#!/usr/bin/env python

from __future__ import annotations

import gradio as gr
import torch
torch.jit.script = lambda f: f
import spaces

from app_canny import create_demo as create_demo_canny
from app_depth import create_demo as create_demo_depth
from app_lineart import create_demo as create_demo_lineart
from app_segmentation import create_demo as create_demo_segmentation
from app_softedge import create_demo as create_demo_softedge
from model import Model
from settings import ALLOW_CHANGING_BASE_MODEL, DEFAULT_MODEL_ID, SHOW_DUPLICATE_BUTTON
from transformers.utils.hub import move_cache
move_cache()

DESCRIPTION = "# [ControlNet++: Improving Conditional Controls with Efficient Consistency Feedback](https://arxiv.org/abs/2404.07987) \n ### The first row in outputs is the input conditions. The second row is the images generated by ControlNet++. The third row is the conditions extracted from our generated images. Please note that we use the SD1.5 and trained on specific public datasets, so the quality of the generated images may not be as good as models such as SDXL-based models, or trained on private datasets. For example, the image quality and resolution in the ADE20K dataset (Segmentation) are often poor \n **We noticed that the HuggingFace online demo had unstable and worse results, possibly caused by ZeroGPU. We strongly recommend running locally, following the instruction on our [Github Repo](https://github.com/liming-ai/ControlNet_Plus_Plus).**"

if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"

model = Model(base_model_id=DEFAULT_MODEL_ID, task_name="Canny")


with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    gr.DuplicateButton(
        value="Duplicate Space for private use",
        elem_id="duplicate-button",
        visible=SHOW_DUPLICATE_BUTTON,
    )

    with gr.Tabs():
        with gr.TabItem("Lineart"):
            create_demo_lineart(model.process_lineart)
        with gr.TabItem("Depth"):
            create_demo_depth(model.process_depth)
        with gr.TabItem("Segmentation"):
            create_demo_segmentation(model.process_segmentation)
        with gr.TabItem("SoftEdge"):
            create_demo_softedge(model.process_softedge)
        with gr.TabItem("Canny"):
            create_demo_canny(model.process_canny)

    with gr.Accordion(label="Base model", open=False):
        with gr.Row():
            with gr.Column(scale=5):
                current_base_model = gr.Text(label="Current base model")
            with gr.Column(scale=1):
                check_base_model_button = gr.Button("Check current base model")
        with gr.Row():
            with gr.Column(scale=5):
                new_base_model_id = gr.Text(
                    label="New base model",
                    max_lines=1,
                    placeholder="botp/stable-diffusion-v1-5",
                    info="The base model must be compatible with Stable Diffusion v1.5.",
                    interactive=ALLOW_CHANGING_BASE_MODEL,
                )
            with gr.Column(scale=1):
                change_base_model_button = gr.Button("Change base model", interactive=ALLOW_CHANGING_BASE_MODEL)
        if not ALLOW_CHANGING_BASE_MODEL:
            gr.Markdown(
                """The base model is not allowed to be changed in this Space so as not to slow down the demo, but it can be changed if you duplicate the Space."""
            )

    check_base_model_button.click(
        fn=lambda: model.base_model_id,
        outputs=current_base_model,
        queue=False,
        api_name="check_base_model",
    )
    gr.on(
        triggers=[new_base_model_id.submit, change_base_model_button.click],
        fn=model.set_base_model,
        inputs=new_base_model_id,
        outputs=current_base_model,
        api_name=False,
    )

if __name__ == "__main__":
    demo.queue(max_size=20).launch()