
    live=True,
)
mainBody.launch()