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"""gr.State() component."""
from __future__ import annotations
import math
from collections.abc import Callable
from copy import deepcopy
from typing import Any
from gradio_client.documentation import document
from gradio.components.base import Component
from gradio.events import Events
from gradio.events import Dependency
@document()
class State(Component):
EVENTS = [Events.change]
"""
Special hidden component that stores session state across runs of the demo by the
same user. Can attach .change listeners that trigger when the state changes.
Demos: interface_state, blocks_simple_squares, state_cleanup
Guides: real-time-speech-recognition
"""
allow_string_shortcut = False
def __init__(
self,
value: Any = None,
render: bool = True,
*,
time_to_live: int | float | None = None,
delete_callback: Callable[[Any], None] | None = None,
):
"""
Parameters:
value: the initial value (of arbitrary type) of the state. The provided argument is deepcopied. If a callable is provided, the function will be called whenever the app loads to set the initial value of the state.
render: has no effect, but is included for consistency with other components.
time_to_live: The number of seconds the state should be stored for after it is created or updated. If None, the state will be stored indefinitely. Gradio automatically deletes state variables after a user closes the browser tab or refreshes the page, so this is useful for clearing state for potentially long running sessions.
delete_callback: A function that is called when the state is deleted. The function should take the state value as an argument.
"""
self.time_to_live = self.time_to_live = (
math.inf if time_to_live is None else time_to_live
)
self.delete_callback = delete_callback or (lambda a: None) # noqa: ARG005
try:
self.value = deepcopy(value)
except TypeError as err:
raise TypeError(
f"The initial value of `gr.State` must be able to be deepcopied. The initial value of type {type(value)} cannot be deepcopied."
) from err
super().__init__(value=self.value, render=render)
@property
def stateful(self) -> bool:
return True
def preprocess(self, payload: Any) -> Any:
"""
Parameters:
payload: Value
Returns:
Passes a value of arbitrary type through.
"""
return payload
def postprocess(self, value: Any) -> Any:
"""
Parameters:
value: Expects a value of arbitrary type, as long as it can be deepcopied.
Returns:
Passes a value of arbitrary type through.
"""
return value
def api_info(self) -> dict[str, Any]:
return {"type": {}, "description": "any valid json"}
def example_payload(self) -> Any:
return None
def example_value(self) -> Any:
return None
@property
def skip_api(self):
return True
from typing import Callable, Literal, Sequence, Any, TYPE_CHECKING
from gradio.blocks import Block
if TYPE_CHECKING:
from gradio.components import Timer
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",
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 | None = None,
concurrency_limit: int | None | Literal["default"] = "default",
concurrency_id: str | None = None,
show_api: bool = True,
) -> 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, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. 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
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.
"""
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