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	from __future__ import annotations

	import copy
	import inspect
	import json
	import os
	import random
	import secrets
	import sys
	import time
	import warnings
	import webbrowser
	from abc import abstractmethod
	from types import ModuleType
	from typing import TYPE_CHECKING, Any, Callable, Dict, Iterator, List, Set, Tuple, Type

	import anyio
	import requests
	from anyio import CapacityLimiter
	from typing_extensions import Literal

	from gradio import components, external, networking, queueing, routes, strings, utils
	from gradio.context import Context
	from gradio.deprecation import check_deprecated_parameters
	from gradio.documentation import document, set_documentation_group
	from gradio.exceptions import DuplicateBlockError, InvalidApiName
	from gradio.helpers import EventData, create_tracker, skip, special_args
	from gradio.themes import Default as DefaultTheme
	from gradio.themes import ThemeClass as Theme
	from gradio.tunneling import CURRENT_TUNNELS
	from gradio.utils import (
	GRADIO_VERSION,
	TupleNoPrint,
	check_function_inputs_match,
	component_or_layout_class,
	delete_none,
	get_cancel_function,
	get_continuous_fn,
	)

	set_documentation_group("blocks")

	if TYPE_CHECKING: # Only import for type checking (is False at runtime).
	import comet_ml
	from fastapi.applications import FastAPI

	from gradio.components import Component


	class Block:
	def __init__(
	self,
	*,
	render: bool = True,
	elem_id: str \| None = None,
	elem_classes: List[str] \| str \| None = None,
	visible: bool = True,
	root_url: str \| None = None, # URL that is prepended to all file paths
	_skip_init_processing: bool = False, # Used for loading from Spaces
	**kwargs,
	):
	self._id = Context.id
	Context.id += 1
	self.visible = visible
	self.elem_id = elem_id
	self.elem_classes = (
	[elem_classes] if isinstance(elem_classes, str) else elem_classes
	)
	self.root_url = root_url
	self.share_token = secrets.token_urlsafe(32)
	self._skip_init_processing = _skip_init_processing
	self._style = {}
	self.parent: BlockContext \| None = None
	self.root = ""

	if render:
	self.render()
	check_deprecated_parameters(self.__class__.__name__, **kwargs)

	def render(self):
	"""
	Adds self into appropriate BlockContext
	"""
	if Context.root_block is not None and self._id in Context.root_block.blocks:
	raise DuplicateBlockError(
	f"A block with id: {self._id} has already been rendered in the current Blocks."
	)
	if Context.block is not None:
	Context.block.add(self)
	if Context.root_block is not None:
	Context.root_block.blocks[self._id] = self
	if isinstance(self, components.TempFileManager):
	Context.root_block.temp_file_sets.append(self.temp_files)
	return self

	def unrender(self):
	"""
	Removes self from BlockContext if it has been rendered (otherwise does nothing).
	Removes self from the layout and collection of blocks, but does not delete any event triggers.
	"""
	if Context.block is not None:
	try:
	Context.block.children.remove(self)
	except ValueError:
	pass
	if Context.root_block is not None:
	try:
	del Context.root_block.blocks[self._id]
	except KeyError:
	pass
	return self

	def get_block_name(self) -> str:
	"""
	Gets block's class name.

	If it is template component it gets the parent's class name.

	@return: class name
	"""
	return (
	self.__class__.__base__.__name__.lower()
	if hasattr(self, "is_template")
	else self.__class__.__name__.lower()
	)

	def get_expected_parent(self) -> Type[BlockContext] \| None:
	return None

	def set_event_trigger(
	self,
	event_name: str,
	fn: Callable \| None,
	inputs: Component \| List[Component] \| Set[Component] \| None,
	outputs: Component \| List[Component] \| None,
	preprocess: bool = True,
	postprocess: bool = True,
	scroll_to_output: bool = False,
	show_progress: bool = True,
	api_name: str \| None = None,
	js: str \| None = None,
	no_target: bool = False,
	queue: bool \| None = None,
	batch: bool = False,
	max_batch_size: int = 4,
	cancels: List[int] \| None = None,
	every: float \| None = None,
	collects_event_data: bool \| None = None,
	trigger_after: int \| None = None,
	trigger_only_on_success: bool = False,
	) -> Tuple[Dict[str, Any], int]:
	"""
	Adds an event to the component's dependencies.
	Parameters:
	event_name: event name
	fn: Callable function
	inputs: input list
	outputs: output list
	preprocess: whether to run the preprocess methods of components
	postprocess: whether to run the postprocess methods of components
	scroll_to_output: whether to scroll to output of dependency on trigger
	show_progress: whether to show progress animation while running.
	api_name: Defining this parameter exposes the endpoint in the api docs
	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
	no_target: if True, sets "targets" to [], used for Blocks "load" event
	batch: whether this function takes in a batch of inputs
	max_batch_size: the maximum batch size to send to the function
	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.
	collects_event_data: whether to collect event data for this event
	trigger_after: if set, this event will be triggered after 'trigger_after' function index
	trigger_only_on_success: if True, this event will only be triggered if the previous event was successful (only applies if `trigger_after` is set)
	Returns: dependency information, dependency index
	"""
	# Support for singular parameter
	if isinstance(inputs, set):
	inputs_as_dict = True
	inputs = sorted(inputs, key=lambda x: x._id)
	else:
	inputs_as_dict = False
	if inputs is None:
	inputs = []
	elif not isinstance(inputs, list):
	inputs = [inputs]

	if isinstance(outputs, set):
	outputs = sorted(outputs, key=lambda x: x._id)
	else:
	if outputs is None:
	outputs = []
	elif not isinstance(outputs, list):
	outputs = [outputs]

	if fn is not None and not cancels:
	check_function_inputs_match(fn, inputs, inputs_as_dict)

	if Context.root_block is None:
	raise AttributeError(
	f"{event_name}() and other events can only be called within a Blocks context."
	)
	if every is not None and every <= 0:
	raise ValueError("Parameter every must be positive or None")
	if every and batch:
	raise ValueError(
	f"Cannot run {event_name} event in a batch and every {every} seconds. "
	"Either batch is True or every is non-zero but not both."
	)

	if every and fn:
	fn = get_continuous_fn(fn, every)
	elif every:
	raise ValueError("Cannot set a value for `every` without a `fn`.")

	_, progress_index, event_data_index = (
	special_args(fn) if fn else (None, None, None)
	)
	Context.root_block.fns.append(
	BlockFunction(
	fn,
	inputs,
	outputs,
	preprocess,
	postprocess,
	inputs_as_dict,
	progress_index is not None,
	)
	)
	if api_name is not None:
	api_name_ = utils.append_unique_suffix(
	api_name, [dep["api_name"] for dep in Context.root_block.dependencies]
	)
	if not (api_name == api_name_):
	warnings.warn(
	"api_name {} already exists, using {}".format(api_name, api_name_)
	)
	api_name = api_name_

	if collects_event_data is None:
	collects_event_data = event_data_index is not None

	dependency = {
	"targets": [self._id] if not no_target else [],
	"trigger": event_name,
	"inputs": [block._id for block in inputs],
	"outputs": [block._id for block in outputs],
	"backend_fn": fn is not None,
	"js": js,
	"queue": False if fn is None else queue,
	"api_name": api_name,
	"scroll_to_output": scroll_to_output,
	"show_progress": show_progress,
	"every": every,
	"batch": batch,
	"max_batch_size": max_batch_size,
	"cancels": cancels or [],
	"types": {
	"continuous": bool(every),
	"generator": inspect.isgeneratorfunction(fn) or bool(every),
	},
	"collects_event_data": collects_event_data,
	"trigger_after": trigger_after,
	"trigger_only_on_success": trigger_only_on_success,
	}
	Context.root_block.dependencies.append(dependency)
	return dependency, len(Context.root_block.dependencies) - 1

	def get_config(self):
	return {
	"visible": self.visible,
	"elem_id": self.elem_id,
	"elem_classes": self.elem_classes,
	"style": self._style,
	"root_url": self.root_url,
	}

	@staticmethod
	@abstractmethod
	def update(**kwargs) -> Dict:
	return {}

	@classmethod
	def get_specific_update(cls, generic_update: Dict[str, Any]) -> Dict:
	generic_update = generic_update.copy()
	del generic_update["__type__"]
	specific_update = cls.update(**generic_update)
	return specific_update


	class BlockContext(Block):
	def __init__(
	self,
	visible: bool = True,
	render: bool = True,
	**kwargs,
	):
	"""
	Parameters:
	visible: If False, this will be hidden but included in the Blocks config file (its visibility can later be updated).
	render: If False, this will not be included in the Blocks config file at all.
	"""
	self.children: List[Block] = []
	Block.__init__(self, visible=visible, render=render, **kwargs)

	def __enter__(self):
	self.parent = Context.block
	Context.block = self
	return self

	def add(self, child: Block):
	child.parent = self
	self.children.append(child)

	def fill_expected_parents(self):
	children = []
	pseudo_parent = None
	for child in self.children:
	expected_parent = child.get_expected_parent()
	if not expected_parent or isinstance(self, expected_parent):
	pseudo_parent = None
	children.append(child)
	else:
	if pseudo_parent is not None and isinstance(
	pseudo_parent, expected_parent
	):
	pseudo_parent.children.append(child)
	else:
	pseudo_parent = expected_parent(render=False)
	children.append(pseudo_parent)
	pseudo_parent.children = [child]
	if Context.root_block:
	Context.root_block.blocks[pseudo_parent._id] = pseudo_parent
	child.parent = pseudo_parent
	self.children = children

	def __exit__(self, *args):
	if getattr(self, "allow_expected_parents", True):
	self.fill_expected_parents()
	Context.block = self.parent

	def postprocess(self, y):
	"""
	Any postprocessing needed to be performed on a block context.
	"""
	return y


	class BlockFunction:
	def __init__(
	self,
	fn: Callable \| None,
	inputs: List[Component],
	outputs: List[Component],
	preprocess: bool,
	postprocess: bool,
	inputs_as_dict: bool,
	tracks_progress: bool = False,
	):
	self.fn = fn
	self.inputs = inputs
	self.outputs = outputs
	self.preprocess = preprocess
	self.postprocess = postprocess
	self.tracks_progress = tracks_progress
	self.total_runtime = 0
	self.total_runs = 0
	self.inputs_as_dict = inputs_as_dict
	self.name = getattr(fn, "__name__", "fn") if fn is not None else None

	def __str__(self):
	return str(
	{
	"fn": self.name,
	"preprocess": self.preprocess,
	"postprocess": self.postprocess,
	}
	)

	def __repr__(self):
	return str(self)


	class class_or_instancemethod(classmethod):
	def __get__(self, instance, type_):
	descr_get = super().__get__ if instance is None else self.__func__.__get__
	return descr_get(instance, type_)


	def postprocess_update_dict(block: Block, update_dict: Dict, postprocess: bool = True):
	"""
	Converts a dictionary of updates into a format that can be sent to the frontend.
	E.g. {"__type__": "generic_update", "value": "2", "interactive": False}
	Into -> {"__type__": "update", "value": 2.0, "mode": "static"}

	Parameters:
	block: The Block that is being updated with this update dictionary.
	update_dict: The original update dictionary
	postprocess: Whether to postprocess the "value" key of the update dictionary.
	"""
	if update_dict.get("__type__", "") == "generic_update":
	update_dict = block.get_specific_update(update_dict)
	if update_dict.get("value") is components._Keywords.NO_VALUE:
	update_dict.pop("value")
	interactive = update_dict.pop("interactive", None)
	if interactive is not None:
	update_dict["mode"] = "dynamic" if interactive else "static"
	prediction_value = delete_none(update_dict, skip_value=True)
	if "value" in prediction_value and postprocess:
	assert isinstance(
	block, components.IOComponent
	), f"Component {block.__class__} does not support value"
	prediction_value["value"] = block.postprocess(prediction_value["value"])
	return prediction_value


	def convert_component_dict_to_list(
	outputs_ids: List[int], predictions: Dict
	) -> List \| Dict:
	"""
	Converts a dictionary of component updates into a list of updates in the order of
	the outputs_ids and including every output component. Leaves other types of dictionaries unchanged.
	E.g. {"textbox": "hello", "number": {"__type__": "generic_update", "value": "2"}}
	Into -> ["hello", {"__type__": "generic_update"}, {"__type__": "generic_update", "value": "2"}]
	"""
	keys_are_blocks = [isinstance(key, Block) for key in predictions.keys()]
	if all(keys_are_blocks):
	reordered_predictions = [skip() for _ in outputs_ids]
	for component, value in predictions.items():
	if component._id not in outputs_ids:
	raise ValueError(
	f"Returned component {component} not specified as output of function."
	)
	output_index = outputs_ids.index(component._id)
	reordered_predictions[output_index] = value
	predictions = utils.resolve_singleton(reordered_predictions)
	elif any(keys_are_blocks):
	raise ValueError(
	"Returned dictionary included some keys as Components. Either all keys must be Components to assign Component values, or return a List of values to assign output values in order."
	)
	return predictions


	@document("launch", "queue", "integrate", "load")
	class Blocks(BlockContext):
	"""
	Blocks is Gradio's low-level API that allows you to create more custom web
	applications and demos than Interfaces (yet still entirely in Python).


	Compared to the Interface class, Blocks offers more flexibility and control over:
	(1) the layout of components (2) the events that
	trigger the execution of functions (3) data flows (e.g. inputs can trigger outputs,
	which can trigger the next level of outputs). Blocks also offers ways to group
	together related demos such as with tabs.


	The basic usage of Blocks is as follows: create a Blocks object, then use it as a
	context (with the "with" statement), and then define layouts, components, or events
	within the Blocks context. Finally, call the launch() method to launch the demo.

	Example:
	import gradio as gr
	def update(name):
	return f"Welcome to Gradio, {name}!"

	with gr.Blocks() as demo:
	gr.Markdown("Start typing below and then click Run to see the output.")
	with gr.Row():
	inp = gr.Textbox(placeholder="What is your name?")
	out = gr.Textbox()
	btn = gr.Button("Run")
	btn.click(fn=update, inputs=inp, outputs=out)

	demo.launch()
	Demos: blocks_hello, blocks_flipper, blocks_speech_text_sentiment, generate_english_german, sound_alert
	Guides: blocks_and_event_listeners, controlling_layout, state_in_blocks, custom_CSS_and_JS, custom_interpretations_with_blocks, using_blocks_like_functions
	"""

	def __init__(
	self,
	theme: Theme \| str \| None = None,
	analytics_enabled: bool \| None = None,
	mode: str = "blocks",
	title: str = "Gradio",
	css: str \| None = None,
	**kwargs,
	):
	"""
	Parameters:
	analytics_enabled: whether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED environment variable or default to True.
	mode: a human-friendly name for the kind of Blocks or Interface being created.
	title: The tab title to display when this is opened in a browser window.
	css: custom css or path to custom css file to apply to entire Blocks
	"""
	# Cleanup shared parameters with Interface #TODO: is this part still necessary after Interface with Blocks?
	self.limiter = None
	self.save_to = None
	if theme is None:
	theme = DefaultTheme()
	elif isinstance(theme, str):
	try:
	theme = Theme.from_hub(theme)
	except Exception as e:
	warnings.warn(f"Cannot load {theme}. Caught Exception: {str(e)}")
	theme = DefaultTheme()
	if not isinstance(theme, Theme):
	warnings.warn("Theme should be a class loaded from gradio.themes")
	theme = DefaultTheme()
	self.theme = theme
	self.theme_css = theme._get_theme_css()
	self.stylesheets = theme._stylesheets
	self.encrypt = False
	self.share = False
	self.enable_queue = None
	self.max_threads = 40
	self.show_error = True
	if css is not None and os.path.exists(css):
	with open(css) as css_file:
	self.css = css_file.read()
	else:
	self.css = css

	# For analytics_enabled and allow_flagging: (1) first check for
	# parameter, (2) check for env variable, (3) default to True/"manual"
	self.analytics_enabled = (
	analytics_enabled
	if analytics_enabled is not None
	else os.getenv("GRADIO_ANALYTICS_ENABLED", "True") == "True"
	)
	if not self.analytics_enabled:
	os.environ["HF_HUB_DISABLE_TELEMETRY"] = "True"
	super().__init__(render=False, **kwargs)
	self.blocks: Dict[int, Block] = {}
	self.fns: List[BlockFunction] = []
	self.dependencies = []
	self.mode = mode

	self.is_running = False
	self.local_url = None
	self.share_url = None
	self.width = None
	self.height = None
	self.api_open = True

	self.is_space = True if os.getenv("SYSTEM") == "spaces" else False
	self.favicon_path = None
	self.auth = None
	self.dev_mode = True
	self.app_id = random.getrandbits(64)
	self.temp_file_sets = []
	self.title = title
	self.show_api = True

	# Only used when an Interface is loaded from a config
	self.predict = None
	self.input_components = None
	self.output_components = None
	self.__name__ = None
	self.api_mode = None
	self.progress_tracking = None

	self.file_directories = []

	if self.analytics_enabled:
	data = {
	"mode": self.mode,
	"custom_css": self.css is not None,
	"theme": self.theme,
	"version": GRADIO_VERSION,
	}
	utils.initiated_analytics(data)

	@classmethod
	def from_config(
	cls,
	config: dict,
	fns: List[Callable],
	root_url: str \| None = None,
	) -> Blocks:
	"""
	Factory method that creates a Blocks from a config and list of functions.

	Parameters:
	config: a dictionary containing the configuration of the Blocks.
	fns: a list of functions that are used in the Blocks. Must be in the same order as the dependencies in the config.
	root_url: an optional root url to use for the components in the Blocks. Allows serving files from an external URL.
	"""
	config = copy.deepcopy(config)
	components_config = config["components"]
	original_mapping: Dict[int, Block] = {}

	def get_block_instance(id: int) -> Block:
	for block_config in components_config:
	if block_config["id"] == id:
	break
	else:
	raise ValueError("Cannot find block with id {}".format(id))
	cls = component_or_layout_class(block_config["type"])
	block_config["props"].pop("type", None)
	block_config["props"].pop("name", None)
	style = block_config["props"].pop("style", None)
	if block_config["props"].get("root_url") is None and root_url:
	block_config["props"]["root_url"] = root_url + "/"
	# Any component has already processed its initial value, so we skip that step here
	block = cls(**block_config["props"], _skip_init_processing=True)
	if style and isinstance(block, components.IOComponent):
	block.style(**style)
	return block

	def iterate_over_children(children_list):
	for child_config in children_list:
	id = child_config["id"]
	block = get_block_instance(id)

	original_mapping[id] = block

	children = child_config.get("children")
	if children is not None:
	assert isinstance(
	block, BlockContext
	), f"Invalid config, Block with id {id} has children but is not a BlockContext."
	with block:
	iterate_over_children(children)

	derived_fields = ["types"]

	with Blocks() as blocks:
	# ID 0 should be the root Blocks component
	original_mapping[0] = Context.root_block or blocks

	iterate_over_children(config["layout"]["children"])

	first_dependency = None

	# add the event triggers
	for dependency, fn in zip(config["dependencies"], fns):
	# We used to add a "fake_event" to the config to cache examples
	# without removing it. This was causing bugs in calling gr.Interface.load
	# We fixed the issue by removing "fake_event" from the config in examples.py
	# but we still need to skip these events when loading the config to support
	# older demos
	if dependency["trigger"] == "fake_event":
	continue
	for field in derived_fields:
	dependency.pop(field, None)
	targets = dependency.pop("targets")
	trigger = dependency.pop("trigger")
	dependency.pop("backend_fn")
	dependency.pop("documentation", None)
	dependency["inputs"] = [
	original_mapping[i] for i in dependency["inputs"]
	]
	dependency["outputs"] = [
	original_mapping[o] for o in dependency["outputs"]
	]
	dependency.pop("status_tracker", None)
	dependency["preprocess"] = False
	dependency["postprocess"] = False

	for target in targets:
	dependency = original_mapping[target].set_event_trigger(
	event_name=trigger, fn=fn, **dependency
	)[0]
	if first_dependency is None:
	first_dependency = dependency

	# Allows some use of Interface-specific methods with loaded Spaces
	if first_dependency and Context.root_block:
	blocks.predict = [fns[0]]
	blocks.input_components = [
	Context.root_block.blocks[i] for i in first_dependency["inputs"]
	]
	blocks.output_components = [
	Context.root_block.blocks[o] for o in first_dependency["outputs"]
	]
	blocks.__name__ = "Interface"
	blocks.api_mode = True

	return blocks

	def __str__(self):
	return self.__repr__()

	def __repr__(self):
	num_backend_fns = len([d for d in self.dependencies if d["backend_fn"]])
	repr = f"Gradio Blocks instance: {num_backend_fns} backend functions"
	repr += "\n" + "-" * len(repr)
	for d, dependency in enumerate(self.dependencies):
	if dependency["backend_fn"]:
	repr += f"\nfn_index={d}"
	repr += "\n inputs:"
	for input_id in dependency["inputs"]:
	block = self.blocks[input_id]
	repr += "\n \|-{}".format(str(block))
	repr += "\n outputs:"
	for output_id in dependency["outputs"]:
	block = self.blocks[output_id]
	repr += "\n \|-{}".format(str(block))
	return repr

	def render(self):
	if Context.root_block is not None:
	if self._id in Context.root_block.blocks:
	raise DuplicateBlockError(
	f"A block with id: {self._id} has already been rendered in the current Blocks."
	)
	if not set(Context.root_block.blocks).isdisjoint(self.blocks):
	raise DuplicateBlockError(
	"At least one block in this Blocks has already been rendered."
	)

	Context.root_block.blocks.update(self.blocks)
	Context.root_block.fns.extend(self.fns)
	dependency_offset = len(Context.root_block.dependencies)
	for i, dependency in enumerate(self.dependencies):
	api_name = dependency["api_name"]
	if api_name is not None:
	api_name_ = utils.append_unique_suffix(
	api_name,
	[dep["api_name"] for dep in Context.root_block.dependencies],
	)
	if not (api_name == api_name_):
	warnings.warn(
	"api_name {} already exists, using {}".format(
	api_name, api_name_
	)
	)
	dependency["api_name"] = api_name_
	dependency["cancels"] = [
	c + dependency_offset for c in dependency["cancels"]
	]
	if dependency.get("trigger_after") is not None:
	dependency["trigger_after"] += dependency_offset
	# Recreate the cancel function so that it has the latest
	# dependency fn indices. This is necessary to properly cancel
	# events in the backend
	if dependency["cancels"]:
	updated_cancels = [
	Context.root_block.dependencies[i]
	for i in dependency["cancels"]
	]
	new_fn = BlockFunction(
	get_cancel_function(updated_cancels)[0],
	[],
	[],
	False,
	True,
	False,
	)
	Context.root_block.fns[dependency_offset + i] = new_fn
	Context.root_block.dependencies.append(dependency)
	Context.root_block.temp_file_sets.extend(self.temp_file_sets)

	if Context.block is not None:
	Context.block.children.extend(self.children)
	return self

	def is_callable(self, fn_index: int = 0) -> bool:
	"""Checks if a particular Blocks function is callable (i.e. not stateful or a generator)."""
	block_fn = self.fns[fn_index]
	dependency = self.dependencies[fn_index]

	if inspect.isasyncgenfunction(block_fn.fn):
	return False
	if inspect.isgeneratorfunction(block_fn.fn):
	return False
	for input_id in dependency["inputs"]:
	block = self.blocks[input_id]
	if getattr(block, "stateful", False):
	return False
	for output_id in dependency["outputs"]:
	block = self.blocks[output_id]
	if getattr(block, "stateful", False):
	return False

	return True

	def __call__(self, *inputs, fn_index: int = 0, api_name: str \| None = None):
	"""
	Allows Blocks objects to be called as functions. Supply the parameters to the
	function as positional arguments. To choose which function to call, use the
	fn_index parameter, which must be a keyword argument.

	Parameters:
	*inputs: the parameters to pass to the function
	fn_index: the index of the function to call (defaults to 0, which for Interfaces, is the default prediction function)
	api_name: The api_name of the dependency to call. Will take precedence over fn_index.
	"""
	if api_name is not None:
	inferred_fn_index = next(
	(
	i
	for i, d in enumerate(self.dependencies)
	if d.get("api_name") == api_name
	),
	None,
	)
	if inferred_fn_index is None:
	raise InvalidApiName(f"Cannot find a function with api_name {api_name}")
	fn_index = inferred_fn_index
	if not (self.is_callable(fn_index)):
	raise ValueError(
	"This function is not callable because it is either stateful or is a generator. Please use the .launch() method instead to create an interactive user interface."
	)

	inputs = list(inputs)
	processed_inputs = self.serialize_data(fn_index, inputs)
	batch = self.dependencies[fn_index]["batch"]
	if batch:
	processed_inputs = [[inp] for inp in processed_inputs]

	outputs = utils.synchronize_async(
	self.process_api,
	fn_index=fn_index,
	inputs=processed_inputs,
	request=None,
	state={},
	)
	outputs = outputs["data"]

	if batch:
	outputs = [out[0] for out in outputs]

	processed_outputs = self.deserialize_data(fn_index, outputs)
	processed_outputs = utils.resolve_singleton(processed_outputs)

	return processed_outputs

	async def call_function(
	self,
	fn_index: int,
	processed_input: List[Any],
	iterator: Iterator[Any] \| None = None,
	requests: routes.Request \| List[routes.Request] \| None = None,
	event_id: str \| None = None,
	event_data: EventData \| None = None,
	):
	"""
	Calls function with given index and preprocessed input, and measures process time.
	Parameters:
	fn_index: index of function to call
	processed_input: preprocessed input to pass to function
	iterator: iterator to use if function is a generator
	requests: requests to pass to function
	event_id: id of event in queue
	event_data: data associated with event trigger
	"""
	block_fn = self.fns[fn_index]
	assert block_fn.fn, f"function with index {fn_index} not defined."
	is_generating = False

	if block_fn.inputs_as_dict:
	processed_input = [
	{
	input_component: data
	for input_component, data in zip(block_fn.inputs, processed_input)
	}
	]

	if isinstance(requests, list):
	request = requests[0]
	else:
	request = requests
	processed_input, progress_index, _ = special_args(
	block_fn.fn, processed_input, request, event_data
	)
	progress_tracker = (
	processed_input[progress_index] if progress_index is not None else None
	)

	start = time.time()

	if iterator is None: # If not a generator function that has already run
	if progress_tracker is not None and progress_index is not None:
	progress_tracker, fn = create_tracker(
	self, event_id, block_fn.fn, progress_tracker.track_tqdm
	)
	processed_input[progress_index] = progress_tracker
	else:
	fn = block_fn.fn

	if inspect.iscoroutinefunction(fn):
	prediction = await fn(*processed_input)
	else:
	prediction = await anyio.to_thread.run_sync(
	fn, *processed_input, limiter=self.limiter
	)
	else:
	prediction = None

	if inspect.isasyncgenfunction(block_fn.fn):
	raise ValueError("Gradio does not support async generators.")
	if inspect.isgeneratorfunction(block_fn.fn):
	if not self.enable_queue:
	raise ValueError("Need to enable queue to use generators.")
	try:
	if iterator is None:
	iterator = prediction
	prediction = await anyio.to_thread.run_sync(
	utils.async_iteration, iterator, limiter=self.limiter
	)
	is_generating = True
	except StopAsyncIteration:
	n_outputs = len(self.dependencies[fn_index].get("outputs"))
	prediction = (
	components._Keywords.FINISHED_ITERATING
	if n_outputs == 1
	else (components._Keywords.FINISHED_ITERATING,) * n_outputs
	)
	iterator = None

	duration = time.time() - start

	return {
	"prediction": prediction,
	"duration": duration,
	"is_generating": is_generating,
	"iterator": iterator,
	}

	def serialize_data(self, fn_index: int, inputs: List[Any]) -> List[Any]:
	dependency = self.dependencies[fn_index]
	processed_input = []

	for i, input_id in enumerate(dependency["inputs"]):
	block = self.blocks[input_id]
	assert isinstance(
	block, components.IOComponent
	), f"{block.__class__} Component with id {input_id} not a valid input component."
	serialized_input = block.serialize(inputs[i])
	processed_input.append(serialized_input)

	return processed_input

	def deserialize_data(self, fn_index: int, outputs: List[Any]) -> List[Any]:
	dependency = self.dependencies[fn_index]
	predictions = []

	for o, output_id in enumerate(dependency["outputs"]):
	block = self.blocks[output_id]
	assert isinstance(
	block, components.IOComponent
	), f"{block.__class__} Component with id {output_id} not a valid output component."
	deserialized = block.deserialize(outputs[o], root_url=block.root_url)
	predictions.append(deserialized)

	return predictions

	def preprocess_data(self, fn_index: int, inputs: List[Any], state: Dict[int, Any]):
	block_fn = self.fns[fn_index]
	dependency = self.dependencies[fn_index]

	if block_fn.preprocess:
	processed_input = []
	for i, input_id in enumerate(dependency["inputs"]):
	block = self.blocks[input_id]
	assert isinstance(
	block, components.Component
	), f"{block.__class__} Component with id {input_id} not a valid input component."
	if getattr(block, "stateful", False):
	processed_input.append(state.get(input_id))
	else:
	processed_input.append(block.preprocess(inputs[i]))
	else:
	processed_input = inputs
	return processed_input

	def postprocess_data(
	self, fn_index: int, predictions: List \| Dict, state: Dict[int, Any]
	):
	block_fn = self.fns[fn_index]
	dependency = self.dependencies[fn_index]
	batch = dependency["batch"]

	if type(predictions) is dict and len(predictions) > 0:
	predictions = convert_component_dict_to_list(
	dependency["outputs"], predictions
	)

	if len(dependency["outputs"]) == 1 and not (batch):
	predictions = [
	predictions,
	]

	output = []
	for i, output_id in enumerate(dependency["outputs"]):
	try:
	if predictions[i] is components._Keywords.FINISHED_ITERATING:
	output.append(None)
	continue
	except (IndexError, KeyError):
	raise ValueError(
	f"Number of output components does not match number of values returned from from function {block_fn.name}"
	)
	block = self.blocks[output_id]
	if getattr(block, "stateful", False):
	if not utils.is_update(predictions[i]):
	state[output_id] = predictions[i]
	output.append(None)
	else:
	prediction_value = predictions[i]
	if utils.is_update(prediction_value):
	assert isinstance(prediction_value, dict)
	prediction_value = postprocess_update_dict(
	block=block,
	update_dict=prediction_value,
	postprocess=block_fn.postprocess,
	)
	elif block_fn.postprocess:
	assert isinstance(
	block, components.Component
	), f"{block.__class__} Component with id {output_id} not a valid output component."
	prediction_value = block.postprocess(prediction_value)
	output.append(prediction_value)

	return output

	async def process_api(
	self,
	fn_index: int,
	inputs: List[Any],
	state: Dict[int, Any],
	request: routes.Request \| List[routes.Request] \| None = None,
	iterators: Dict[int, Any] \| None = None,
	event_id: str \| None = None,
	event_data: EventData \| None = None,
	) -> Dict[str, Any]:
	"""
	Processes API calls from the frontend. First preprocesses the data,
	then runs the relevant function, then postprocesses the output.
	Parameters:
	fn_index: Index of function to run.
	inputs: input data received from the frontend
	username: name of user if authentication is set up (not used)
	state: data stored from stateful components for session (key is input block id)
	iterators: the in-progress iterators for each generator function (key is function index)
	event_id: id of event that triggered this API call
	event_data: data associated with the event trigger itself
	Returns: None
	"""
	block_fn = self.fns[fn_index]
	batch = self.dependencies[fn_index]["batch"]

	if batch:
	max_batch_size = self.dependencies[fn_index]["max_batch_size"]
	batch_sizes = [len(inp) for inp in inputs]
	batch_size = batch_sizes[0]
	if inspect.isasyncgenfunction(block_fn.fn) or inspect.isgeneratorfunction(
	block_fn.fn
	):
	raise ValueError("Gradio does not support generators in batch mode.")
	if not all(x == batch_size for x in batch_sizes):
	raise ValueError(
	f"All inputs to a batch function must have the same length but instead have sizes: {batch_sizes}."
	)
	if batch_size > max_batch_size:
	raise ValueError(
	f"Batch size ({batch_size}) exceeds the max_batch_size for this function ({max_batch_size})"
	)

	inputs = [
	self.preprocess_data(fn_index, list(i), state) for i in zip(*inputs)
	]
	result = await self.call_function(
	fn_index, list(zip(*inputs)), None, request, event_id, event_data
	)
	preds = result["prediction"]
	data = [
	self.postprocess_data(fn_index, list(o), state) for o in zip(*preds)
	]
	data = list(zip(*data))
	is_generating, iterator = None, None
	else:
	inputs = self.preprocess_data(fn_index, inputs, state)
	iterator = iterators.get(fn_index, None) if iterators else None
	result = await self.call_function(
	fn_index, inputs, iterator, request, event_id, event_data
	)
	data = self.postprocess_data(fn_index, result["prediction"], state)
	is_generating, iterator = result["is_generating"], result["iterator"]

	block_fn.total_runtime += result["duration"]
	block_fn.total_runs += 1

	return {
	"data": data,
	"is_generating": is_generating,
	"iterator": iterator,
	"duration": result["duration"],
	"average_duration": block_fn.total_runtime / block_fn.total_runs,
	}

	async def create_limiter(self):
	self.limiter = (
	None
	if self.max_threads == 40
	else CapacityLimiter(total_tokens=self.max_threads)
	)

	def get_config(self):
	return {"type": "column"}

	def get_config_file(self):
	config = {
	"version": routes.VERSION,
	"mode": self.mode,
	"dev_mode": self.dev_mode,
	"analytics_enabled": self.analytics_enabled,
	"components": [],
	"css": self.css,
	"title": self.title or "Gradio",
	"is_space": self.is_space,
	"enable_queue": getattr(self, "enable_queue", False), # launch attributes
	"show_error": getattr(self, "show_error", False),
	"show_api": self.show_api,
	"is_colab": utils.colab_check(),
	"stylesheets": self.stylesheets,
	"root": self.root,
	}

	def getLayout(block):
	if not isinstance(block, BlockContext):
	return {"id": block._id}
	children_layout = []
	for child in block.children:
	children_layout.append(getLayout(child))
	return {"id": block._id, "children": children_layout}

	config["layout"] = getLayout(self)

	for _id, block in self.blocks.items():
	config["components"].append(
	{
	"id": _id,
	"type": (block.get_block_name()),
	"props": utils.delete_none(block.get_config())
	if hasattr(block, "get_config")
	else {},
	}
	)
	config["dependencies"] = self.dependencies
	return config

	def __enter__(self):
	if Context.block is None:
	Context.root_block = self
	self.parent = Context.block
	Context.block = self
	return self

	def __exit__(self, *args):
	super().fill_expected_parents()
	Context.block = self.parent
	# Configure the load events before root_block is reset
	self.attach_load_events()
	if self.parent is None:
	Context.root_block = None
	else:
	self.parent.children.extend(self.children)
	self.config = self.get_config_file()
	self.app = routes.App.create_app(self)
	self.progress_tracking = any(block_fn.tracks_progress for block_fn in self.fns)

	@class_or_instancemethod
	def load(
	self_or_cls,
	fn: Callable \| None = None,
	inputs: List[Component] \| None = None,
	outputs: List[Component] \| None = None,
	api_name: str \| 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,
	every: float \| None = None,
	_js: str \| None = None,
	*,
	name: str \| None = None,
	src: str \| None = None,
	api_key: str \| None = None,
	alias: str \| None = None,
	**kwargs,
	) -> Blocks \| Dict[str, Any] \| None:
	"""
	For reverse compatibility reasons, this is both a class method and an instance
	method, the two of which, confusingly, do two completely different things.


	Class method: loads a demo from a Hugging Face Spaces repo and creates it locally and returns a block instance. Equivalent to gradio.Interface.load()


	Instance method: adds event that runs as soon as the demo loads in the browser. Example usage below.
	Parameters:
	name: Class Method - the name of the model (e.g. "gpt2" or "facebook/bart-base") or space (e.g. "flax-community/spanish-gpt2"), can include the `src` as prefix (e.g. "models/facebook/bart-base")
	src: Class Method - the source of the model: `models` or `spaces` (or leave empty if source is provided as a prefix in `name`)
	api_key: Class Method - optional access token for loading private Hugging Face Hub models or spaces. Find your token here: https://huggingface.co/settings/tokens
	alias: Class Method - optional string used as the name of the loaded model instead of the default name (only applies if loading a Space running Gradio 2.x)
	fn: Instance Method - 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: Instance Method - List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
	outputs: Instance Method - List of gradio.components to use as inputs. If the function returns no outputs, this should be an empty list.
	api_name: Instance Method - Defining this parameter exposes the endpoint in the api docs
	scroll_to_output: Instance Method - If True, will scroll to output component on completion
	show_progress: Instance Method - If True, will show progress animation while pending
	queue: Instance Method - If True, will place the request on the queue, if the queue exists
	batch: Instance Method - 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: Instance Method - Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
	preprocess: Instance Method - 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: Instance Method - If False, will not run postprocessing of component data before returning 'fn' output to the browser.
	every: Instance Method - Run this event 'every' number of seconds. Interpreted in seconds. Queue must be enabled.
	Example:
	import gradio as gr
	import datetime
	with gr.Blocks() as demo:
	def get_time():
	return datetime.datetime.now().time()
	dt = gr.Textbox(label="Current time")
	demo.load(get_time, inputs=None, outputs=dt)
	demo.launch()
	"""
	# _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 isinstance(self_or_cls, type):
	if name is None:
	raise ValueError(
	"Blocks.load() requires passing parameters as keyword arguments"
	)
	return external.load_blocks_from_repo(name, src, api_key, alias, **kwargs)
	else:
	return self_or_cls.set_event_trigger(
	event_name="load",
	fn=fn,
	inputs=inputs,
	outputs=outputs,
	api_name=api_name,
	preprocess=preprocess,
	postprocess=postprocess,
	scroll_to_output=scroll_to_output,
	show_progress=show_progress,
	js=_js,
	queue=queue,
	batch=batch,
	max_batch_size=max_batch_size,
	every=every,
	no_target=True,
	)[0]

	def clear(self):
	"""Resets the layout of the Blocks object."""
	self.blocks = {}
	self.fns = []
	self.dependencies = []
	self.children = []
	return self

	@document()
	def queue(
	self,
	concurrency_count: int = 1,
	status_update_rate: float \| Literal["auto"] = "auto",
	client_position_to_load_data: int \| None = None,
	default_enabled: bool \| None = None,
	api_open: bool = True,
	max_size: int \| None = None,
	):
	"""
	You can control the rate of processed requests by creating a queue. This will allow you to set the number of requests to be processed at one time, and will let users know their position in the queue.
	Parameters:
	concurrency_count: Number of worker threads that will be processing requests from the queue concurrently. Increasing this number will increase the rate at which requests are processed, but will also increase the memory usage of the queue.
	status_update_rate: If "auto", Queue will send status estimations to all clients whenever a job is finished. Otherwise Queue will send status at regular intervals set by this parameter as the number of seconds.
	client_position_to_load_data: DEPRECATED. This parameter is deprecated and has no effect.
	default_enabled: Deprecated and has no effect.
	api_open: If True, the REST routes of the backend will be open, allowing requests made directly to those endpoints to skip the queue.
	max_size: The maximum number of events the queue will store at any given moment. If the queue is full, new events will not be added and a user will receive a message saying that the queue is full. If None, the queue size will be unlimited.
	Example: (Blocks)
	with gr.Blocks() as demo:
	button = gr.Button(label="Generate Image")
	button.click(fn=image_generator, inputs=gr.Textbox(), outputs=gr.Image())
	demo.queue(concurrency_count=3)
	demo.launch()
	Example: (Interface)
	demo = gr.Interface(image_generator, gr.Textbox(), gr.Image())
	demo.queue(concurrency_count=3)
	demo.launch()
	"""
	if default_enabled is not None:
	warnings.warn(
	"The default_enabled parameter of queue has no effect and will be removed "
	"in a future version of gradio."
	)
	self.enable_queue = True
	self.api_open = api_open
	if client_position_to_load_data is not None:
	warnings.warn("The client_position_to_load_data parameter is deprecated.")
	self._queue = queueing.Queue(
	live_updates=status_update_rate == "auto",
	concurrency_count=concurrency_count,
	update_intervals=status_update_rate if status_update_rate != "auto" else 1,
	max_size=max_size,
	blocks_dependencies=self.dependencies,
	)
	self.config = self.get_config_file()
	self.app = routes.App.create_app(self)
	return self

	def launch(
	self,
	inline: bool \| None = None,
	inbrowser: bool = False,
	share: bool \| None = None,
	debug: bool = False,
	enable_queue: bool \| None = None,
	max_threads: int = 40,
	auth: Callable \| Tuple[str, str] \| List[Tuple[str, str]] \| None = None,
	auth_message: str \| None = None,
	prevent_thread_lock: bool = False,
	show_error: bool = False,
	server_name: str \| None = None,
	server_port: int \| None = None,
	show_tips: bool = False,
	height: int = 500,
	width: int \| str = "100%",
	encrypt: bool \| None = None,
	favicon_path: str \| None = None,
	ssl_keyfile: str \| None = None,
	ssl_certfile: str \| None = None,
	ssl_keyfile_password: str \| None = None,
	quiet: bool = False,
	show_api: bool = True,
	file_directories: List[str] \| None = None,
	_frontend: bool = True,
	) -> Tuple[FastAPI, str, str]:
	"""
	Launches a simple web server that serves the demo. Can also be used to create a
	public link used by anyone to access the demo from their browser by setting share=True.

	Parameters:
	inline: whether to display in the interface inline in an iframe. Defaults to True in python notebooks; False otherwise.
	inbrowser: whether to automatically launch the interface in a new tab on the default browser.
	share: whether to create a publicly shareable link for the interface. Creates an SSH tunnel to make your UI accessible from anywhere. If not provided, it is set to False by default every time, except when running in Google Colab. When localhost is not accessible (e.g. Google Colab), setting share=False is not supported.
	debug: if True, blocks the main thread from running. If running in Google Colab, this is needed to print the errors in the cell output.
	auth: If provided, username and password (or list of username-password tuples) required to access interface. Can also provide function that takes username and password and returns True if valid login.
	auth_message: If provided, HTML message provided on login page.
	prevent_thread_lock: If True, the interface will block the main thread while the server is running.
	show_error: If True, any errors in the interface will be displayed in an alert modal and printed in the browser console log
	server_port: will start gradio app on this port (if available). Can be set by environment variable GRADIO_SERVER_PORT. If None, will search for an available port starting at 7860.
	server_name: to make app accessible on local network, set this to "0.0.0.0". Can be set by environment variable GRADIO_SERVER_NAME. If None, will use "127.0.0.1".
	show_tips: if True, will occasionally show tips about new Gradio features
	enable_queue: DEPRECATED (use .queue() method instead.) if True, inference requests will be served through a queue instead of with parallel threads. Required for longer inference times (> 1min) to prevent timeout. The default option in HuggingFace Spaces is True. The default option elsewhere is False.
	max_threads: the maximum number of total threads that the Gradio app can generate in parallel. The default is inherited from the starlette library (currently 40). Applies whether the queue is enabled or not. But if queuing is enabled, this parameter is increaseed to be at least the concurrency_count of the queue.
	width: The width in pixels of the iframe element containing the interface (used if inline=True)
	height: The height in pixels of the iframe element containing the interface (used if inline=True)
	encrypt: DEPRECATED. Has no effect.
	favicon_path: If a path to a file (.png, .gif, or .ico) is provided, it will be used as the favicon for the web page.
	ssl_keyfile: If a path to a file is provided, will use this as the private key file to create a local server running on https.
	ssl_certfile: If a path to a file is provided, will use this as the signed certificate for https. Needs to be provided if ssl_keyfile is provided.
	ssl_keyfile_password: If a password is provided, will use this with the ssl certificate for https.
	quiet: If True, suppresses most print statements.
	show_api: If True, shows the api docs in the footer of the app. Default True. If the queue is enabled, then api_open parameter of .queue() will determine if the api docs are shown, independent of the value of show_api.
	file_directories: List of directories that gradio is allowed to serve files from (in addition to the directory containing the gradio python file). Must be absolute paths. Warning: any files in these directories or its children are potentially accessible to all users of your app.
	Returns:
	app: FastAPI app object that is running the demo
	local_url: Locally accessible link to the demo
	share_url: Publicly accessible link to the demo (if share=True, otherwise None)
	Example: (Blocks)
	import gradio as gr
	def reverse(text):
	return text[::-1]
	with gr.Blocks() as demo:
	button = gr.Button(value="Reverse")
	button.click(reverse, gr.Textbox(), gr.Textbox())
	demo.launch(share=True, auth=("username", "password"))
	Example: (Interface)
	import gradio as gr
	def reverse(text):
	return text[::-1]
	demo = gr.Interface(reverse, "text", "text")
	demo.launch(share=True, auth=("username", "password"))
	"""
	self.dev_mode = False
	if (
	auth
	and not callable(auth)
	and not isinstance(auth[0], tuple)
	and not isinstance(auth[0], list)
	):
	self.auth = [auth]
	else:
	self.auth = auth
	self.auth_message = auth_message
	self.show_tips = show_tips
	self.show_error = show_error
	self.height = height
	self.width = width
	self.favicon_path = favicon_path

	if enable_queue is not None:
	self.enable_queue = enable_queue
	warnings.warn(
	"The `enable_queue` parameter has been deprecated. Please use the `.queue()` method instead.",
	DeprecationWarning,
	)
	if encrypt is not None:
	warnings.warn(
	"The `encrypt` parameter has been deprecated and has no effect.",
	DeprecationWarning,
	)

	if self.is_space:
	self.enable_queue = self.enable_queue is not False
	else:
	self.enable_queue = self.enable_queue is True
	if self.enable_queue and not hasattr(self, "_queue"):
	self.queue()
	self.show_api = self.api_open if self.enable_queue else show_api

	self.file_directories = file_directories if file_directories is not None else []
	if not isinstance(self.file_directories, list):
	raise ValueError("file_directories must be a list of directories.")

	if not self.enable_queue and self.progress_tracking:
	raise ValueError("Progress tracking requires queuing to be enabled.")

	for dep in self.dependencies:
	for i in dep["cancels"]:
	if not self.queue_enabled_for_fn(i):
	raise ValueError(
	"In order to cancel an event, the queue for that event must be enabled! "
	"You may get this error by either 1) passing a function that uses the yield keyword "
	"into an interface without enabling the queue or 2) defining an event that cancels "
	"another event without enabling the queue. Both can be solved by calling .queue() "
	"before .launch()"
	)
	if dep["batch"] and (
	dep["queue"] is False
	or (dep["queue"] is None and not self.enable_queue)
	):
	raise ValueError("In order to use batching, the queue must be enabled.")

	self.config = self.get_config_file()
	self.max_threads = max(
	self._queue.max_thread_count if self.enable_queue else 0, max_threads
	)

	if self.is_running:
	assert isinstance(
	self.local_url, str
	), f"Invalid local_url: {self.local_url}"
	if not (quiet):
	print(
	"Rerunning server... use `close()` to stop if you need to change `launch()` parameters.\n----"
	)
	else:
	server_name, server_port, local_url, app, server = networking.start_server(
	self,
	server_name,
	server_port,
	ssl_keyfile,
	ssl_certfile,
	ssl_keyfile_password,
	)
	self.server_name = server_name
	self.local_url = local_url
	self.server_port = server_port
	self.server_app = app
	self.server = server
	self.is_running = True
	self.is_colab = utils.colab_check()
	self.is_kaggle = utils.kaggle_check()
	self.is_sagemaker = utils.sagemaker_check()

	self.protocol = (
	"https"
	if self.local_url.startswith("https") or self.is_colab
	else "http"
	)

	if self.enable_queue:
	self._queue.set_url(self.local_url)

	# Cannot run async functions in background other than app's scope.
	# Workaround by triggering the app endpoint
	requests.get(f"{self.local_url}startup-events")

	utils.launch_counter()

	if share is None:
	if self.is_colab and self.enable_queue:
	if not quiet:
	print(
	"Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n"
	)
	self.share = True
	elif self.is_kaggle:
	if not quiet:
	print(
	"Kaggle notebooks require sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n"
	)
	self.share = True
	elif self.is_sagemaker:
	if not quiet:
	print(
	"Sagemaker notebooks may require sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n"
	)
	self.share = True
	else:
	self.share = False
	else:
	self.share = share

	# If running in a colab or not able to access localhost,
	# a shareable link must be created.
	if _frontend and (not networking.url_ok(self.local_url)) and (not self.share):
	raise ValueError(
	"When localhost is not accessible, a shareable link must be created. Please set share=True."
	)

	if self.is_colab:
	if not quiet:
	if debug:
	print(strings.en["COLAB_DEBUG_TRUE"])
	else:
	print(strings.en["COLAB_DEBUG_FALSE"])
	if not self.share:
	print(strings.en["COLAB_WARNING"].format(self.server_port))
	if self.enable_queue and not self.share:
	raise ValueError(
	"When using queueing in Colab, a shareable link must be created. Please set share=True."
	)
	else:
	print(
	strings.en["RUNNING_LOCALLY_SEPARATED"].format(
	self.protocol, self.server_name, self.server_port
	)
	)

	if self.share:
	if self.is_space:
	raise RuntimeError("Share is not supported when you are in Spaces")
	try:
	if self.share_url is None:
	self.share_url = networking.setup_tunnel(
	self.server_name, self.server_port, self.share_token
	)
	print(strings.en["SHARE_LINK_DISPLAY"].format(self.share_url))
	if not (quiet):
	print(strings.en["SHARE_LINK_MESSAGE"])
	except (RuntimeError, requests.exceptions.ConnectionError):
	if self.analytics_enabled:
	utils.error_analytics("Not able to set up tunnel")
	self.share_url = None
	self.share = False
	print(strings.en["COULD_NOT_GET_SHARE_LINK"])
	else:
	if not (quiet):
	print(strings.en["PUBLIC_SHARE_TRUE"])
	self.share_url = None

	if inbrowser:
	link = self.share_url if self.share and self.share_url else self.local_url
	webbrowser.open(link)

	# Check if running in a Python notebook in which case, display inline
	if inline is None:
	inline = utils.ipython_check() and (self.auth is None)
	if inline:
	if self.auth is not None:
	print(
	"Warning: authentication is not supported inline. Please"
	"click the link to access the interface in a new tab."
	)
	try:
	from IPython.display import HTML, Javascript, display # type: ignore

	if self.share and self.share_url:
	while not networking.url_ok(self.share_url):
	time.sleep(0.25)
	display(
	HTML(
	f'<div><iframe src="{self.share_url}" width="{self.width}" height="{self.height}" allow="autoplay; camera; microphone; clipboard-read; clipboard-write;" frameborder="0" allowfullscreen></iframe></div>'
	)
	)
	elif self.is_colab:
	# modified from /usr/local/lib/python3.7/dist-packages/google/colab/output/_util.py within Colab environment
	code = """(async (port, path, width, height, cache, element) => {
	if (!google.colab.kernel.accessAllowed && !cache) {
	return;
	}
	element.appendChild(document.createTextNode(''));
	const url = await google.colab.kernel.proxyPort(port, {cache});

	const external_link = document.createElement('div');
	external_link.innerHTML = `
	<div style="font-family: monospace; margin-bottom: 0.5rem">
	Running on <a href=${new URL(path, url).toString()} target="_blank">
	https://localhost:${port}${path}
	</a>
	</div>
	`;
	element.appendChild(external_link);

	const iframe = document.createElement('iframe');
	iframe.src = new URL(path, url).toString();
	iframe.height = height;
	iframe.allow = "autoplay; camera; microphone; clipboard-read; clipboard-write;"
	iframe.width = width;
	iframe.style.border = 0;
	element.appendChild(iframe);
	})""" + "({port}, {path}, {width}, {height}, {cache}, window.element)".format(
	port=json.dumps(self.server_port),
	path=json.dumps("/"),
	width=json.dumps(self.width),
	height=json.dumps(self.height),
	cache=json.dumps(False),
	)

	display(Javascript(code))
	else:
	display(
	HTML(
	f'<div><iframe src="{self.local_url}" width="{self.width}" height="{self.height}" allow="autoplay; camera; microphone; clipboard-read; clipboard-write;" frameborder="0" allowfullscreen></iframe></div>'
	)
	)
	except ImportError:
	pass

	if getattr(self, "analytics_enabled", False):
	data = {
	"launch_method": "browser" if inbrowser else "inline",
	"is_google_colab": self.is_colab,
	"is_sharing_on": self.share,
	"share_url": self.share_url,
	"enable_queue": self.enable_queue,
	"show_tips": self.show_tips,
	"server_name": server_name,
	"server_port": server_port,
	"is_spaces": self.is_space,
	"mode": self.mode,
	}
	utils.launch_analytics(data)
	utils.launched_telemetry(self, data)

	utils.show_tip(self)

	# Block main thread if debug==True
	if debug or int(os.getenv("GRADIO_DEBUG", 0)) == 1:
	self.block_thread()
	# Block main thread if running in a script to stop script from exiting
	is_in_interactive_mode = bool(getattr(sys, "ps1", sys.flags.interactive))

	if not prevent_thread_lock and not is_in_interactive_mode:
	self.block_thread()

	return TupleNoPrint((self.server_app, self.local_url, self.share_url))

	def integrate(
	self,
	comet_ml: comet_ml.Experiment \| None = None,
	wandb: ModuleType \| None = None,
	mlflow: ModuleType \| None = None,
	) -> None:
	"""
	A catch-all method for integrating with other libraries. This method should be run after launch()
	Parameters:
	comet_ml: If a comet_ml Experiment object is provided, will integrate with the experiment and appear on Comet dashboard
	wandb: If the wandb module is provided, will integrate with it and appear on WandB dashboard
	mlflow: If the mlflow module is provided, will integrate with the experiment and appear on ML Flow dashboard
	"""
	analytics_integration = ""
	if comet_ml is not None:
	analytics_integration = "CometML"
	comet_ml.log_other("Created from", "Gradio")
	if self.share_url is not None:
	comet_ml.log_text("gradio: " + self.share_url)
	comet_ml.end()
	elif self.local_url:
	comet_ml.log_text("gradio: " + self.local_url)
	comet_ml.end()
	else:
	raise ValueError("Please run `launch()` first.")
	if wandb is not None:
	analytics_integration = "WandB"
	if self.share_url is not None:
	wandb.log(
	{
	"Gradio panel": wandb.Html(
	'<iframe src="'
	+ self.share_url
	+ '" width="'
	+ str(self.width)
	+ '" height="'
	+ str(self.height)
	+ '" frameBorder="0"></iframe>'
	)
	}
	)
	else:
	print(
	"The WandB integration requires you to "
	"`launch(share=True)` first."
	)
	if mlflow is not None:
	analytics_integration = "MLFlow"
	if self.share_url is not None:
	mlflow.log_param("Gradio Interface Share Link", self.share_url)
	else:
	mlflow.log_param("Gradio Interface Local Link", self.local_url)
	if self.analytics_enabled and analytics_integration:
	data = {"integration": analytics_integration}
	utils.integration_analytics(data)

	def close(self, verbose: bool = True) -> None:
	"""
	Closes the Interface that was launched and frees the port.
	"""
	try:
	if self.enable_queue:
	self._queue.close()
	self.server.close()
	self.is_running = False
	# So that the startup events (starting the queue)
	# happen the next time the app is launched
	self.app.startup_events_triggered = False
	if verbose:
	print("Closing server running on port: {}".format(self.server_port))
	except (AttributeError, OSError): # can't close if not running
	pass

	def block_thread(
	self,
	) -> None:
	"""Block main thread until interrupted by user."""
	try:
	while True:
	time.sleep(0.1)
	except (KeyboardInterrupt, OSError):
	print("Keyboard interruption in main thread... closing server.")
	self.server.close()
	for tunnel in CURRENT_TUNNELS:
	tunnel.kill()

	def attach_load_events(self):
	"""Add a load event for every component whose initial value should be randomized."""
	if Context.root_block:
	for component in Context.root_block.blocks.values():
	if (
	isinstance(component, components.IOComponent)
	and component.load_event_to_attach
	):
	load_fn, every = component.load_event_to_attach
	# Use set_event_trigger to avoid ambiguity between load class/instance method
	dep = self.set_event_trigger(
	"load",
	load_fn,
	None,
	component,
	no_target=True,
	# If every is None, for sure skip the queue
	# else, let the enable_queue parameter take precedence
	# this will raise a nice error message is every is used
	# without queue
	queue=False if every is None else None,
	every=every,
	)[0]
	component.load_event = dep

	def startup_events(self):
	"""Events that should be run when the app containing this block starts up."""

	if self.enable_queue:
	utils.run_coro_in_background(self._queue.start, (self.progress_tracking,))
	# So that processing can resume in case the queue was stopped
	self._queue.stopped = False
	utils.run_coro_in_background(self.create_limiter)

	def queue_enabled_for_fn(self, fn_index: int):
	if self.dependencies[fn_index]["queue"] is None:
	return self.enable_queue
	return self.dependencies[fn_index]["queue"]