from gradio_imageslider import ImageSlider
import functools
import os
import tempfile
import diffusers
import gradio as gr
import imageio as imageio
import numpy as np
import spaces
import torch as torch
from PIL import Image
from tqdm import tqdm
from pathlib import Path
import gradio
from gradio.utils import get_cache_folder
from infer import lotus, lotus_video

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

def infer(path_input, seed):
    name_base, name_ext = os.path.splitext(os.path.basename(path_input))
    output_g, output_d = lotus(path_input, 'depth', seed, device)
    if not os.path.exists("files/output"):
        os.makedirs("files/output")
    g_save_path = os.path.join("files/output", f"{name_base}_g{name_ext}")
    d_save_path = os.path.join("files/output", f"{name_base}_d{name_ext}")
    output_g.save(g_save_path)
    output_d.save(d_save_path)
    return [path_input, g_save_path], [path_input, d_save_path]

def infer_video(path_input, seed):
    frames_g, frames_d = lotus_video(path_input, 'depth', seed, device)
    if not os.path.exists("files/output"):
        os.makedirs("files/output")
    name_base, _ = os.path.splitext(os.path.basename(path_input))
    g_save_path = os.path.join("files/output", f"{name_base}_g.mp4")
    d_save_path = os.path.join("files/output", f"{name_base}_d.mp4")
    imageio.mimsave(g_save_path, frames_g)
    imageio.mimsave(d_save_path, frames_d)
    return [g_save_path, d_save_path]

def run_demo_server():
    gradio_theme = gr.themes.Default()

    with gr.Blocks(
        theme=gradio_theme,
        title="LOTUS (Depth)",
        css="""
            #download {
                height: 118px;
            }
            .slider .inner {
                width: 5px;
                background: #FFF;
            }
            .viewport {
                aspect-ratio: 4/3;
            }
            .tabs button.selected {
                font-size: 20px !important;
                color: crimson !important;
            }
            h1 {
                text-align: center;
                display: block;
            }
            h2 {
                text-align: center;
                display: block;
            }
            h3 {
                text-align: center;
                display: block;
            }
            .md_feedback li {
                margin-bottom: 0px !important;
            }
        """,
        head="""
            <script async src="https://www.googletagmanager.com/gtag/js?id=G-1FWSVCGZTG"></script>
            <script>
                window.dataLayer = window.dataLayer || [];
                function gtag() {dataLayer.push(arguments);}
                gtag('js', new Date());
                gtag('config', 'G-1FWSVCGZTG');
            </script>
        """,
    ) as demo:
        gr.Markdown(
            """
            # LOTUS: Diffusion-based Visual Foundation Model for High-quality Dense Prediction
            <p align="center">
            <a title="Page" href="https://lotus3d.github.io/" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
                <img src="https://img.shields.io/badge/Project-Website-pink?logo=googlechrome&logoColor=white">
            </a>
            <a title="arXiv" href="https://arxiv.org/abs/2409.18124" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
                <img src="https://img.shields.io/badge/arXiv-Paper-b31b1b?logo=arxiv&logoColor=white">
            </a>
            <a title="Github" href="https://github.com/EnVision-Research/Lotus" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
                <img src="https://img.shields.io/github/stars/EnVision-Research/Lotus?label=GitHub%20%E2%98%85&logo=github&color=C8C" alt="badge-github-stars">
            </a>
            <a title="Social" href="https://x.com/haodongli00/status/1839524569058582884" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
                <img src="https://www.obukhov.ai/img/badges/badge-social.svg" alt="social">
            </a>
        """
        )
        with gr.Tabs(elem_classes=["tabs"]):
            with gr.Tab("IMAGE"):
                with gr.Row():
                    with gr.Column():
                        image_input = gr.Image(
                            label="Input Image",
                            type="filepath",
                        )
                        seed = gr.Number(
                            label="Seed (only for Generative mode)",
                            minimum=0,
                            maximum=999999999,
                        )
                        with gr.Row():
                            image_submit_btn = gr.Button(
                                value="Predict Depth!", variant="primary"
                            )
                            image_reset_btn = gr.Button(value="Reset")
                    with gr.Column():
                        image_output_g = ImageSlider(
                            label="Output (Generative)",
                            type="filepath",
                            interactive=False,
                            elem_classes="slider",
                            position=0.25,
                        )
                        with gr.Row():
                            image_output_d = ImageSlider(
                                label="Output (Discriminative)",
                                type="filepath",
                                interactive=False,
                                elem_classes="slider",
                                position=0.25,
                            )

                gr.Examples(
                    fn=infer,
                    examples=sorted([
                        [os.path.join("files", "images", name), 0]
                        for name in os.listdir(os.path.join("files", "images"))
                    ]),
                    inputs=[image_input, seed],
                    outputs=[image_output_g, image_output_d],
                    cache_examples=False,
                )

            with gr.Tab("VIDEO"):
                with gr.Row():
                    with gr.Column():
                        input_video = gr.Video(
                            label="Input Video",
                            autoplay=True,
                            loop=True,
                        )
                        seed = gr.Number(
                            label="Seed (only for Generative mode)",
                            minimum=0,
                            maximum=999999999,
                        )
                        with gr.Row():
                            video_submit_btn = gr.Button(
                                value="Predict Depth!", variant="primary"
                            )
                            video_reset_btn = gr.Button(value="Reset")
                    with gr.Column():
                        video_output_g = gr.Video(
                            label="Output (Generative)",
                            interactive=False,
                            autoplay=True,
                            loop=True,
                            show_share_button=True,
                        )
                        with gr.Row():
                            video_output_d = gr.Video(
                                label="Output (Discriminative)",
                                interactive=False,
                                autoplay=True,
                                loop=True,
                                show_share_button=True,
                            )

                gr.Examples(
                    fn=infer_video,
                    examples=sorted([
                        [os.path.join("files", "videos", name), 0]
                        for name in os.listdir(os.path.join("files", "videos"))
                    ]),
                    inputs=[input_video, seed],
                    outputs=[video_output_g, video_output_d],
                    cache_examples=False,
                )

        ### Image
        image_submit_btn.click(
            fn=infer,
            inputs=[image_input, seed],
            outputs=[image_output_g, image_output_d],
            concurrency_limit=1,
        )
        image_reset_btn.click(
            fn=lambda: (
                None,
                None,
                None,
            ),
            inputs=[],
            outputs=[image_output_g, image_output_d],
            queue=False,
        )

        ### Video
        video_submit_btn.click(
            fn=infer_video,
            inputs=[input_video, seed],
            outputs=[video_output_g, video_output_d],
            queue=True,
        )
        video_reset_btn.click(
            fn=lambda: (None, None, None),
            inputs=[],
            outputs=[video_output_g, video_output_d],
        )

        ### Server launch
        demo.queue(
            api_open=False,
        ).launch(
            server_name="0.0.0.0",
            server_port=7860,
        )

def main():
    os.system("pip freeze")
    if os.path.exists("files/output"):
        os.system("rm -rf files/output")
    run_demo_server()

if __name__ == "__main__":
    main()