from __future__ import annotations
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

# def process_image_check(path_input):
#     if path_input is None:
#         raise gr.Error(
#             "Missing image in the first pane: upload a file or use one from the gallery below."
#         )

# def infer(path_input, seed=0):
#     print(f"==> Processing image {path_input}")
#     return path_input
#     return [path_input, path_input]
#     # name_base, name_ext = os.path.splitext(os.path.basename(path_input))
#     # print(f"==> Processing image {name_base}{name_ext}")
#     # device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#     # print(f"==> Device: {device}")
#     # 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)
#     # yield [path_input, g_save_path], [path_input, 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",
#                             minimum=0,
#                             maximum=999999,
#                         )
#                         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 = gr.Image(
#                             label="Output (Generative)",
#                             type="filepath",
#                         )
#                         # image_output_g = ImageSlider(
#                         #     label="Output (Generative)",
#                         #     type="filepath",
#                         #     show_download_button=True,
#                         #     show_share_button=True,
#                         #     interactive=False,
#                         #     elem_classes="slider",
#                         #     position=0.25,
#                         # )
#                         # with gr.Row():
#                         #     image_output_d = gr.Image(
#                         #         label="Output (Generative)",
#                         #         type="filepath",
#                         #     )
#                         #     image_output_d = ImageSlider(
#                         #         label="Output (Discriminative)",
#                         #         type="filepath",
#                         #         show_download_button=True,
#                         #         show_share_button=True,
#                         #         interactive=False,
#                         #         elem_classes="slider",
#                         #         position=0.25,
#                         #     )

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

#             with gr.Tab("VIDEO"):
#                 with gr.Column():
#                     gr.Markdown("Coming soon")

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

#         ### Video

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

# def main():
#     os.system("pip freeze")
#     run_demo_server()

# if __name__ == "__main__":
#     main()

def flip_text(x):
    return x[::-1]

def flip_image(x):
    return np.fliplr(x)

with gr.Blocks() as demo:
    gr.Markdown("Flip text or image files using this demo.")
    with gr.Tab("Flip Text"):
        text_input = gr.Textbox()
        text_output = gr.Textbox()
        text_button = gr.Button("Flip")
    with gr.Tab("Flip Image"):
        with gr.Row():
            image_input = gr.Image()
            image_output = gr.Image()
        image_button = gr.Button("Flip")

    with gr.Accordion("Open for More!", open=False):
        gr.Markdown("Look at me...")
        temp_slider = gr.Slider(
            0, 1,
            value=0.1,
            step=0.1,
            interactive=True,
            label="Slide me",
        )

    text_button.click(flip_text, inputs=text_input, outputs=text_output)
    image_button.click(flip_image, inputs=image_input, outputs=image_output)

demo.launch(share=True)