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r"""gr.ImageEditor() component."""
from __future__ import annotations
import dataclasses
import warnings
from collections.abc import Iterable, Sequence
from io import BytesIO
from pathlib import Path
from typing import (
TYPE_CHECKING,
Any,
Literal,
Optional,
Union,
cast,
)
import numpy as np
import PIL.Image
from gradio_client import handle_file
from gradio_client.documentation import document
from typing_extensions import TypedDict
from gradio import image_utils, utils
from gradio.components.base import Component, server
from gradio.data_classes import FileData, GradioModel
from gradio.events import Events
from gradio.i18n import I18nData
if TYPE_CHECKING:
from gradio.components import Timer
ImageType = Union[np.ndarray, PIL.Image.Image, str]
class EditorValue(TypedDict):
background: Optional[ImageType]
layers: list[ImageType]
composite: Optional[ImageType]
class EditorExampleValue(TypedDict):
background: Optional[str]
layers: Optional[list[Union[str, None]]]
composite: Optional[str]
class EditorData(GradioModel):
background: Optional[FileData] = None
layers: list[FileData] = []
composite: Optional[FileData] = None
id: Optional[str] = None
class EditorDataBlobs(GradioModel):
background: Optional[bytes]
layers: list[Union[bytes, None]]
composite: Optional[bytes]
class BlobData(TypedDict):
type: str
index: Optional[int]
file: bytes
id: str
class AcceptBlobs(GradioModel):
data: BlobData
files: list[tuple[str, bytes]]
@document()
@dataclasses.dataclass
class Eraser:
"""
A dataclass for specifying options for the eraser tool in the ImageEditor component. An instance of this class can be passed to the `eraser` parameter of `gr.ImageEditor`.
Parameters:
default_size: The default radius, in pixels, of the eraser tool. Defaults to "auto" in which case the radius is automatically determined based on the size of the image (generally 1/50th of smaller dimension).
"""
default_size: int | Literal["auto"] = "auto"
@document()
@dataclasses.dataclass
class Brush(Eraser):
"""
A dataclass for specifying options for the brush tool in the ImageEditor component. An instance of this class can be passed to the `brush` parameter of `gr.ImageEditor`.
Parameters:
default_size: The default radius, in pixels, of the brush tool. Defaults to "auto" in which case the radius is automatically determined based on the size of the image (generally 1/50th of smaller dimension).
colors: A list of colors to make available to the user when using the brush. Defaults to a list of 5 colors.
default_color: The default color of the brush. Defaults to the first color in the `colors` list.
color_mode: If set to "fixed", user can only select from among the colors in `colors`. If "defaults", the colors in `colors` are provided as a default palette, but the user can also select any color using a color picker.
"""
colors: list[str | tuple[str, float]] | str | tuple[str, float] | None = None
default_color: str | tuple[str, float] | None = None
color_mode: Literal["fixed", "defaults"] = "defaults"
def __post_init__(self):
if self.colors is None:
self.colors = [
"rgb(204, 50, 50)",
"rgb(173, 204, 50)",
"rgb(50, 204, 112)",
"rgb(50, 112, 204)",
"rgb(173, 50, 204)",
]
if self.default_color is None:
self.default_color = (
self.colors[0] if isinstance(self.colors, list) else self.colors
)
@document()
@dataclasses.dataclass
class LayerOptions:
"""
A dataclass for specifying options for the layer tool in the ImageEditor component. An instance of this class can be passed to the `layers` parameter of `gr.ImageEditor`.
Parameters:
allow_additional_layers: If True, users can add additional layers to the image. If False, the add layer button will not be shown.
layers: A list of layers to make available to the user when using the layer tool. One layer must be provided, if the length of the list is 0 then a layer will be generated automatically.
"""
allow_additional_layers: bool = True
layers: list[str] | None = None
disabled: bool = False
def __post_init__(self):
if self.layers is None or len(self.layers) == 0:
self.layers = ["Layer 1"]
@document()
@dataclasses.dataclass
class WebcamOptions:
"""
A dataclass for specifying options for the webcam tool in the ImageEditor component. An instance of this class can be passed to the `webcam_options` parameter of `gr.ImageEditor`.
Parameters:
mirror: If True, the webcam will be mirrored.
constraints: A dictionary of constraints for the webcam.
"""
mirror: bool = True
constraints: dict[str, Any] | None = None
from gradio.events import Dependency
@document()
class ImageEditor(Component):
"""
Creates an image component that, as an input, can be used to upload and edit images using simple editing tools such
as brushes, strokes, cropping, and layers. Or, as an output, this component can be used to display images.
Demos: image_editor
"""
EVENTS = [
Events.clear,
Events.change,
Events.input,
Events.select,
Events.upload,
Events.apply,
]
data_model = EditorData
def __init__(
self,
value: EditorValue | ImageType | 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"
] = "RGBA",
sources: (
Iterable[Literal["upload", "webcam", "clipboard"]]
| Literal["upload", "webcam", "clipboard"]
| None
) = (
"upload",
"webcam",
"clipboard",
),
type: Literal["numpy", "pil", "filepath"] = "numpy",
label: str | I18nData | None = None,
every: Timer | float | None = None,
inputs: Component | Sequence[Component] | set[Component] | 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,
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",
placeholder: str | None = None,
mirror_webcam: bool | None = None,
show_share_button: bool | None = None,
_selectable: bool = False,
crop_size: tuple[int | float, int | float] | str | None = None,
transforms: Iterable[Literal["crop", "resize"]] | None = ("crop", "resize"),
eraser: Eraser | None | Literal[False] = None,
brush: Brush | None | Literal[False] = None,
format: str = "webp",
layers: bool | LayerOptions = True,
canvas_size: tuple[int, int] = (800, 800),
fixed_canvas: bool = False,
show_fullscreen_button: bool = True,
webcam_options: WebcamOptions | None = None,
):
"""
Parameters:
value: Optional initial image(s) to populate the image editor. Should be a dictionary with keys: `background`, `layers`, and `composite`. The values corresponding to `background` and `composite` should be images or None, while `layers` should be a list of images. Images can be of type PIL.Image, np.array, or str filepath/URL. Or, the value can be a callable, in which case the function will be called whenever the app loads to set the initial value of the component.
height: The height of the component, specified in pixels if a number is passed, or in CSS units if a string is passed. This has no effect on the preprocessed image files or numpy arrays, but will affect the displayed images. Beware of conflicting values with the canvas_size paramter. If the canvas_size is larger than the height, the editing canvas will not fit in the component.
width: The width of the component, specified in pixels if a number is passed, or in CSS units if a string is passed. This has no effect on the preprocessed image files or numpy arrays, but will affect the displayed images. Beware of conflicting values with the canvas_size paramter. If the canvas_size is larger than the height, the editing canvas will not fit in the component.
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 that can be used to set the background 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.
type: The format the images are converted to before being passed into the prediction function. "numpy" converts the images to numpy arrays with shape (height, width, 3) and values from 0 to 255, "pil" converts the images to PIL image objects, "filepath" passes images as str filepaths to temporary copies of the images.
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.
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 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.
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.
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.
placeholder: Custom text for the upload area. Overrides default upload messages when provided. Accepts new lines and `#` to designate a heading.
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.
crop_size: Deprecated. Used to set the `canvas_size` parameter.
transforms: The transforms tools to make available to users. "crop" allows the user to crop the image.
eraser: The options for the eraser tool in the image editor. Should be an instance of the `gr.Eraser` class, or None to use the default settings. Can also be False to hide the eraser tool. [See `gr.Eraser` docs](#eraser).
brush: The options for the brush tool in the image editor. Should be an instance of the `gr.Brush` class, or None to use the default settings. Can also be False to hide the brush tool, which will also hide the eraser tool. [See `gr.Brush` docs](#brush).
format: Format to save image if it does not already have a valid format (e.g. if the image is being returned to the frontend as a numpy array or PIL Image). The format should be supported by the PIL library. This parameter has no effect on SVG files.
layers: The options for the layer tool in the image editor. Can be a boolean or an instance of the `gr.LayerOptions` class. If True, will allow users to add layers to the image. If False, the layers option will be hidden. If an instance of `gr.LayerOptions`, it will be used to configure the layer tool. [See `gr.LayerOptions` docs](#layer-options).
canvas_size: The initial size of the canvas in pixels. The first value is the width and the second value is the height. If `fixed_canvas` is `True`, uploaded images will be rescaled to fit the canvas size while preserving the aspect ratio. Otherwise, the canvas size will change to match the size of an uploaded image.
fixed_canvas: If True, the canvas size will not change based on the size of the background image and the image will be rescaled to fit (while preserving the aspect ratio) and placed in the center of the canvas.
show_fullscreen_button: If True, will display button to view image in fullscreen mode.
webcam_options: The options for the webcam tool in the image editor. Can be an instance of the `gr.WebcamOptions` class, or None to use the default settings. [See `gr.WebcamOptions` docs](#webcam-options).
"""
self._selectable = _selectable
self.webcam_options = (
webcam_options if webcam_options is not None else WebcamOptions()
)
if mirror_webcam is not None:
warnings.warn(
"The `mirror_webcam` parameter is deprecated. Please use the `webcam_options` parameter with a `gr.WebcamOptions` instance instead."
)
self.webcam_options.mirror = 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 isinstance(sources, str):
sources = [sources]
if sources is not None:
for source in sources:
if source not in valid_sources:
raise ValueError(
f"`sources` must be a list consisting of elements in {valid_sources}"
)
self.sources = sources
else:
self.sources = []
self.show_download_button = show_download_button
self.show_share_button = (
(utils.get_space() is not None)
if show_share_button is None
else show_share_button
)
if crop_size is not None and canvas_size is None:
warnings.warn(
"`crop_size` parameter is deprecated. Please use `canvas_size` instead."
)
if isinstance(crop_size, str):
# convert ratio to tuple
proportion = [
int(crop_size.split(":")[0]),
int(crop_size.split(":")[1]),
]
ratio = proportion[0] / proportion[1]
canvas_size = (
(int(800 * ratio), 800) if ratio > 1 else (800, int(800 / ratio))
)
else:
canvas_size = (int(crop_size[0]), int(crop_size[1]))
self.transforms = transforms
self.eraser = Eraser() if eraser is None else eraser
self.brush = Brush() if brush is None else brush
self.blob_storage: dict[str, EditorDataBlobs] = {}
self.format = format
self.layers = (
LayerOptions()
if layers is True
else LayerOptions(disabled=True)
if layers is False
else layers
)
self.canvas_size = canvas_size
self.fixed_canvas = fixed_canvas
self.show_fullscreen_button = show_fullscreen_button
self.placeholder = placeholder
super().__init__(
label=label,
every=every,
inputs=inputs,
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,
key=key,
preserved_by_key=preserved_by_key,
value=value,
)
self._value_description = f"a dictionary with structure {{'background': image, 'layers': list of images, 'composite': image}} where each image is {'a filepath' if self.type == 'filepath' else 'a numpy array' if self.type == 'numpy' else 'a PIL Image object'}."
def convert_and_format_image(
self,
file: FileData | None | bytes,
) -> np.ndarray | PIL.Image.Image | str | None:
if file is None:
return None
im = (
PIL.Image.open(file.path)
if isinstance(file, FileData)
else PIL.Image.open(BytesIO(file))
)
if isinstance(file, (bytes, bytearray, memoryview)):
name = "image"
suffix = self.format
elif file.orig_name:
p = Path(file.orig_name)
name = p.stem
suffix = p.suffix.replace(".", "")
if suffix in ["jpg", "jpeg"]:
suffix = "jpeg"
else:
name = "image"
suffix = self.format
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,
format=suffix,
name=name,
)
def preprocess(self, payload: EditorData | None) -> EditorValue | None:
"""
Parameters:
payload: An instance of `EditorData` consisting of the background image, layers, and composite image.
Returns:
Passes the uploaded images as an instance of EditorValue, which is just a `dict` with keys: 'background', 'layers', and 'composite'. The values corresponding to 'background' and 'composite' are images, while 'layers' is a `list` of images. The images are of type `PIL.Image`, `np.array`, or `str` filepath, depending on the `type` parameter.
"""
if payload is None:
return payload
if payload.id is not None:
cached = self.blob_storage.get(payload.id)
_payload = (
EditorDataBlobs(
background=cached.background,
layers=cached.layers,
composite=cached.composite,
)
if cached
else None
)
else:
_payload = payload
bg = None
layers = None
composite = None
if _payload is not None:
bg = self.convert_and_format_image(_payload.background)
layers = (
[self.convert_and_format_image(layer) for layer in _payload.layers]
if _payload.layers
else None
)
composite = self.convert_and_format_image(_payload.composite)
if payload.id is not None and payload.id in self.blob_storage:
self.blob_storage.pop(payload.id)
return {
"background": bg,
"layers": [x for x in layers if x is not None] if layers else [],
"composite": composite,
}
def postprocess(self, value: EditorValue | ImageType | None) -> EditorData | None:
"""
Parameters:
value: Expects a EditorValue, which is just a dictionary with keys: 'background', 'layers', and 'composite'. The values corresponding to 'background' and 'composite' should be images or None, while `layers` should be a list of images. Images can be of type `PIL.Image`, `np.array`, or `str` filepath/URL. Or, the value can be simply a single image (`ImageType`), in which case it will be used as the background.
Returns:
An instance of `EditorData` consisting of the background image, layers, and composite image.
"""
if value is None:
return None
elif isinstance(value, dict):
pass
elif isinstance(value, (np.ndarray, PIL.Image.Image, str)):
value = {"background": value, "layers": [], "composite": value}
else:
raise ValueError(
"The value to `gr.ImageEditor` must be a dictionary of images or a single image."
)
layers = (
[
FileData(
path=image_utils.save_image(
cast(Union[np.ndarray, PIL.Image.Image, str], layer),
self.GRADIO_CACHE,
format=self.format,
)
)
for layer in value["layers"]
]
if value["layers"]
else []
)
return EditorData(
background=(
FileData(
path=image_utils.save_image(
value["background"], self.GRADIO_CACHE, format=self.format
)
)
if value["background"] is not None
else None
),
layers=layers,
composite=(
FileData(
path=image_utils.save_image(
cast(
Union[np.ndarray, PIL.Image.Image, str], value["composite"]
),
self.GRADIO_CACHE,
format=self.format,
)
)
if value["composite"] is not None
else None
),
)
def example_payload(self) -> Any:
return {
"background": handle_file(
"https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png"
),
"layers": [],
"composite": None,
}
def example_value(self) -> Any:
return {
"background": "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png",
"layers": [],
"composite": None,
}
@server
def accept_blobs(self, data: AcceptBlobs):
"""
Accepts a dictionary of image blobs, where the keys are 'background', 'layers', and 'composite', and the values are binary file-like objects.
"""
type = data.data["type"]
index = (
int(data.data["index"])
if data.data["index"] and data.data["index"] != "null"
else None
)
file = data.files[0][1]
id = data.data["id"]
current = self.blob_storage.get(
id, EditorDataBlobs(background=None, layers=[], composite=None)
)
if type == "layer" and index is not None:
if index >= len(current.layers):
current.layers.extend([None] * (index + 1 - len(current.layers)))
current.layers[index] = file
elif type == "background":
current.background = file
elif type == "composite":
current.composite = file
self.blob_storage[id] = current
from typing import Callable, Literal, Sequence, Any, TYPE_CHECKING
from gradio.blocks import Block
if TYPE_CHECKING:
from gradio.components import Timer
from gradio.components.base import Component
def clear(self,
fn: Callable[..., Any] | None = None,
inputs: Block | Sequence[Block] | set[Block] | None = None,
outputs: Block | Sequence[Block] | None = None,
api_name: str | None | Literal[False] = None,
scroll_to_output: bool = False,
show_progress: Literal["full", "minimal", "hidden"] = "full",
show_progress_on: Component | Sequence[Component] | None = None,
queue: bool | None = None,
batch: bool = False,
max_batch_size: int = 4,
preprocess: bool = True,
postprocess: bool = True,
cancels: dict[str, Any] | list[dict[str, Any]] | None = None,
every: Timer | float | None = None,
trigger_mode: Literal["once", "multiple", "always_last"] | None = None,
js: str | Literal[True] | None = None,
concurrency_limit: int | None | Literal["default"] = "default",
concurrency_id: str | None = None,
show_api: bool = True,
key: int | str | tuple[int | str, ...] | None = None,
) -> Dependency:
"""
Parameters:
fn: the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
inputs: list of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
outputs: list of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
api_name: defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, will use the functions name as the endpoint route. If set to a string, the endpoint will be exposed in the api docs with the given name.
scroll_to_output: if True, will scroll to output component on completion
show_progress: how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all
show_progress_on: Component or list of components to show the progress animation on. If None, will show the progress animation on all of the output components.
queue: if True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
batch: if True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
max_batch_size: maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
preprocess: if False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
postprocess: if False, will not run postprocessing of component data before returning 'fn' output to the browser.
cancels: a list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
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.
trigger_mode: if "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete.
js: optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components.
concurrency_limit: if set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default).
concurrency_id: if set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit.
show_api: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False.
key: A unique key for this event listener to be used in @gr.render(). If set, this value identifies an event as identical across re-renders when the key is identical.
"""
...
def change(self,
fn: Callable[..., Any] | None = None,
inputs: Block | Sequence[Block] | set[Block] | None = None,
outputs: Block | Sequence[Block] | None = None,
api_name: str | None | Literal[False] = None,
scroll_to_output: bool = False,
show_progress: Literal["full", "minimal", "hidden"] = "full",
show_progress_on: Component | Sequence[Component] | None = None,
queue: bool | None = None,
batch: bool = False,
max_batch_size: int = 4,
preprocess: bool = True,
postprocess: bool = True,
cancels: dict[str, Any] | list[dict[str, Any]] | None = None,
every: Timer | float | None = None,
trigger_mode: Literal["once", "multiple", "always_last"] | None = None,
js: str | Literal[True] | None = None,
concurrency_limit: int | None | Literal["default"] = "default",
concurrency_id: str | None = None,
show_api: bool = True,
key: int | str | tuple[int | str, ...] | None = None,
) -> Dependency:
"""
Parameters:
fn: the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
inputs: list of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
outputs: list of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
api_name: defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, will use the functions name as the endpoint route. If set to a string, the endpoint will be exposed in the api docs with the given name.
scroll_to_output: if True, will scroll to output component on completion
show_progress: how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all
show_progress_on: Component or list of components to show the progress animation on. If None, will show the progress animation on all of the output components.
queue: if True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
batch: if True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
max_batch_size: maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
preprocess: if False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
postprocess: if False, will not run postprocessing of component data before returning 'fn' output to the browser.
cancels: a list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
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.
trigger_mode: if "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete.
js: optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components.
concurrency_limit: if set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default).
concurrency_id: if set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit.
show_api: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False.
key: A unique key for this event listener to be used in @gr.render(). If set, this value identifies an event as identical across re-renders when the key is identical.
"""
...
def input(self,
fn: Callable[..., Any] | None = None,
inputs: Block | Sequence[Block] | set[Block] | None = None,
outputs: Block | Sequence[Block] | None = None,
api_name: str | None | Literal[False] = None,
scroll_to_output: bool = False,
show_progress: Literal["full", "minimal", "hidden"] = "full",
show_progress_on: Component | Sequence[Component] | None = None,
queue: bool | None = None,
batch: bool = False,
max_batch_size: int = 4,
preprocess: bool = True,
postprocess: bool = True,
cancels: dict[str, Any] | list[dict[str, Any]] | None = None,
every: Timer | float | None = None,
trigger_mode: Literal["once", "multiple", "always_last"] | None = None,
js: str | Literal[True] | None = None,
concurrency_limit: int | None | Literal["default"] = "default",
concurrency_id: str | None = None,
show_api: bool = True,
key: int | str | tuple[int | str, ...] | None = None,
) -> Dependency:
"""
Parameters:
fn: the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
inputs: list of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
outputs: list of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
api_name: defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, will use the functions name as the endpoint route. If set to a string, the endpoint will be exposed in the api docs with the given name.
scroll_to_output: if True, will scroll to output component on completion
show_progress: how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all
show_progress_on: Component or list of components to show the progress animation on. If None, will show the progress animation on all of the output components.
queue: if True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
batch: if True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
max_batch_size: maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
preprocess: if False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
postprocess: if False, will not run postprocessing of component data before returning 'fn' output to the browser.
cancels: a list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
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.
trigger_mode: if "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete.
js: optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components.
concurrency_limit: if set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default).
concurrency_id: if set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit.
show_api: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False.
key: A unique key for this event listener to be used in @gr.render(). If set, this value identifies an event as identical across re-renders when the key is identical.
"""
...
def select(self,
fn: Callable[..., Any] | None = None,
inputs: Block | Sequence[Block] | set[Block] | None = None,
outputs: Block | Sequence[Block] | None = None,
api_name: str | None | Literal[False] = None,
scroll_to_output: bool = False,
show_progress: Literal["full", "minimal", "hidden"] = "full",
show_progress_on: Component | Sequence[Component] | None = None,
queue: bool | None = None,
batch: bool = False,
max_batch_size: int = 4,
preprocess: bool = True,
postprocess: bool = True,
cancels: dict[str, Any] | list[dict[str, Any]] | None = None,
every: Timer | float | None = None,
trigger_mode: Literal["once", "multiple", "always_last"] | None = None,
js: str | Literal[True] | None = None,
concurrency_limit: int | None | Literal["default"] = "default",
concurrency_id: str | None = None,
show_api: bool = True,
key: int | str | tuple[int | str, ...] | None = None,
) -> Dependency:
"""
Parameters:
fn: the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
inputs: list of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
outputs: list of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
api_name: defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, will use the functions name as the endpoint route. If set to a string, the endpoint will be exposed in the api docs with the given name.
scroll_to_output: if True, will scroll to output component on completion
show_progress: how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all
show_progress_on: Component or list of components to show the progress animation on. If None, will show the progress animation on all of the output components.
queue: if True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
batch: if True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
max_batch_size: maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
preprocess: if False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
postprocess: if False, will not run postprocessing of component data before returning 'fn' output to the browser.
cancels: a list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
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.
trigger_mode: if "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete.
js: optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components.
concurrency_limit: if set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default).
concurrency_id: if set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit.
show_api: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False.
key: A unique key for this event listener to be used in @gr.render(). If set, this value identifies an event as identical across re-renders when the key is identical.
"""
...
def upload(self,
fn: Callable[..., Any] | None = None,
inputs: Block | Sequence[Block] | set[Block] | None = None,
outputs: Block | Sequence[Block] | None = None,
api_name: str | None | Literal[False] = None,
scroll_to_output: bool = False,
show_progress: Literal["full", "minimal", "hidden"] = "full",
show_progress_on: Component | Sequence[Component] | None = None,
queue: bool | None = None,
batch: bool = False,
max_batch_size: int = 4,
preprocess: bool = True,
postprocess: bool = True,
cancels: dict[str, Any] | list[dict[str, Any]] | None = None,
every: Timer | float | None = None,
trigger_mode: Literal["once", "multiple", "always_last"] | None = None,
js: str | Literal[True] | None = None,
concurrency_limit: int | None | Literal["default"] = "default",
concurrency_id: str | None = None,
show_api: bool = True,
key: int | str | tuple[int | str, ...] | None = None,
) -> Dependency:
"""
Parameters:
fn: the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
inputs: list of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
outputs: list of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
api_name: defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, will use the functions name as the endpoint route. If set to a string, the endpoint will be exposed in the api docs with the given name.
scroll_to_output: if True, will scroll to output component on completion
show_progress: how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all
show_progress_on: Component or list of components to show the progress animation on. If None, will show the progress animation on all of the output components.
queue: if True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
batch: if True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
max_batch_size: maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
preprocess: if False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
postprocess: if False, will not run postprocessing of component data before returning 'fn' output to the browser.
cancels: a list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
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.
trigger_mode: if "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete.
js: optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components.
concurrency_limit: if set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default).
concurrency_id: if set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit.
show_api: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False.
key: A unique key for this event listener to be used in @gr.render(). If set, this value identifies an event as identical across re-renders when the key is identical.
"""
...
def apply(self,
fn: Callable[..., Any] | None = None,
inputs: Block | Sequence[Block] | set[Block] | None = None,
outputs: Block | Sequence[Block] | None = None,
api_name: str | None | Literal[False] = None,
scroll_to_output: bool = False,
show_progress: Literal["full", "minimal", "hidden"] = "full",
show_progress_on: Component | Sequence[Component] | None = None,
queue: bool | None = None,
batch: bool = False,
max_batch_size: int = 4,
preprocess: bool = True,
postprocess: bool = True,
cancels: dict[str, Any] | list[dict[str, Any]] | None = None,
every: Timer | float | None = None,
trigger_mode: Literal["once", "multiple", "always_last"] | None = None,
js: str | Literal[True] | None = None,
concurrency_limit: int | None | Literal["default"] = "default",
concurrency_id: str | None = None,
show_api: bool = True,
key: int | str | tuple[int | str, ...] | None = None,
) -> Dependency:
"""
Parameters:
fn: the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
inputs: list of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
outputs: list of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
api_name: defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, will use the functions name as the endpoint route. If set to a string, the endpoint will be exposed in the api docs with the given name.
scroll_to_output: if True, will scroll to output component on completion
show_progress: how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all
show_progress_on: Component or list of components to show the progress animation on. If None, will show the progress animation on all of the output components.
queue: if True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
batch: if True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
max_batch_size: maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
preprocess: if False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
postprocess: if False, will not run postprocessing of component data before returning 'fn' output to the browser.
cancels: a list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
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.
trigger_mode: if "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete.
js: optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components.
concurrency_limit: if set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default).
concurrency_id: if set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit.
show_api: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False.
key: A unique key for this event listener to be used in @gr.render(). If set, this value identifies an event as identical across re-renders when the key is identical.
"""
... |