# Copyright 2023-2025 Marigold Team, ETH Zürich. All rights reserved.
# This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.
# See https://creativecommons.org/licenses/by-sa/4.0/ for details.
# --------------------------------------------------------------------------
# DualVision is a Gradio template app for image processing. It was developed
# to support the Marigold project. If you find this code useful, we kindly
# ask you to cite our most relevant papers.
# More information about Marigold:
#   https://marigoldmonodepth.github.io
#   https://marigoldcomputervision.github.io
# Efficient inference pipelines are now part of diffusers:
#   https://huggingface.co/docs/diffusers/using-diffusers/marigold_usage
#   https://huggingface.co/docs/diffusers/api/pipelines/marigold
# Examples of trained models and live demos:
#   https://huggingface.co/prs-eth
# Related projects:
#   https://marigolddepthcompletion.github.io/
#   https://rollingdepth.github.io/
# Citation (BibTeX):
#   https://github.com/prs-eth/Marigold#-citation
#   https://github.com/prs-eth/Marigold-DC#-citation
#   https://github.com/prs-eth/rollingdepth#-citation
# --------------------------------------------------------------------------
import gradio
import gradio.component_meta as _component_meta

def _noop_create_or_modify_pyi(*args, **kwargs):
    return None

_component_meta.create_or_modify_pyi = _noop_create_or_modify_pyi

import numpy as np
import PIL.Image
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from urllib.parse import quote, urlparse

from gradio import FileData, image_utils, processing_utils, utils, wasm_utils
from gradio.components.gallery import (
    GalleryData,
    GalleryImage,
    GalleryMediaType,
    CaptionedGalleryMediaType,
    GalleryVideo,
)
from gradio_client import utils as client_utils
from gradio_client.utils import is_http_url_like
from gradio.data_classes import ImageData


class Gallery(gradio.Gallery):

    def postprocess(
        self,
        value: list[GalleryMediaType | CaptionedGalleryMediaType] | None,
    ) -> GalleryData:
        """
        Parameters:
            value: Expects the function to return a `list` of images or videos, or `list` of (media, `str` caption) tuples. Each image can be a `str` file path, a `numpy` array, or a `PIL.Image` object. Each video can be a `str` file path.
        Returns:
            a list of images or videos, or list of (media, caption) tuples
        """
        if value is None:
            return GalleryData(root=[])
        if isinstance(value, str):
            raise ValueError(
                "The `value` passed into `gr.Gallery` must be a list of images or videos, or list of (media, caption) tuples."
            )
        output = []

        def _save(img):
            url = None
            caption = None
            orig_name = None
            mime_type = None
            if isinstance(img, (tuple, list)):
                img, caption = img
            if isinstance(img, np.ndarray):
                file = processing_utils.save_img_array_to_cache(
                    img, cache_dir=self.GRADIO_CACHE, format=self.format
                )
                file_path = str(utils.abspath(file))
            elif isinstance(img, PIL.Image.Image):
                format = (
                    "png" if img.mode == "I;16" else self.format
                )  # Patch 1: change format based on the inbound dtype
                file = processing_utils.save_pil_to_cache(
                    img, cache_dir=self.GRADIO_CACHE, format=format
                )
                file_path = str(utils.abspath(file))
            elif isinstance(img, str):
                mime_type = client_utils.get_mimetype(img)
                if img.lower().endswith(".svg"):
                    svg_content = image_utils.extract_svg_content(img)
                    orig_name = Path(img).name
                    url = f"data:image/svg+xml,{quote(svg_content)}"
                    file_path = None
                elif is_http_url_like(img):
                    url = img
                    orig_name = Path(urlparse(img).path).name
                    file_path = img
                else:
                    url = None
                    orig_name = Path(img).name
                    file_path = img
            elif isinstance(img, Path):
                file_path = str(img)
                orig_name = img.name
                mime_type = client_utils.get_mimetype(file_path)
            else:
                raise ValueError(f"Cannot process type as image: {type(img)}")
            if mime_type is not None and "video" in mime_type:
                return GalleryVideo(
                    video=FileData(
                        path=file_path,  # type: ignore
                        url=url,
                        orig_name=orig_name,
                        mime_type=mime_type,
                    ),
                    caption=caption,
                )
            else:
                return GalleryImage(
                    image=ImageData(
                        path=file_path,
                        url=url,
                        orig_name=orig_name,
                        mime_type=mime_type,
                    ),
                    caption=caption,
                )

        if wasm_utils.IS_WASM:
            for img in value:
                output.append(_save(img))
        else:
            with ThreadPoolExecutor() as executor:
                for o in executor.map(_save, value):
                    output.append(o)
        return GalleryData(root=output)