#!/usr/bin/env python

import gradio as gr

from settings import (
    DEFAULT_IMAGE_RESOLUTION,
    DEFAULT_NUM_IMAGES,
    MAX_IMAGE_RESOLUTION,
    MAX_NUM_IMAGES,
    MAX_SEED,
)
from utils import randomize_seed_fn


examples = [
    [
        "images/lineart/Ryan Reynolds_1.png",
        "Ryan Reynolds",
        "None",
    ],
    [
        "images/lineart/Ryan Reynolds_2.png",
        "Ryan Reynolds",
        "None",
    ],
    [
        "images/lineart/Ryan Reynolds_3.png",
        "Ryan Reynolds",
        "None",
    ],
    [
        "images/lineart/tube.jpg",
        "Picture Of Looking Through A View Finder",
        "Lineart",
    ],
]


def create_demo(process):
    with gr.Blocks() as demo:
        with gr.Row():
            with gr.Column():
                image = gr.Image()
                prompt = gr.Textbox(label="Prompt")
                run_button = gr.Button("Run")
                with gr.Accordion("Advanced options", open=False):
                    preprocessor_name = gr.Radio(
                        label="Preprocessor",
                        choices=[
                            "Lineart",
                            "Lineart coarse",
                            "None",
                            "Lineart (anime)",
                            "None (anime)",
                        ],
                        type="value",
                        value="Lineart",
                        info='Please note that the only difference here is pre-processing. We use exactly the same ControlNet++ model to generate images.',
                    )
                    num_samples = gr.Slider(
                        label="Number of images", minimum=1, maximum=MAX_NUM_IMAGES, value=DEFAULT_NUM_IMAGES, step=1
                    )
                    image_resolution = gr.Slider(
                        label="Image resolution",
                        minimum=256,
                        maximum=MAX_IMAGE_RESOLUTION,
                        value=DEFAULT_IMAGE_RESOLUTION,
                        step=256,
                    )
                    preprocess_resolution = gr.Slider(
                        label="Preprocess resolution", minimum=128, maximum=512, value=512, step=1
                    )
                    num_steps = gr.Slider(label="Number of steps", minimum=1, maximum=100, value=20, step=1)
                    guidance_scale = gr.Slider(label="Guidance scale", minimum=0.1, maximum=30.0, value=7.5, step=0.1)
                    seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
                    a_prompt = gr.Textbox(label="Additional prompt")
                    n_prompt = gr.Textbox(
                        label="Negative prompt",
                    )
            with gr.Column():
                result = gr.Gallery(label="Output", show_label=False, columns=2, object_fit="scale-down")


        gr.Examples(
            examples=examples,
            inputs=[
                image,
                prompt,
                preprocessor_name
            ],
            outputs=result,
            fn=process,
        )

        inputs = [
            image,
            prompt,
            a_prompt,
            n_prompt,
            num_samples,
            image_resolution,
            preprocess_resolution,
            num_steps,
            guidance_scale,
            seed,
            preprocessor_name,
        ]
        prompt.submit(
            fn=randomize_seed_fn,
            inputs=[seed, randomize_seed],
            outputs=seed,
            queue=False,
            api_name=False,
        ).then(
            fn=process,
            inputs=inputs,
            outputs=result,
            api_name=False,
        )
        run_button.click(
            fn=randomize_seed_fn,
            inputs=[seed, randomize_seed],
            outputs=seed,
            queue=False,
            api_name=False,
        ).then(
            fn=process,
            inputs=inputs,
            outputs=result,
            api_name="lineart",
        )
    return demo


if __name__ == "__main__":
    from model import Model

    model = Model(task_name="lineart")
    demo = create_demo(model.process_lineart)
    demo.queue().launch()