"""gr.AnnotatedImage() component."""

from __future__ import annotations

from typing import Any, List

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 gradio import processing_utils, utils
from gradio.components.base import Component
from gradio.data_classes import FileData, GradioModel
from gradio.events import Events

set_documentation_group("component")

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


class Annotation(GradioModel):
    image: FileData
    label: str


class AnnotatedImageData(GradioModel):
    image: FileData
    annotations: List[Annotation]


@document()
class TestAnnImage(Component):
    """
    Displays a base image and colored subsections on top of that image. Subsections can take the from of rectangles (e.g. object detection) or masks (e.g. image segmentation).
    Preprocessing: this component does *not* accept input.
    Postprocessing: expects a {Tuple[numpy.ndarray | PIL.Image | str, List[Tuple[numpy.ndarray | Tuple[int, int, int, int], str]]]} consisting of a base image and a list of subsections, that are either (x1, y1, x2, y2) tuples identifying object boundaries, or 0-1 confidence masks of the same shape as the image. A label is provided for each subsection.

    Demos: image_segmentation
    """

    EVENTS = [Events.select]

    data_model = AnnotatedImageData

    def __init__(
        self,
        value: tuple[
            np.ndarray | _Image.Image | str,
            list[tuple[np.ndarray | tuple[int, int, int, int], str]],
        ]
        | None = None,
        *,
        show_legend: bool = True,
        height: int | str | None = None,
        width: int | str | None = None,
        color_map: dict[str, str] | None = None,
        label: str | None = None,
        every: float | None = None,
        show_label: bool | None = None,
        container: bool = True,
        scale: int | None = None,
        min_width: int = 160,
        visible: bool = True,
        elem_id: str | None = None,
        elem_classes: list[str] | str | None = None,
        render: bool = True,
    ):
        """
        Parameters:
            value: Tuple of base image and list of (subsection, label) pairs.
            show_legend: If True, will show a legend of the subsections.
            height: The height of the image, specified in pixels if a number is passed, or in CSS units if a string is passed.
            width: The width of the image, specified in pixels if a number is passed, or in CSS units if a string is passed.
            color_map: A dictionary mapping labels to colors. The colors must be specified as hex codes.
            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.
            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.
            visible: If False, component will be hidden.
            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.
        """
        self.show_legend = show_legend
        self.height = height
        self.width = width
        self.color_map = color_map
        super().__init__(
            label=label,
            every=every,
            show_label=show_label,
            container=container,
            scale=scale,
            min_width=min_width,
            visible=visible,
            elem_id=elem_id,
            elem_classes=elem_classes,
            render=render,
            value=value,
        )

    def postprocess(
        self,
        value: tuple[
            np.ndarray | _Image.Image | str,
            list[tuple[np.ndarray | tuple[int, int, int, int], str]],
        ]
        | None,
    ) -> AnnotatedImageData | None:
        """
        Parameters:
            value: Tuple of base image and list of subsections, with each subsection a two-part tuple where the first element is a 4 element bounding box or a 0-1 confidence mask, and the second element is the label.
        Returns:
            Tuple of base image file and list of subsections, with each subsection a two-part tuple where the first element image path of the mask, and the second element is the label.
        """
        if value is None:
            return None
        base_img = value[0]
        if isinstance(base_img, str):
            base_img_path = base_img
            base_img = np.array(_Image.open(base_img))
        elif isinstance(base_img, np.ndarray):
            base_file = processing_utils.save_img_array_to_cache(
                base_img, cache_dir=self.GRADIO_CACHE
            )
            base_img_path = str(utils.abspath(base_file))
        elif isinstance(base_img, _Image.Image):
            base_file = processing_utils.save_pil_to_cache(
                base_img, cache_dir=self.GRADIO_CACHE
            )
            base_img_path = str(utils.abspath(base_file))
            base_img = np.array(base_img)
        else:
            raise ValueError(
                "TestAnnImage only accepts filepaths, PIL images or numpy arrays for the base image."
            )

        sections = []
        color_map = self.color_map or {}

        def hex_to_rgb(value):
            value = value.lstrip("#")
            lv = len(value)
            return [int(value[i : i + lv // 3], 16) for i in range(0, lv, lv // 3)]

        for mask, label in value[1]:
            mask_array = np.zeros((base_img.shape[0], base_img.shape[1]))
            if isinstance(mask, np.ndarray):
                mask_array = mask
            else:
                x1, y1, x2, y2 = mask
                border_width = 3
                mask_array[y1:y2, x1:x2] = 0.5
                mask_array[y1:y2, x1 : x1 + border_width] = 1
                mask_array[y1:y2, x2 - border_width : x2] = 1
                mask_array[y1 : y1 + border_width, x1:x2] = 1
                mask_array[y2 - border_width : y2, x1:x2] = 1

            if label in color_map:
                rgb_color = hex_to_rgb(color_map[label])
            else:
                rgb_color = [255, 0, 0]
            colored_mask = np.zeros((base_img.shape[0], base_img.shape[1], 4))
            solid_mask = np.copy(mask_array)
            solid_mask[solid_mask > 0] = 1

            colored_mask[:, :, 0] = rgb_color[0] * solid_mask
            colored_mask[:, :, 1] = rgb_color[1] * solid_mask
            colored_mask[:, :, 2] = rgb_color[2] * solid_mask
            colored_mask[:, :, 3] = mask_array * 255

            colored_mask_img = _Image.fromarray((colored_mask).astype(np.uint8))

            mask_file = processing_utils.save_pil_to_cache(
                colored_mask_img, cache_dir=self.GRADIO_CACHE
            )
            mask_file_path = str(utils.abspath(mask_file))
            sections.append(
                Annotation(image=FileData(path=mask_file_path), label=label)
            )

        return AnnotatedImageData(
            image=FileData(path=base_img_path),
            annotations=sections,
        )

    def example_inputs(self) -> Any:
        return {}

    def preprocess(
        self, payload: AnnotatedImageData | None
    ) -> AnnotatedImageData | None:
        return payload
