#!/usr/bin/env python

from __future__ import annotations

import pathlib

import gradio as gr
import numpy as np

from model import Model

DESCRIPTION = "# [Self-Distilled StyleGAN](https://github.com/self-distilled-stylegan/self-distilled-internet-photos)"


def get_sample_image_url(name: str) -> str:
    sample_image_dir = "https://huggingface.co/spaces/hysts/Self-Distilled-StyleGAN/resolve/main/samples"
    return f"{sample_image_dir}/{name}.jpg"


def get_sample_image_markdown(name: str) -> str:
    url = get_sample_image_url(name)
    size = name.split("_")[1]
    truncation_type = "_".join(name.split("_")[2:])
    return f"""
    - size: {size}x{size}
    - seed: 0-99
    - truncation: 0.7
    - truncation type: {truncation_type}
    ![sample images]({url})"""


def get_cluster_center_image_url(model_name: str) -> str:
    cluster_center_image_dir = (
        "https://huggingface.co/spaces/hysts/Self-Distilled-StyleGAN/resolve/main/cluster_center_images"
    )
    return f"{cluster_center_image_dir}/{model_name}.jpg"


def get_cluster_center_image_markdown(model_name: str) -> str:
    url = get_cluster_center_image_url(model_name)
    return f"![cluster center images]({url})"


model = Model()

with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)

    with gr.Tabs():
        with gr.TabItem("App"):
            with gr.Row():
                with gr.Column():
                    with gr.Group():
                        model_name = gr.Dropdown(label="Model", choices=model.MODEL_NAMES, value=model.MODEL_NAMES[0])
                        seed = gr.Slider(label="Seed", minimum=0, maximum=np.iinfo(np.uint32).max, step=1, value=0)
                        psi = gr.Slider(label="Truncation psi", minimum=0, maximum=2, step=0.05, value=0.7)
                        truncation_type = gr.Dropdown(
                            label="Truncation Type", choices=model.TRUNCATION_TYPES, value=model.TRUNCATION_TYPES[0]
                        )
                        run_button = gr.Button("Run")
                with gr.Column():
                    result = gr.Image(label="Result", elem_id="result")

        with gr.TabItem("Sample Images"):
            with gr.Row():
                paths = sorted(pathlib.Path("samples").glob("*"))
                names = [path.stem for path in paths]
                model_name2 = gr.Dropdown(label="Type", choices=names, value="dogs_1024_multimodal_lpips")
            with gr.Row():
                text = get_sample_image_markdown(model_name2.value)
                sample_images = gr.Markdown(text)

        with gr.TabItem("Cluster Center Images"):
            with gr.Row():
                model_name3 = gr.Dropdown(label="Model", choices=model.MODEL_NAMES, value=model.MODEL_NAMES[0])
            with gr.Row():
                text = get_cluster_center_image_markdown(model_name3.value)
                cluster_center_images = gr.Markdown(value=text)

    model_name.change(
        fn=model.set_model,
        inputs=model_name,
    )
    run_button.click(
        fn=model.set_model_and_generate_image,
        inputs=[
            model_name,
            seed,
            psi,
            truncation_type,
        ],
        outputs=result,
    )
    model_name2.change(
        fn=get_sample_image_markdown,
        inputs=model_name2,
        outputs=sample_images,
    )
    model_name3.change(
        fn=get_cluster_center_image_markdown,
        inputs=model_name3,
        outputs=cluster_center_images,
    )

if __name__ == "__main__":
    demo.queue(max_size=10).launch()