"""gr.Image() component."""

from __future__ import annotations

import warnings
from pathlib import Path
from typing import Any, Literal, cast

import numpy as np
from gradio_client.documentation import document, set_documentation_group
from PIL import Image as _Image  # using _ to minimize namespace pollution
from PIL import ImageOps

import gradio.image_utils as image_utils
from gradio import utils
from gradio.components.base import Component, StreamingInput
from gradio.data_classes import FileData
from gradio.events import Events

set_documentation_group("component")
_Image.init()  # fixes https://github.com/gradio-app/gradio/issues/2843

from gradio.events import Dependency

@document()
class ImageFeed(StreamingInput, Component):
    """
    Creates an image component that can be used to upload images (as an input) or display images (as an output).
    Preprocessing: passes the uploaded image as a {numpy.array}, {PIL.Image} or {str} filepath depending on `type`. For SVGs, the `type` parameter is ignored and the filepath of the SVG is returned.
    Postprocessing: expects a {numpy.array}, {PIL.Image} or {str} or {pathlib.Path} filepath to an image and displays the image.
    Examples-format: a {str} local filepath or URL to an image.
    Demos: image_mod, image_mod_default_image
    Guides: image-classification-in-pytorch, image-classification-in-tensorflow, image-classification-with-vision-transformers, create-your-own-friends-with-a-gan
    """

    EVENTS = []

    data_model = FileData

    def __init__(
        self,
        value: str | _Image.Image | np.ndarray | None = None,
        *,
        height: int | str | None = None,
        width: int | str | None = None,
        image_mode: Literal[
            "1", "L", "P", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"
        ] = "RGB",
        sources: list[Literal["upload", "webcam", "clipboard"]] | None = None,
        type: Literal["numpy", "pil", "filepath"] = "numpy",
        label: str | None = None,
        every: float | None = None,
        show_label: bool | None = None,
        show_download_button: bool = True,
        container: bool = True,
        scale: int | None = None,
        min_width: int = 160,
        interactive: bool | None = None,
        visible: bool = True,
        streaming: bool = False,
        elem_id: str | None = None,
        elem_classes: list[str] | str | None = None,
        render: bool = True,
        mirror_webcam: bool = True,
        show_share_button: bool | None = None,
    ):
        """
        Parameters:
            value: A PIL ImageFeed, numpy array, path or URL for the default value that ImageFeed component is going to take. If callable, the function will be called whenever the app loads to set the initial value of the component.
            height: The height of the displayed image, specified in pixels if a number is passed, or in CSS units if a string is passed.
            width: The width of the displayed image, specified in pixels if a number is passed, or in CSS units if a string is passed.
            image_mode: "RGB" if color, or "L" if black and white. See https://pillow.readthedocs.io/en/stable/handbook/concepts.html for other supported image modes and their meaning.
            sources: List of sources for the image. "upload" creates a box where user can drop an image file, "webcam" allows user to take snapshot from their webcam, "clipboard" allows users to paste an image from the clipboard. If None, defaults to ["upload", "webcam", "clipboard"] if streaming is False, otherwise defaults to ["webcam"].
            type: The format the image is converted before being passed into the prediction function. "numpy" converts the image to a numpy array with shape (height, width, 3) and values from 0 to 255, "pil" converts the image to a PIL image object, "filepath" passes a str path to a temporary file containing the image. If the image is SVG, the `type` is ignored and the filepath of the SVG is returned.
            label: The label for this component. Appears above the component and is also used as the header if there are a table of examples for this component. If None and used in a `gr.Interface`, the label will be the name of the parameter this component is assigned to.
            every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
            show_label: if True, will display label.
            show_download_button: If True, will display button to download image.
            container: If True, will place the component in a container - providing some extra padding around the border.
            scale: relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer.
            min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
            interactive: if True, will allow users to upload and edit an image; if False, can only be used to display images. If not provided, this is inferred based on whether the component is used as an input or output.
            visible: If False, component will be hidden.
            streaming: If True when used in a `live` interface, will automatically stream webcam feed. Only valid is source is 'webcam'.
            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
            elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
            render: If False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.
            mirror_webcam: If True webcam will be mirrored. Default is True.
            show_share_button: If True, will show a share icon in the corner of the component that allows user to share outputs to Hugging Face Spaces Discussions. If False, icon does not appear. If set to None (default behavior), then the icon appears if this Gradio app is launched on Spaces, but not otherwise.
        """
        self.mirror_webcam = mirror_webcam
        valid_types = ["numpy", "pil", "filepath"]
        if type not in valid_types:
            raise ValueError(
                f"Invalid value for parameter `type`: {type}. Please choose from one of: {valid_types}"
            )
        self.type = type
        self.height = height
        self.width = width
        self.image_mode = image_mode
        valid_sources = ["upload", "webcam", "clipboard"]
        if sources is None:
            self.sources = (
                ["webcam"] if streaming else ["upload", "webcam", "clipboard"]
            )
        elif isinstance(sources, str):
            self.sources = [sources]  # type: ignore
        else:
            self.sources = sources
        for source in self.sources:  # type: ignore
            if source not in valid_sources:
                raise ValueError(
                    f"`sources` must a list consisting of elements in {valid_sources}"
                )
        self.streaming = streaming
        self.show_download_button = show_download_button
        if streaming and self.sources != ["webcam"]:
            raise ValueError(
                "ImageFeed streaming only available if sources is ['webcam']. Streaming not supported with multiple sources."
            )
        self.show_share_button = (
            (utils.get_space() is not None)
            if show_share_button is None
            else show_share_button
        )
        super().__init__(
            label=label,
            every=every,
            show_label=show_label,
            container=container,
            scale=scale,
            min_width=min_width,
            interactive=interactive,
            visible=visible,
            elem_id=elem_id,
            elem_classes=elem_classes,
            render=render,
            value=value,
        )

    def preprocess(
        self, payload: FileData | None
    ) -> np.ndarray | _Image.Image | str | None:
        if payload is None:
            return payload
        file_path = Path(payload.path)
        if payload.orig_name:
            p = Path(payload.orig_name)
            name = p.stem
            suffix = p.suffix.replace(".", "")
            if suffix in ["jpg", "jpeg"]:
                suffix = "jpeg"
        else:
            name = "image"
            suffix = "png"

        if suffix.lower() == "svg":
            return str(file_path)

        im = _Image.open(file_path)
        exif = im.getexif()
        # 274 is the code for image rotation and 1 means "correct orientation"
        if exif.get(274, 1) != 1 and hasattr(ImageOps, "exif_transpose"):
            try:
                im = ImageOps.exif_transpose(im)
            except Exception:
                warnings.warn(
                    f"Failed to transpose image {file_path} based on EXIF data."
                )
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            im = im.convert(self.image_mode)
        return image_utils.format_image(
            im,
            cast(Literal["numpy", "pil", "filepath"], self.type),
            self.GRADIO_CACHE,
            name=name,
            format=suffix,
        )

    def postprocess(
        self, value: np.ndarray | _Image.Image | str | Path | None
    ) -> FileData | None:
        if value is None:
            return None
        if isinstance(value, str) and value.lower().endswith(".svg"):
            return FileData(path=value, orig_name=Path(value).name)
        saved = image_utils.save_image(value, self.GRADIO_CACHE)
        orig_name = Path(saved).name if Path(saved).exists() else None
        return FileData(path=saved, orig_name=orig_name)

    def check_streamable(self):
        if self.streaming and self.sources != ["webcam"]:
            raise ValueError(
                "ImageFeed streaming only available if sources is ['webcam']. Streaming not supported with multiple sources."
            )

    def example_inputs(self) -> Any:
        return "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png"