"""gr.Label() component."""

from __future__ import annotations

import json
import operator
from collections.abc import Callable, Sequence
from pathlib import Path
from typing import TYPE_CHECKING, Any, Optional, Union

from gradio_client.documentation import document

from gradio.components.base import Component
from gradio.data_classes import GradioModel
from gradio.events import Events
from gradio.i18n import I18nData

if TYPE_CHECKING:
    from gradio.components import Timer


class LabelConfidence(GradioModel):
    label: Optional[Union[str, int, float]] = None
    confidence: Optional[float] = None


class LabelData(GradioModel):
    label: Optional[Union[str, int, float]] = None
    confidences: Optional[list[LabelConfidence]] = None


@document()
class Label(Component):
    """
    Displays a classification label, along with confidence scores of top categories, if provided. As this component does not
    accept user input, it is rarely used as an input component.

    Guides: image-classification-in-pytorch, image-classification-in-tensorflow, image-classification-with-vision-transformers
    """

    CONFIDENCES_KEY = "confidences"
    data_model = LabelData
    EVENTS = [Events.change, Events.select]

    def __init__(
        self,
        value: dict[str, float] | str | float | Callable | None = None,
        *,
        num_top_classes: int | None = None,
        label: str | I18nData | None = None,
        every: Timer | float | None = None,
        inputs: Component | Sequence[Component] | set[Component] | 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,
        key: int | str | tuple[int | str, ...] | None = None,
        preserved_by_key: list[str] | str | None = "value",
        color: str | None = None,
        show_heading: bool = True,
    ):
        """
        Parameters:
            value: Default value to show in the component. If a str or number is provided, simply displays the string or number. If a {Dict[str, float]} of classes and confidences is provided, displays the top class on top and the `num_top_classes` below, along with their confidence bars. If a function is provided, the function will be called each time the app loads to set the initial value of this component.
            num_top_classes: number of most confident classes to show.
            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: Continously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer.
            inputs: Components that are used as inputs to calculate `value` if `value` is a function (has no effect otherwise). `value` is recalculated any time the inputs change.
            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 size compared to adjacent Components. For example if Components A and B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide as B. Should be an integer. scale applies in Rows, and to top-level Components in Blocks where fill_height=True.
            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.
            key: in a gr.render, Components with the same key across re-renders are treated as the same component, not a new component. Properties set in 'preserved_by_key' are not reset across a re-render.
            preserved_by_key: A list of parameters from this component's constructor. Inside a gr.render() function, if a component is re-rendered with the same key, these (and only these) parameters will be preserved in the UI (if they have been changed by the user or an event listener) instead of re-rendered based on the values provided during constructor.
            color: The background color of the label (either a valid css color name or hexadecimal string).
            show_heading: If False, the heading will not be displayed if a dictionary of labels and confidences is provided. The heading will still be visible if the value is a string or number.
        """
        self.num_top_classes = num_top_classes
        self.color = color
        self.show_heading = show_heading
        super().__init__(
            label=label,
            every=every,
            inputs=inputs,
            show_label=show_label,
            container=container,
            scale=scale,
            min_width=min_width,
            visible=visible,
            elem_id=elem_id,
            elem_classes=elem_classes,
            render=render,
            key=key,
            preserved_by_key=preserved_by_key,
            value=value,
        )
        self._value_description = "a dictionary mapping string categories to float values that represent confidence from 0 - 1."

    def preprocess(
        self, payload: LabelData | None
    ) -> dict[str, float] | str | int | float | None:
        """
        Parameters:
            payload: An instance of `LabelData` containing the label and confidences.
        Returns:
            Depending on the value, passes the label as a `str | int | float`, or the labels and confidences as a `dict[str, float]`.
        """
        if payload is None:
            return None
        if payload.confidences is None:
            return payload.label
        return {
            d["label"]: d["confidence"] for d in payload.model_dump()["confidences"]
        }

    def postprocess(
        self, value: dict[str | float, float] | str | int | float | None
    ) -> LabelData | dict | None:
        """
        Parameters:
            value: Expects a `dict[str, float]` of classes and confidences, or `str` with just the class or an `int | float` for regression outputs, or a `str` path to a .json file containing a json dictionary in one of the preceding formats.
        Returns:
            Returns a `LabelData` object with the label and confidences, or a `dict` of the same format, or a `str` or `int` or `float` if the input was a single label.
        """
        if value is None or value == {}:
            return {}
        if isinstance(value, str) and value.endswith(".json") and Path(value).exists():
            return LabelData(**json.loads(Path(value).read_text()))
        if isinstance(value, (str, float, int)):
            return LabelData(label=str(value))
        if isinstance(value, dict):
            if "confidences" in value and isinstance(value["confidences"], dict):
                value = value["confidences"]
                value = {c["label"]: c["confidence"] for c in value}
            sorted_pred = sorted(
                value.items(), key=operator.itemgetter(1), reverse=True
            )
            if self.num_top_classes is not None:
                sorted_pred = sorted_pred[: self.num_top_classes]
            return LabelData(
                label=sorted_pred[0][0],
                confidences=[
                    LabelConfidence(label=pred[0], confidence=pred[1])
                    for pred in sorted_pred
                ],
            )
        raise ValueError(
            "The `Label` output interface expects one of: a string label, or an int label, a "
            "float label, or a dictionary whose keys are labels and values are confidences. "
            f"Instead, got a {type(value)}"
        )

    def example_payload(self) -> Any:
        return {
            "label": "Cat",
            "confidences": [
                {"label": "cat", "confidence": 0.9},
                {"label": "dog", "confidence": 0.1},
            ],
        }

    def example_value(self) -> Any:
        return {"cat": 0.9, "dog": 0.1}