#!/usr/bin/env python

from __future__ import annotations

import os
import random

import gradio as gr
import numpy as np
import torch

from model import Model

DESCRIPTION = "# [UniDiffuser](https://github.com/thu-ml/unidiffuser)"

SPACE_ID = os.getenv("SPACE_ID")
if SPACE_ID is not None:
    DESCRIPTION += f'\n<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>'
if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶</p>"

model = Model()

MAX_SEED = np.iinfo(np.int32).max


def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    return seed


def create_demo(mode_name: str) -> gr.Blocks:
    with gr.Blocks() as demo:
        with gr.Row():
            with gr.Column():
                mode = gr.Dropdown(
                    label="Mode",
                    choices=[
                        "t2i",
                        "i2t",
                        "joint",
                        "i",
                        "t",
                        "i2t2i",
                        "t2i2t",
                    ],
                    value=mode_name,
                    visible=False,
                )
                prompt = gr.Text(label="Prompt", max_lines=1, visible=mode_name in ["t2i", "t2i2t"])
                image = gr.Image(label="Input image", type="pil", visible=mode_name in ["i2t", "i2t2i"])
                run_button = gr.Button("Run")
                with gr.Accordion("Advanced options", open=False):
                    seed = gr.Slider(
                        label="Seed",
                        minimum=0,
                        maximum=MAX_SEED,
                        step=1,
                        value=0,
                    )
                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
                    num_steps = gr.Slider(
                        label="Steps",
                        minimum=1,
                        maximum=100,
                        value=20,
                        step=1,
                    )
                    guidance_scale = gr.Slider(
                        label="Guidance Scale",
                        minimum=0.1,
                        maximum=30.0,
                        value=8.0,
                        step=0.1,
                    )
            with gr.Column():
                result_image = gr.Image(label="Generated image", visible=mode_name in ["t2i", "i", "joint", "i2t2i"])
                result_text = gr.Text(label="Generated text", visible=mode_name in ["i2t", "t", "joint", "t2i2t"])
        inputs = [
            mode,
            prompt,
            image,
            seed,
            num_steps,
            guidance_scale,
        ]
        outputs = [
            result_image,
            result_text,
        ]

        prompt.submit(
            fn=randomize_seed_fn,
            inputs=[seed, randomize_seed],
            outputs=seed,
            queue=False,
        ).then(
            fn=model.run,
            inputs=inputs,
            outputs=outputs,
        )
        run_button.click(
            fn=randomize_seed_fn,
            inputs=[seed, randomize_seed],
            outputs=seed,
            queue=False,
        ).then(
            fn=model.run,
            inputs=inputs,
            outputs=outputs,
            api_name=f"run_{mode_name}",
        )
    return demo


with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    with gr.Tabs():
        with gr.TabItem("text2image"):
            create_demo("t2i")
        with gr.TabItem("image2text"):
            create_demo("i2t")
        with gr.TabItem("image variation"):
            create_demo("i2t2i")
        with gr.TabItem("joint generation"):
            create_demo("joint")
        with gr.TabItem("image generation"):
            create_demo("i")
        with gr.TabItem("text generation"):
            create_demo("t")
        with gr.TabItem("text variation"):
            create_demo("t2i2t")

if __name__ == "__main__":
    demo.queue(max_size=15).launch()