import gradio as gr
import cv2
import matplotlib
import numpy as np
import os
from PIL import Image
import spaces
import torch
import tempfile
from gradio_imageslider import ImageSlider
from huggingface_hub import hf_hub_download

from ppd.utils.set_seed import set_seed
from ppd.models.ppd import PixelPerfectDepth

css = """
#img-display-container {
    max-height: 100vh;
}
#img-display-input {
    max-height: 100vh;
}
#img-display-output {
    max-height: 100vh;
}
#download {
    height: 62px;
}

#img-display-output .image-slider-image {
    object-fit: contain !important;
    width: 100% !important;
    height: 100% !important;
}
"""

DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
model = PixelPerfectDepth(sampling_steps=4)
ckpt_path = hf_hub_download(
    repo_id="gangweix/Pixel-Perfect-Depth",
    filename="ppd.pth",
    repo_type="model"
)
state_dict = torch.load(ckpt_path, map_location="cpu")
model.load_state_dict(state_dict, strict=False)
model = model.to(DEVICE).eval()



title = "# Pixel-Perfect Depth"
description = """Official demo for **Pixel-Perfect Depth**.
Please refer to our [paper](), [project page](https://pixel-perfect-depth.github.io), and [github](https://github.com/gangweix/pixel-perfect-depth) for more details."""

@spaces.GPU
def predict_depth(image):
    return model.infer_image(image)

with gr.Blocks(css=css) as demo:
    gr.Markdown(title)
    gr.Markdown(description)
    gr.Markdown("### Depth Prediction demo")

    with gr.Row():
        input_image = gr.Image(label="Input Image", type='numpy', elem_id='img-display-input')
        depth_image_slider = ImageSlider(label="Depth Map with Slider View", elem_id='img-display-output', position=0.5)
    submit = gr.Button(value="Predict Depth")
    
    concat_file = gr.File(label="Concatenated visualization (image+depth)", elem_id="image-depth-download")
    raw_file = gr.File(label="Raw depth output (saved as .npy)", elem_id="download",)

    cmap = matplotlib.colormaps.get_cmap('Spectral')

    def on_submit(image):
        original_image = image.copy()

        depth = predict_depth(image[:, :, ::-1])

        # save raw depth (npy)
        tmp_raw_depth = tempfile.NamedTemporaryFile(suffix='.npy', delete=False)
        np.save(tmp_raw_depth.name, depth)


        depth_vis = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
        depth_vis = depth_vis.astype(np.uint8)
        colored_depth = (cmap(depth_vis)[:, :, :3] * 255).astype(np.uint8)

        split_region = np.ones((image.shape[0], 50, 3), dtype=np.uint8) * 255
        combined_result = cv2.hconcat([image[:, :, ::-1], split_region, colored_depth[:, :, ::-1]])
        tmp_concat = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
        cv2.imwrite(tmp_concat.name, combined_result)

        return [(original_image, colored_depth), tmp_concat.name, tmp_raw_depth.name]

    submit.click(
        on_submit, 
        inputs=[input_image], 
        outputs=[depth_image_slider, concat_file, raw_file]
    )

    example_files = os.listdir('assets/examples')
    example_files.sort()
    example_files = [os.path.join('assets/examples', filename) for filename in example_files]
    examples = gr.Examples(
        examples=example_files, 
        inputs=[input_image], 
        outputs=[depth_image_slider, concat_file, raw_file], 
        fn=on_submit
    )


if __name__ == '__main__':
    demo.queue().launch(share=True)