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"""Contains all of the events that can be triggered in a gr.Blocks() app, with the exception
of the on-page-load event, which is defined in gr.Blocks().load()."""
from __future__ import annotations
import warnings
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Set
from gradio.blocks import Block
from gradio.utils import get_cancel_function
if TYPE_CHECKING: # Only import for type checking (is False at runtime).
from gradio.components import Component, StatusTracker
def set_cancel_events(
block: Block, event_name: str, cancels: None | Dict[str, Any] | List[Dict[str, Any]]
):
if cancels:
if not isinstance(cancels, list):
cancels = [cancels]
cancel_fn, fn_indices_to_cancel = get_cancel_function(cancels)
block.set_event_trigger(
event_name,
cancel_fn,
inputs=None,
outputs=None,
queue=False,
preprocess=False,
cancels=fn_indices_to_cancel,
)
class EventListener(Block):
pass
class Changeable(EventListener):
def change(
self,
fn: Callable | None,
inputs: Component | List[Component] | Set[Component] | None = None,
outputs: Component | List[Component] | None = None,
api_name: str | None = None,
status_tracker: StatusTracker | None = None,
scroll_to_output: bool = False,
show_progress: bool = True,
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: float | None = None,
_js: str | None = None,
):
"""
This event is triggered when the component's input value changes (e.g. when the user types in a textbox
or uploads an image). This method can be used when this component is in a Gradio Blocks.
Parameters:
fn: the function to wrap an interface around. 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 inputs. If the function returns no outputs, this should be an empty list.
api_name: Defining this parameter exposes the endpoint in the api docs
scroll_to_output: If True, will scroll to output component on completion
show_progress: If True, will show progress animation while pending
queue: If True, will place the request on the queue, if the queue exists
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 event 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.
every: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.
"""
# _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.
if status_tracker:
warnings.warn(
"The 'status_tracker' parameter has been deprecated and has no effect."
)
dep = self.set_event_trigger(
"change",
fn,
inputs,
outputs,
preprocess=preprocess,
postprocess=postprocess,
scroll_to_output=scroll_to_output,
show_progress=show_progress,
api_name=api_name,
js=_js,
queue=queue,
batch=batch,
max_batch_size=max_batch_size,
every=every,
)
set_cancel_events(self, "change", cancels)
return dep
class Clickable(EventListener):
def click(
self,
fn: Callable | None,
inputs: Component | List[Component] | Set[Component] | None = None,
outputs: Component | List[Component] | None = None,
api_name: str | None = None,
status_tracker: StatusTracker | None = None,
scroll_to_output: bool = False,
show_progress: bool = True,
queue=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: float | None = None,
_js: str | None = None,
):
"""
This event is triggered when the component (e.g. a button) is clicked.
This method can be used when this component is in a Gradio Blocks.
Parameters:
fn: the function to wrap an interface around. 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 inputs. If the function returns no outputs, this should be an empty list.
api_name: Defining this parameter exposes the endpoint in the api docs
scroll_to_output: If True, will scroll to output component on completion
show_progress: If True, will show progress animation while pending
queue: If True, will place the request on the queue, if the queue exists
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 event 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.
every: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.
"""
# _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.
if status_tracker:
warnings.warn(
"The 'status_tracker' parameter has been deprecated and has no effect."
)
dep = self.set_event_trigger(
"click",
fn,
inputs,
outputs,
preprocess=preprocess,
postprocess=postprocess,
scroll_to_output=scroll_to_output,
show_progress=show_progress,
api_name=api_name,
js=_js,
queue=queue,
batch=batch,
max_batch_size=max_batch_size,
every=every,
)
set_cancel_events(self, "click", cancels)
return dep
class Submittable(EventListener):
def submit(
self,
fn: Callable | None,
inputs: Component | List[Component] | Set[Component] | None = None,
outputs: Component | List[Component] | None = None,
api_name: str | None = None,
status_tracker: StatusTracker | None = None,
scroll_to_output: bool = False,
show_progress: bool = True,
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: float | None = None,
_js: str | None = None,
):
"""
This event is triggered when the user presses the Enter key while the component (e.g. a textbox) is focused.
This method can be used when this component is in a Gradio Blocks.
Parameters:
fn: the function to wrap an interface around. 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 inputs. If the function returns no outputs, this should be an empty list.
api_name: Defining this parameter exposes the endpoint in the api docs
scroll_to_output: If True, will scroll to output component on completion
show_progress: If True, will show progress animation while pending
queue: If True, will place the request on the queue, if the queue exists
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 event 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.
every: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.
"""
# _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.
if status_tracker:
warnings.warn(
"The 'status_tracker' parameter has been deprecated and has no effect."
)
dep = self.set_event_trigger(
"submit",
fn,
inputs,
outputs,
preprocess=preprocess,
postprocess=postprocess,
scroll_to_output=scroll_to_output,
show_progress=show_progress,
api_name=api_name,
js=_js,
queue=queue,
batch=batch,
max_batch_size=max_batch_size,
every=every,
)
set_cancel_events(self, "submit", cancels)
return dep
class Editable(EventListener):
def edit(
self,
fn: Callable | None,
inputs: Component | List[Component] | Set[Component] | None = None,
outputs: Component | List[Component] | None = None,
api_name: str | None = None,
status_tracker: StatusTracker | None = None,
scroll_to_output: bool = False,
show_progress: bool = True,
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: float | None = None,
_js: str | None = None,
):
"""
This event is triggered when the user edits the component (e.g. image) using the
built-in editor. This method can be used when this component is in a Gradio Blocks.
Parameters:
fn: the function to wrap an interface around. 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 inputs. If the function returns no outputs, this should be an empty list.
api_name: Defining this parameter exposes the endpoint in the api docs
scroll_to_output: If True, will scroll to output component on completion
show_progress: If True, will show progress animation while pending
queue: If True, will place the request on the queue, if the queue exists
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 event 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.
every: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.
"""
# _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.
if status_tracker:
warnings.warn(
"The 'status_tracker' parameter has been deprecated and has no effect."
)
dep = self.set_event_trigger(
"edit",
fn,
inputs,
outputs,
preprocess=preprocess,
postprocess=postprocess,
scroll_to_output=scroll_to_output,
show_progress=show_progress,
api_name=api_name,
js=_js,
queue=queue,
batch=batch,
max_batch_size=max_batch_size,
every=every,
)
set_cancel_events(self, "edit", cancels)
return dep
class Clearable(EventListener):
def clear(
self,
fn: Callable | None,
inputs: Component | List[Component] | Set[Component] | None = None,
outputs: Component | List[Component] | None = None,
api_name: str | None = None,
status_tracker: StatusTracker | None = None,
scroll_to_output: bool = False,
show_progress: bool = True,
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: float | None = None,
_js: str | None = None,
):
"""
This event is triggered when the user clears the component (e.g. image or audio)
using the X button for the component. This method can be used when this component is in a Gradio Blocks.
Parameters:
fn: the function to wrap an interface around. 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 inputs. If the function returns no outputs, this should be an empty list.
api_name: Defining this parameter exposes the endpoint in the api docs
scroll_to_output: If True, will scroll to output component on completion
show_progress: If True, will show progress animation while pending
queue: If True, will place the request on the queue, if the queue exists
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 event 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.
every: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.
"""
# _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.
if status_tracker:
warnings.warn(
"The 'status_tracker' parameter has been deprecated and has no effect."
)
dep = self.set_event_trigger(
"submit",
fn,
inputs,
outputs,
preprocess=preprocess,
postprocess=postprocess,
scroll_to_output=scroll_to_output,
show_progress=show_progress,
api_name=api_name,
js=_js,
queue=queue,
batch=batch,
max_batch_size=max_batch_size,
every=every,
)
set_cancel_events(self, "submit", cancels)
return dep
class Playable(EventListener):
def play(
self,
fn: Callable | None,
inputs: Component | List[Component] | Set[Component] | None = None,
outputs: Component | List[Component] | None = None,
api_name: str | None = None,
status_tracker: StatusTracker | None = None,
scroll_to_output: bool = False,
show_progress: bool = True,
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: float | None = None,
_js: str | None = None,
):
"""
This event is triggered when the user plays the component (e.g. audio or video).
This method can be used when this component is in a Gradio Blocks.
Parameters:
fn: the function to wrap an interface around. 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 inputs. If the function returns no outputs, this should be an empty list.
api_name: Defining this parameter exposes the endpoint in the api docs
scroll_to_output: If True, will scroll to output component on completion
show_progress: If True, will show progress animation while pending
queue: If True, will place the request on the queue, if the queue exists
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 event 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.
every: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.
"""
# _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.
if status_tracker:
warnings.warn(
"The 'status_tracker' parameter has been deprecated and has no effect."
)
dep = self.set_event_trigger(
"play",
fn,
inputs,
outputs,
preprocess=preprocess,
postprocess=postprocess,
scroll_to_output=scroll_to_output,
show_progress=show_progress,
api_name=api_name,
js=_js,
queue=queue,
batch=batch,
max_batch_size=max_batch_size,
every=every,
)
set_cancel_events(self, "play", cancels)
return dep
def pause(
self,
fn: Callable | None,
inputs: Component | List[Component] | Set[Component] | None = None,
outputs: Component | List[Component] | None = None,
api_name: str | None = None,
status_tracker: StatusTracker | None = None,
scroll_to_output: bool = False,
show_progress: bool = True,
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: float | None = None,
_js: str | None = None,
):
"""
This event is triggered when the user pauses the component (e.g. audio or video).
This method can be used when this component is in a Gradio Blocks.
Parameters:
fn: the function to wrap an interface around. 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 inputs. If the function returns no outputs, this should be an empty list.
api_name: Defining this parameter exposes the endpoint in the api docs
scroll_to_output: If True, will scroll to output component on completion
show_progress: If True, will show progress animation while pending
queue: If True, will place the request on the queue, if the queue exists
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 event 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.
every: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.
"""
# _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.
if status_tracker:
warnings.warn(
"The 'status_tracker' parameter has been deprecated and has no effect."
)
dep = self.set_event_trigger(
"pause",
fn,
inputs,
outputs,
preprocess=preprocess,
postprocess=postprocess,
scroll_to_output=scroll_to_output,
show_progress=show_progress,
api_name=api_name,
js=_js,
queue=queue,
batch=batch,
max_batch_size=max_batch_size,
every=every,
)
set_cancel_events(self, "pause", cancels)
return dep
def stop(
self,
fn: Callable | None,
inputs: Component | List[Component] | Set[Component] | None = None,
outputs: Component | List[Component] | None = None,
api_name: str | None = None,
status_tracker: StatusTracker | None = None,
scroll_to_output: bool = False,
show_progress: bool = True,
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: float | None = None,
_js: str | None = None,
):
"""
This event is triggered when the user stops the component (e.g. audio or video).
This method can be used when this component is in a Gradio Blocks.
Parameters:
fn: the function to wrap an interface around. 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 inputs. If the function returns no outputs, this should be an empty list.
api_name: Defining this parameter exposes the endpoint in the api docs
scroll_to_output: If True, will scroll to output component on completion
show_progress: If True, will show progress animation while pending
queue: If True, will place the request on the queue, if the queue exists
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 event 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.
every: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.
"""
# _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.
if status_tracker:
warnings.warn(
"The 'status_tracker' parameter has been deprecated and has no effect."
)
dep = self.set_event_trigger(
"stop",
fn,
inputs,
outputs,
preprocess=preprocess,
postprocess=postprocess,
scroll_to_output=scroll_to_output,
show_progress=show_progress,
api_name=api_name,
js=_js,
queue=queue,
batch=batch,
max_batch_size=max_batch_size,
every=every,
)
set_cancel_events(self, "stop", cancels)
return dep
class Streamable(EventListener):
def stream(
self,
fn: Callable | None,
inputs: Component | List[Component] | Set[Component] | None = None,
outputs: Component | List[Component] | None = None,
api_name: str | None = None,
status_tracker: StatusTracker | None = None,
scroll_to_output: bool = False,
show_progress: bool = False,
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: float | None = None,
_js: str | None = None,
):
"""
This event is triggered when the user streams the component (e.g. a live webcam
component). This method can be used when this component is in a Gradio Blocks.
Parameters:
fn: the function to wrap an interface around. 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 inputs. If the function returns no outputs, this should be an empty list.
api_name: Defining this parameter exposes the endpoint in the api docs
scroll_to_output: If True, will scroll to output component on completion
show_progress: If True, will show progress animation while pending
queue: If True, will place the request on the queue, if the queue exists
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 event 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.
every: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.
"""
# _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.
self.streaming = True
if status_tracker:
warnings.warn(
"The 'status_tracker' parameter has been deprecated and has no effect."
)
dep = self.set_event_trigger(
"stream",
fn,
inputs,
outputs,
preprocess=preprocess,
postprocess=postprocess,
scroll_to_output=scroll_to_output,
show_progress=show_progress,
api_name=api_name,
js=_js,
queue=queue,
batch=batch,
max_batch_size=max_batch_size,
every=every,
)
set_cancel_events(self, "stream", cancels)
return dep
class Blurrable(EventListener):
def blur(
self,
fn: Callable | None,
inputs: Component | List[Component] | Set[Component] | None = None,
outputs: Component | List[Component] | None = None,
api_name: str | None = None,
scroll_to_output: bool = False,
show_progress: bool = True,
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: float | None = None,
_js: str | None = None,
):
"""
This event is triggered when the component's is unfocused/blurred (e.g. when the user clicks outside of a textbox). This method can be used when this component is in a Gradio Blocks.
Parameters:
fn: Callable function
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 inputs. If the function returns no outputs, this should be an empty list.
api_name: Defining this parameter exposes the endpoint in the api docs
scroll_to_output: If True, will scroll to output component on completion
show_progress: If True, will show progress animation while pending
queue: If True, will place the request on the queue, if the queue exists
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 event 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.
every: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.
"""
# _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.
self.set_event_trigger(
"blur",
fn,
inputs,
outputs,
preprocess=preprocess,
postprocess=postprocess,
scroll_to_output=scroll_to_output,
show_progress=show_progress,
api_name=api_name,
js=_js,
queue=queue,
batch=batch,
max_batch_size=max_batch_size,
every=every,
)
set_cancel_events(self, "blur", cancels)
class Uploadable(EventListener):
def upload(
self,
fn: Callable | None,
inputs: List[Component],
outputs: Component | List[Component] | None = None,
api_name: str | None = None,
scroll_to_output: bool = False,
show_progress: bool = True,
queue: bool | None = None,
batch: bool = False,
max_batch_size: int = 4,
preprocess: bool = True,
postprocess: bool = True,
cancels: List[Dict[str, Any]] | None = None,
every: float | None = None,
_js: str | None = None,
):
"""
This event is triggered when the user uploads a file into the component (e.g. when the user uploads a video into a video component). This method can be used when this component is in a Gradio Blocks.
Parameters:
fn: Callable function
inputs: List of inputs
outputs: List of outputs
api_name: Defining this parameter exposes the endpoint in the api docs
scroll_to_output: If True, will scroll to output component on completion
show_progress: If True, will show progress animation while pending
queue: If True, will place the request on the queue, if the queue exists
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 event 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.
every: Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.
"""
# _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.
self.set_event_trigger(
"upload",
fn,
inputs,
outputs,
preprocess=preprocess,
postprocess=postprocess,
scroll_to_output=scroll_to_output,
show_progress=show_progress,
api_name=api_name,
js=_js,
queue=queue,
batch=batch,
max_batch_size=max_batch_size,
every=every,
)
set_cancel_events(self, "upload", cancels)
|