import gradio as gr
from model import NeuralStyleTransfer
import tensorflow as tf


def model_fn(
    style, content, extractor="inception_v3", n_content_layers=3, n_style_layers=2,
    epochs=4, learning_rate=60.0, steps_per_epoch=100, style_weight=1e-2,
):
    model = NeuralStyleTransfer(
        style_image=style,
        content_image=content,
        extractor=extractor,
        n_content_layers=n_content_layers,
        n_style_layers=n_style_layers,
    )

    return model.fit_style_transfer(
        epochs=10,
        learning_rate=80.0,
        steps_per_epoch=100,
        style_weight=1e-2,
        content_weight=1e-4,
        show_image=True,
        show_interval=90,
        var_weight=1e-12,
        terminal=False,
    )


def hugging_face():
    demo = gr.Interface(
        fn=model_fn,
        inputs=[
            "image",
            "image",
            gr.Dropdown(
                ["inception_v3", "vgg19", "resnet50", "mobilenet_v2"],
                label="extractor",
                default="inception_v3",
                info="Feature extractor to use.",
            ),
            gr.Slider(
                1,
                5,
                value=3,
                label="n_content_layers",
                info="Number of content layers to use.",
            ),
            gr.Slider(
                1,
                5,
                value=2,
                label="n_style_layers",
                info="Number of style layers to use.",
            ),
            gr.Slider(
                2, 20, value=4, label="epochs", info="Number of epochs to train for."
            ),
            gr.Slider(
                1, 100, value=60, label="learning_rate", info="Initial Learning rate."
            ),
            gr.Slider(
                1,
                100,
                value=100,
                label="steps_per_epoch",
                info="Number of steps per epoch.",
            ),
            gr.Slider(
                1e-4,
                1e-2,
                value=1e-2,
                label="style_weight",
                info="Weight of style loss.",
            ),
            gr.Slider(
                1e-4,
                1e-2,
                value=1e-4,
                label="content_weight",
                info="Weight of content loss.",
            ),
            gr.Slider(
                1e-12,
                1e-9,
                value=1e-12,
                label="var_weight",
                info="Weight of total variation loss.",
            ),
        ],
        outputs="image",
    )

    return demo


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