"""gr.Label() component."""
from __future__ import annotations
import json
import operator
from pathlib import Path
from typing import Any, Callable, List, 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
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.
Demos: main_note, titanic_survival
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 | 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,
color: str | None = None,
):
"""
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 callable, the function will be called whenever the app loads to set the initial value of the 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: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. 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 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.
color: The background color of the label (either a valid css color name or hexadecimal string).
"""
self.num_top_classes = num_top_classes
self.color = color
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 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] | 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}