blocks.py

from __future__ import annotations

import copy
import hashlib
import inspect
import json
import os
import random
import secrets
import sys
import threading
import time
import warnings
import webbrowser
from collections import defaultdict
from pathlib import Path
from types import ModuleType
from typing import TYPE_CHECKING, Any, AsyncIterator, Callable, Literal, Sequence, cast
from urllib.parse import urlparse, urlunparse

import anyio
import requests
from anyio import CapacityLimiter
from gradio_client import utils as client_utils
from gradio_client.documentation import document, set_documentation_group

from gradio import (
    analytics,
    components,
    networking,
    processing_utils,
    queueing,
    routes,
    strings,
    themes,
    utils,
    wasm_utils,
)
from gradio.blocks_events import BlocksEvents, BlocksMeta
from gradio.context import Context
from gradio.data_classes import FileData, GradioModel, GradioRootModel
from gradio.events import (
    EventData,
    EventListener,
    EventListenerMethod,
)
from gradio.exceptions import (
    DuplicateBlockError,
    InvalidApiNameError,
    InvalidBlockError,
    InvalidComponentError,
)
from gradio.helpers import create_tracker, skip, special_args
from gradio.state_holder import SessionState
from gradio.themes import Default as DefaultTheme
from gradio.themes import ThemeClass as Theme
from gradio.tunneling import (
    BINARY_FILENAME,
    BINARY_FOLDER,
    BINARY_PATH,
    BINARY_URL,
    CURRENT_TUNNELS,
)
from gradio.utils import (
    TupleNoPrint,
    check_function_inputs_match,
    component_or_layout_class,
    concurrency_count_warning,
    get_cancel_function,
    get_continuous_fn,
    get_package_version,
)

try:
    import spaces  # type: ignore
except Exception:
    spaces = None

set_documentation_group("blocks")

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

    from gradio.components.base import Component

BUILT_IN_THEMES: dict[str, Theme] = {
    t.name: t
    for t in [
        themes.Base(),
        themes.Default(),
        themes.Monochrome(),
        themes.Soft(),
        themes.Glass(),
    ]
}


class Block:
    def __init__(
        self,
        *,
        elem_id: str | None = None,
        elem_classes: list[str] | str | None = None,
        render: bool = True,
        visible: bool = True,
        proxy_url: str | None = None,
        _skip_init_processing: bool = False,
    ):
        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.proxy_url = proxy_url
        self.share_token = secrets.token_urlsafe(32)
        self._skip_init_processing = _skip_init_processing
        self.parent: BlockContext | None = None
        self.is_rendered: bool = False
        self._constructor_args: dict
        self.state_session_capacity = 10000

        if render:
            self.render()

    @property
    def skip_api(self):
        return False

    @property
    def constructor_args(self) -> dict[str, Any]:
        """Get the arguments passed to the component's initializer.

        Only set classes whose metaclass is ComponentMeta
        """
        # the _constructor_args list is appended based on the mro of the class
        # so the first entry is for the bottom of the hierarchy
        return self._constructor_args[0] if self._constructor_args else {}

    @property
    def events(
        self,
    ) -> list[EventListener]:
        return getattr(self, "EVENTS", [])

    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
            self.is_rendered = True
            if isinstance(self, components.Component):
                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]
                self.is_rendered = False
            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 get_config(self):
        config = {}
        signature = inspect.signature(self.__class__.__init__)
        for parameter in signature.parameters.values():
            if hasattr(self, parameter.name):
                value = getattr(self, parameter.name)
                config[parameter.name] = value
        for e in self.events:
            to_add = e.config_data()
            if to_add:
                config = {**to_add, **config}
        config.pop("render", None)
        config = {**config, "proxy_url": self.proxy_url, "name": self.get_block_name()}
        if (_selectable := getattr(self, "_selectable", None)) is not None:
            config["_selectable"] = _selectable
        return config

    @classmethod
    def recover_kwargs(
        cls, props: dict[str, Any], additional_keys: list[str] | None = None
    ):
        """
        Recovers kwargs from a dict of props.
        """
        additional_keys = additional_keys or []
        signature = inspect.signature(cls.__init__)
        kwargs = {}
        for parameter in signature.parameters.values():
            if parameter.name in props and parameter.name not in additional_keys:
                kwargs[parameter.name] = props[parameter.name]
        return kwargs


class BlockContext(Block):
    def __init__(
        self,
        elem_id: str | None = None,
        elem_classes: list[str] | str | None = None,
        visible: bool = True,
        render: bool = True,
    ):
        """
        Parameters:
            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
            elem_classes: An optional string or list of strings that are assigned as the class of this component in the HTML DOM. Can be used for targeting CSS styles.
            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,
            elem_id=elem_id,
            elem_classes=elem_classes,
            visible=visible,
            render=render,
        )

    TEMPLATE_DIR = "./templates/"
    FRONTEND_DIR = "../../frontend/"

    @property
    def skip_api(self):
        return True

    @classmethod
    def get_component_class_id(cls) -> str:
        module_name = cls.__module__
        module_path = sys.modules[module_name].__file__
        module_hash = hashlib.md5(f"{cls.__name__}_{module_path}".encode()).hexdigest()
        return module_hash

    @property
    def component_class_id(self):
        return self.get_component_class_id()

    def add_child(self, child: Block):
        self.children.append(child)

    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.add_child(child)
                else:
                    pseudo_parent = expected_parent(render=False)
                    pseudo_parent.parent = self
                    children.append(pseudo_parent)
                    pseudo_parent.add_child(child)
                    if Context.root_block:
                        Context.root_block.blocks[pseudo_parent._id] = pseudo_parent
                child.parent = pseudo_parent
        self.children = children

    def __exit__(self, exc_type: type[BaseException] | None = None, *args):
        Context.block = self.parent
        if exc_type is not None:
            return
        if getattr(self, "allow_expected_parents", True):
            self.fill_expected_parents()

    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
        self.spaces_auto_wrap()

    def spaces_auto_wrap(self):
        if spaces is None:
            return
        if utils.get_space() is None:
            return
        self.fn = spaces.gradio_auto_wrap(self.fn)

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

    def __repr__(self):
        return str(self)


def postprocess_update_dict(
    block: Component | BlockContext, update_dict: dict, postprocess: bool = True
):
    """
    Converts a dictionary of updates into a format that can be sent to the frontend.
    E.g. {"__type__": "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("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"
    attr_dict = {
        k: getattr(block, k) if hasattr(block, k) else v for k, v in update_dict.items()
    }
    attr_dict["__type__"] = "update"
    attr_dict.pop("value", None)
    if "value" in update_dict:
        if not isinstance(block, components.Component):
            raise InvalidComponentError(
                f"Component {block.__class__} does not support value"
            )
        if postprocess:
            attr_dict["value"] = block.postprocess(update_dict["value"])
            if isinstance(attr_dict["value"], (GradioModel, GradioRootModel)):
                attr_dict["value"] = attr_dict["value"].model_dump()
        else:
            attr_dict["value"] = update_dict["value"]
    return attr_dict


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]
    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, BlocksEvents, metaclass=BlocksMeta):
    """
    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
    Guides: blocks-and-event-listeners, controlling-layout, state-in-blocks, custom-CSS-and-JS, 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:
            theme: a Theme object or a string representing a theme. If a string, will look for a built-in theme with that name (e.g. "soft" or "default"), or will attempt to load a theme from the HF Hub (e.g. "gradio/monochrome"). If None, will use the Default theme.
            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
        """
        self.limiter = None
        if theme is None:
            theme = DefaultTheme()
        elif isinstance(theme, str):
            if theme.lower() in BUILT_IN_THEMES:
                theme = BUILT_IN_THEMES[theme.lower()]
            else:
                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 = theme
        self.theme_css = theme._get_theme_css()
        self.stylesheets = theme._stylesheets
        self.encrypt = False
        self.share = False
        self.enable_queue = True
        self.max_threads = 40
        self.pending_streams = defaultdict(dict)
        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 analytics.analytics_enabled()
        )
        if self.analytics_enabled:
            if not wasm_utils.IS_WASM:
                t = threading.Thread(target=analytics.version_check)
                t.start()
        else:
            os.environ["HF_HUB_DISABLE_TELEMETRY"] = "True"
        super().__init__(render=False, **kwargs)
        self.blocks: dict[int, Component | 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 = utils.get_space() is None

        self.space_id = utils.get_space()
        self.favicon_path = None
        self.auth = None
        self.dev_mode = bool(os.getenv("GRADIO_WATCH_DIRS", False))
        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.ssl_verify = True

        self.allowed_paths = []
        self.blocked_paths = []
        self.root_path = os.environ.get("GRADIO_ROOT_PATH", "")
        self.proxy_urls = set()

        if self.analytics_enabled:
            is_custom_theme = not any(
                self.theme.to_dict() == built_in_theme.to_dict()
                for built_in_theme in BUILT_IN_THEMES.values()
            )
            data = {
                "mode": self.mode,
                "custom_css": self.css is not None,
                "theme": self.theme.name,
                "is_custom_theme": is_custom_theme,
                "version": get_package_version(),
            }
            analytics.initiated_analytics(data)

        self.queue()

    def get_component(self, id: int) -> Component:
        comp = self.blocks[id]
        assert isinstance(comp, components.Component), f"{comp}"
        return comp

    @property
    def _is_running_in_reload_thread(self):
        from gradio.cli.commands.reload import reload_thread

        return getattr(reload_thread, "running_reload", False)

    @classmethod
    def from_config(
        cls,
        config: dict,
        fns: list[Callable],
        proxy_url: str,
    ) -> Blocks:
        """
        Factory method that creates a Blocks from a config and list of functions. Used
        internally by the gradio.external.load() method.

        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.
        proxy_url: an external url to use as a root URL when serving files for components in the Blocks.
        """
        config = copy.deepcopy(config)
        components_config = config["components"]
        for component_config in components_config:
            # for backwards compatibility, extract style into props
            if "style" in component_config["props"]:
                component_config["props"].update(component_config["props"]["style"])
                del component_config["props"]["style"]
        theme = config.get("theme", "default")
        original_mapping: dict[int, Block] = {}
        proxy_urls = {proxy_url}

        def get_block_instance(id: int) -> Block:
            for block_config in components_config:
                if block_config["id"] == id:
                    break
            else:
                raise ValueError(f"Cannot find block with id {id}")
            cls = component_or_layout_class(block_config["type"])

            # If a Gradio app B is loaded into a Gradio app A, and B itself loads a
            # Gradio app C, then the proxy_urls of the components in A need to be the
            # URL of C, not B. The else clause below handles this case.
            if block_config["props"].get("proxy_url") is None:
                block_config["props"]["proxy_url"] = f"{proxy_url}/"

            constructor_args = cls.recover_kwargs(block_config["props"])
            block = cls(**constructor_args)

            block_proxy_url = block_config["props"]["proxy_url"]
            block.proxy_url = block_proxy_url
            proxy_urls.add(block_proxy_url)
            if (
                _selectable := block_config["props"].pop("_selectable", None)
            ) is not None:
                block._selectable = _selectable  # type: ignore
            # Any component has already processed its initial value, so we skip that step here
            block._skip_init_processing = True

            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:
                    if not isinstance(block, BlockContext):
                        raise ValueError(
                            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(theme=theme) 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.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 "trigger" in dependency and dependency["trigger"] == "fake_event":
                    continue
                for field in derived_fields:
                    dependency.pop(field, None)

                # older versions had a separate trigger field, but now it is part of the
                # targets field
                _targets = dependency.pop("targets")
                trigger = dependency.pop("trigger", None)
                targets = [
                    getattr(
                        original_mapping[
                            target if isinstance(target, int) else target[0]
                        ],
                        trigger if isinstance(target, int) else target[1],
                    )
                    for target in _targets
                ]
                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
                targets = [
                    EventListenerMethod(
                        t.__self__ if t.has_trigger else None, t.event_name
                    )
                    for t in targets
                ]
                dependency = blocks.set_event_trigger(
                    targets=targets, 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
        blocks.proxy_urls = proxy_urls
        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 += f"\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 += f"\n |-{block}"
                repr += "\n outputs:"
                for output_id in dependency["outputs"]:
                    block = self.blocks[output_id]
                    repr += f"\n |-{block}"
        return repr

    @property
    def expects_oauth(self):
        """Return whether the app expects user to authenticate via OAuth."""
        return any(
            isinstance(block, (components.LoginButton, components.LogoutButton))
            for block in self.blocks.values()
        )

    def set_event_trigger(
        self,
        targets: Sequence[EventListenerMethod],
        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: Literal["full", "minimal", "hidden"] | None = "full",
        api_name: str | None | Literal[False] = 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,
        trigger_mode: Literal["once", "multiple", "always_last"] | None = "once",
    ) -> tuple[dict[str, Any], int]:
        """
        Adds an event to the component's dependencies.
        Parameters:
            targets: a list of EventListenerMethod objects that define the event trigger
            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: defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
            js: Experimental parameter (API may change): 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
            queue: If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
            batch: 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)
            trigger_mode: If "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` event) would allow a second submission after the pending event is complete.
        Returns: dependency information, dependency index
        """
        # Support for singular parameter
        _targets = [
            (
                target.block._id if target.block and not no_target else None,
                target.event_name,
            )
            for target in targets
        ]
        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 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 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`.")

        if _targets[0][1] == "change" and trigger_mode is None:
            trigger_mode = "always_last"
        elif trigger_mode is None:
            trigger_mode = "once"
        elif trigger_mode not in ["once", "multiple", "always_last"]:
            raise ValueError(
                f"Invalid value for parameter `trigger_mode`: {trigger_mode}. Please choose from: {['once', 'multiple', 'always_last']}"
            )

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

        if collects_event_data is None:
            collects_event_data = event_data_index is not None

        dependency = {
            "targets": _targets,
            "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": False if utils.get_space() else 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,
            "trigger_mode": trigger_mode,
        }
        self.dependencies.append(dependency)
        return dependency, len(self.dependencies) - 1

    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."
                )
            overlapping_ids = set(Context.root_block.blocks).intersection(self.blocks)
            for id in overlapping_ids:
                # State components are allowed to be reused between Blocks
                if not isinstance(self.blocks[id], components.State):
                    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 and api_name is not False:
                    api_name_ = utils.append_unique_suffix(
                        api_name,
                        [dep["api_name"] for dep in Context.root_block.dependencies],
                    )
                    if api_name != api_name_:
                        warnings.warn(
                            f"api_name {api_name} already exists, using {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)
            Context.root_block.proxy_urls.update(self.proxy_urls)

        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 InvalidApiNameError(
                    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 = client_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]

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

        return processed_outputs

    async def call_function(
        self,
        fn_index: int,
        processed_input: list[Any],
        iterator: AsyncIterator[Any] | None = None,
        requests: routes.Request | list[routes.Request] | None = None,
        event_id: str | None = None,
        event_data: EventData | None = None,
        in_event_listener: bool = False,
    ):
        """
        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]
        if not block_fn.fn:
            raise IndexError(f"function with index {fn_index} not defined.")
        is_generating = False
        request = requests[0] if isinstance(requests, list) else requests
        start = time.time()

        fn = utils.get_function_with_locals(
            fn=block_fn.fn,
            blocks=self,
            event_id=event_id,
            in_event_listener=in_event_listener,
            request=request,
        )

        if iterator is None:  # If not a generator function that has already run
            if block_fn.inputs_as_dict:
                processed_input = [dict(zip(block_fn.inputs, processed_input))]

            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
            )

            if progress_tracker is not None and progress_index is not None:
                progress_tracker, fn = create_tracker(fn, progress_tracker.track_tqdm)
                processed_input[progress_index] = progress_tracker

            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.isgeneratorfunction(fn) or inspect.isasyncgenfunction(fn):
            try:
                if iterator is None:
                    iterator = cast(AsyncIterator[Any], prediction)
                if inspect.isgenerator(iterator):
                    iterator = utils.SyncToAsyncIterator(iterator, self.limiter)
                prediction = await utils.async_iteration(iterator)
                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 = []

        def format_file(s):
            return FileData(path=s).model_dump()

        for i, input_id in enumerate(dependency["inputs"]):
            try:
                block = self.blocks[input_id]
            except KeyError as e:
                raise InvalidBlockError(
                    f"Input component with id {input_id} used in {dependency['trigger']}() event is not defined in this gr.Blocks context. You are allowed to nest gr.Blocks contexts, but there must be a gr.Blocks context that contains all components and events."
                ) from e
            if not isinstance(block, components.Component):
                raise InvalidComponentError(
                    f"{block.__class__} Component with id {input_id} not a valid input component."
                )
            api_info = block.api_info()
            if client_utils.value_is_file(api_info):
                serialized_input = client_utils.traverse(
                    inputs[i],
                    format_file,
                    lambda s: client_utils.is_filepath(s) or client_utils.is_url(s),
                )
            else:
                serialized_input = 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"]):
            try:
                block = self.blocks[output_id]
            except KeyError as e:
                raise InvalidBlockError(
                    f"Output component with id {output_id} used in {dependency['trigger']}() event not found in this gr.Blocks context. You are allowed to nest gr.Blocks contexts, but there must be a gr.Blocks context that contains all components and events."
                ) from e
            if not isinstance(block, components.Component):
                raise InvalidComponentError(
                    f"{block.__class__} Component with id {output_id} not a valid output component."
                )

            deserialized = client_utils.traverse(
                outputs[o], lambda s: s["path"], client_utils.is_file_obj
            )
            predictions.append(deserialized)

        return predictions

    def validate_inputs(self, fn_index: int, inputs: list[Any]):
        block_fn = self.fns[fn_index]
        dependency = self.dependencies[fn_index]

        dep_inputs = dependency["inputs"]

        # This handles incorrect inputs when args are changed by a JS function
        # Only check not enough args case, ignore extra arguments (for now)
        # TODO: make this stricter?
        if len(inputs) < len(dep_inputs):
            name = (
                f" ({block_fn.name})"
                if block_fn.name and block_fn.name != "<lambda>"
                else ""
            )

            wanted_args = []
            received_args = []
            for input_id in dep_inputs:
                block = self.blocks[input_id]
                wanted_args.append(str(block))
            for inp in inputs:
                v = f'"{inp}"' if isinstance(inp, str) else str(inp)
                received_args.append(v)

            wanted = ", ".join(wanted_args)
            received = ", ".join(received_args)

            # JS func didn't pass enough arguments
            raise ValueError(
                f"""An event handler{name} didn't receive enough input values (needed: {len(dep_inputs)}, got: {len(inputs)}).
Check if the event handler calls a Javascript function, and make sure its return value is correct.
Wanted inputs:
    [{wanted}]
Received inputs:
    [{received}]"""
            )

    def preprocess_data(
        self, fn_index: int, inputs: list[Any], state: SessionState | None
    ):
        state = state or SessionState(self)
        block_fn = self.fns[fn_index]
        dependency = self.dependencies[fn_index]

        self.validate_inputs(fn_index, inputs)

        if block_fn.preprocess:
            processed_input = []
            for i, input_id in enumerate(dependency["inputs"]):
                try:
                    block = self.blocks[input_id]
                except KeyError as e:
                    raise InvalidBlockError(
                        f"Input component with id {input_id} used in {dependency['trigger']}() event not found in this gr.Blocks context. You are allowed to nest gr.Blocks contexts, but there must be a gr.Blocks context that contains all components and events."
                    ) from e
                if not isinstance(block, components.Component):
                    raise InvalidComponentError(
                        f"{block.__class__} Component with id {input_id} not a valid input component."
                    )
                if getattr(block, "stateful", False):
                    processed_input.append(state[input_id])
                else:
                    if input_id in state:
                        block = state[input_id]
                    inputs_cached = processing_utils.move_files_to_cache(
                        inputs[i], block
                    )
                    processed_input.append(block.preprocess(inputs_cached))
        else:
            processed_input = inputs
        return processed_input

    def validate_outputs(self, fn_index: int, predictions: Any | list[Any]):
        block_fn = self.fns[fn_index]
        dependency = self.dependencies[fn_index]

        dep_outputs = dependency["outputs"]

        if type(predictions) is not list and type(predictions) is not tuple:
            predictions = [predictions]

        if len(predictions) < len(dep_outputs):
            name = (
                f" ({block_fn.name})"
                if block_fn.name and block_fn.name != "<lambda>"
                else ""
            )

            wanted_args = []
            received_args = []
            for output_id in dep_outputs:
                block = self.blocks[output_id]
                wanted_args.append(str(block))
            for pred in predictions:
                v = f'"{pred}"' if isinstance(pred, str) else str(pred)
                received_args.append(v)

            wanted = ", ".join(wanted_args)
            received = ", ".join(received_args)

            raise ValueError(
                f"""An event handler{name} didn't receive enough output values (needed: {len(dep_outputs)}, received: {len(predictions)}).
Wanted outputs:
    [{wanted}]
Received outputs:
    [{received}]"""
            )

    def postprocess_data(
        self, fn_index: int, predictions: list | dict, state: SessionState | None
    ):
        state = state or SessionState(self)
        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,
            ]

        self.validate_outputs(fn_index, predictions)  # type: ignore

        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) as err:
                raise ValueError(
                    "Number of output components does not match number "
                    f"of values returned from from function {block_fn.name}"
                ) from err

            try:
                block = self.blocks[output_id]
            except KeyError as e:
                raise InvalidBlockError(
                    f"Output component with id {output_id} used in {dependency['trigger']}() event not found in this gr.Blocks context. You are allowed to nest gr.Blocks contexts, but there must be a gr.Blocks context that contains all components and events."
                ) from e

            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
                ):  # if update is passed directly (deprecated), remove Nones
                    prediction_value = utils.delete_none(
                        prediction_value, skip_value=True
                    )

                if isinstance(prediction_value, Block):
                    prediction_value = prediction_value.constructor_args
                    prediction_value["__type__"] = "update"
                if utils.is_update(prediction_value):
                    if output_id in state:
                        args = state[output_id].constructor_args.copy()
                    else:
                        args = self.blocks[output_id].constructor_args.copy()
                    args.update(prediction_value)
                    args.pop("value", None)
                    args.pop("__type__")
                    args["render"] = False
                    state[output_id] = self.blocks[output_id].__class__(**args)

                    prediction_value = postprocess_update_dict(
                        block=state[output_id],
                        update_dict=prediction_value,
                        postprocess=block_fn.postprocess,
                    )
                elif block_fn.postprocess:
                    if not isinstance(block, components.Component):
                        raise InvalidComponentError(
                            f"{block.__class__} Component with id {output_id} not a valid output component."
                        )
                    prediction_value = block.postprocess(prediction_value)
                outputs_cached = processing_utils.move_files_to_cache(prediction_value, block)  # type: ignore
                output.append(outputs_cached)

        return output

    def handle_streaming_outputs(
        self,
        fn_index: int,
        data: list,
        session_hash: str | None,
        run: int | None,
    ) -> list:
        if session_hash is None or run is None:
            return data
        if run not in self.pending_streams[session_hash]:
            self.pending_streams[session_hash][run] = {}
        stream_run = self.pending_streams[session_hash][run]

        for i, output_id in enumerate(self.dependencies[fn_index]["outputs"]):
            block = self.blocks[output_id]
            if isinstance(block, components.StreamingOutput) and block.streaming:
                first_chunk = output_id not in stream_run
                binary_data, output_data = block.stream_output(
                    data[i], f"{session_hash}/{run}/{output_id}", first_chunk
                )
                if first_chunk:
                    stream_run[output_id] = []
                self.pending_streams[session_hash][run][output_id].append(binary_data)
                data[i] = output_data
        return data

    async def process_api(
        self,
        fn_index: int,
        inputs: list[Any],
        state: SessionState | None = None,
        request: routes.Request | list[routes.Request] | None = None,
        iterator: AsyncIterator | None = None,
        session_hash: str | None = None,
        event_id: str | None = None,
        event_data: EventData | None = None,
        in_event_listener: bool = True,
    ) -> 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
            state: data stored from stateful components for session (key is input block id)
            request: the gr.Request object containing information about the network request (e.g. IP address, headers, query parameters, username)
            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,
                in_event_listener,
            )
            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:
            old_iterator = iterator
            if old_iterator:
                inputs = []
            else:
                inputs = self.preprocess_data(fn_index, inputs, state)
            was_generating = old_iterator is not None
            result = await self.call_function(
                fn_index,
                inputs,
                old_iterator,
                request,
                event_id,
                event_data,
                in_event_listener,
            )
            data = self.postprocess_data(fn_index, result["prediction"], state)
            is_generating, iterator = result["is_generating"], result["iterator"]
            if is_generating or was_generating:
                data = self.handle_streaming_outputs(
                    fn_index,
                    data,
                    session_hash=session_hash,
                    run=id(old_iterator) if was_generating else id(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,
        }

    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,
            "app_id": self.app_id,
            "dev_mode": self.dev_mode,
            "analytics_enabled": self.analytics_enabled,
            "components": [],
            "css": self.css,
            "title": self.title or "Gradio",
            "space_id": self.space_id,
            "enable_queue": True,  # launch attributes
            "show_error": getattr(self, "show_error", False),
            "show_api": self.show_api,
            "is_colab": utils.colab_check(),
            "stylesheets": self.stylesheets,
            "theme": self.theme.name,
            "protocol": "sse",
        }

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

        config["layout"] = get_layout(self)

        for _id, block in self.blocks.items():
            props = block.get_config() if hasattr(block, "get_config") else {}
            block_config = {
                "id": _id,
                "type": block.get_block_name(),
                "props": utils.delete_none(props),
            }
            block_config["skip_api"] = block.skip_api
            block_config["component_class_id"] = getattr(
                block, "component_class_id", None
            )

            if not block.skip_api:
                block_config["api_info"] = block.api_info()  # type: ignore
                block_config["example_inputs"] = block.example_inputs()  # type: ignore
            config["components"].append(block_config)
        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
        self.exited = False
        return self

    def __exit__(self, exc_type: type[BaseException] | None = None, *args):
        if exc_type is not None:
            Context.block = None
            Context.root_block = None
            return
        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)
        self.exited = True

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

    @concurrency_count_warning
    @document()
    def queue(
        self,
        concurrency_count: int | None = None,
        status_update_rate: float | Literal["auto"] = "auto",
        api_open: bool | None = None,
        max_size: int | None = None,
    ):
        """
        By enabling the queue you can control the rate of processed requests, let users know their position in the queue, and set a limit on maximum number of events allowed.
        Parameters:
            concurrency_count: Number of worker threads that will be processing requests from the queue concurrently. Default is 40 when running locally, and 1 in Spaces.
            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.
            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(max_size=10)
            demo.launch()
        Example: (Interface)
            demo = gr.Interface(image_generator, gr.Textbox(), gr.Image())
            demo.queue(max_size=20)
            demo.launch()
        """
        if concurrency_count is None:
            concurrency_count = 1 if utils.get_space() else 40
        if api_open is not None:
            self.api_open = api_open
        if utils.is_zero_gpu_space():
            concurrency_count = self.max_threads
            max_size = 1 if max_size is None else max_size
        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 validate_queue_settings(self):
        for dep in self.dependencies:
            for i in dep["cancels"]:
                if not self.queue_enabled_for_fn(i):
                    raise ValueError(
                        "Queue needs to 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:
                raise ValueError("In order to use batching, the queue must be enabled.")

    def launch(
        self,
        inline: bool | None = None,
        inbrowser: bool = False,
        share: bool | None = None,
        debug: bool = False,
        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,
        *,
        height: int = 500,
        width: int | str = "100%",
        favicon_path: str | None = None,
        ssl_keyfile: str | None = None,
        ssl_certfile: str | None = None,
        ssl_keyfile_password: str | None = None,
        ssl_verify: bool = True,
        quiet: bool = False,
        show_api: bool = True,
        allowed_paths: list[str] | None = None,
        blocked_paths: list[str] | None = None,
        root_path: str | None = None,
        app_kwargs: dict[str, Any] | None = None,
        state_session_capacity: int = 10000,
        share_server_address: str | None = None,
        share_server_protocol: Literal["http", "https"] | 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".
            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)
            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.
            ssl_verify: If False, skips certificate validation which allows self-signed certificates to be used.
            quiet: If True, suppresses most print statements.
            show_api: If True, shows the api docs in the footer of the app. Default True.
            allowed_paths: List of complete filepaths or parent directories that gradio is allowed to serve (in addition to the directory containing the gradio python file). Must be absolute paths. Warning: if you provide directories, any files in these directories or their subdirectories are accessible to all users of your app.
            blocked_paths: List of complete filepaths or parent directories that gradio is not allowed to serve (i.e. users of your app are not allowed to access). Must be absolute paths. Warning: takes precedence over `allowed_paths` and all other directories exposed by Gradio by default.
            root_path: The root path (or "mount point") of the application, if it's not served from the root ("/") of the domain. Often used when the application is behind a reverse proxy that forwards requests to the application. For example, if the application is served at "https://example.com/myapp", the `root_path` should be set to "/myapp". Can be set by environment variable GRADIO_ROOT_PATH. Defaults to "".
            app_kwargs: Additional keyword arguments to pass to the underlying FastAPI app as a dictionary of parameter keys and argument values. For example, `{"docs_url": "/docs"}`
            state_session_capacity: The maximum number of sessions whose information to store in memory. If the number of sessions exceeds this number, the oldest sessions will be removed. Reduce capacity to reduce memory usage when using gradio.State or returning updated components from functions. Defaults to 10000.
            share_server_address: Use this to specify a custom FRP server and port for sharing Gradio apps (only applies if share=True). If not provided, will use the default FRP server at https://gradio.live. See https://github.com/huggingface/frp for more information.
            share_server_protocol: Use this to specify the protocol to use for the share links. Defaults to "https", unless a custom share_server_address is provided, in which case it defaults to "http". If you are using a custom share_server_address and want to use https, you must set this to "https".
        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"))
        """
        if self._is_running_in_reload_thread:
            # We have already launched the demo
            return None, None, None  # type: ignore

        if not self.exited:
            self.__exit__()

        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_error = show_error
        self.height = height
        self.width = width
        self.favicon_path = favicon_path
        self.ssl_verify = ssl_verify
        self.state_session_capacity = state_session_capacity
        if root_path is None:
            self.root_path = os.environ.get("GRADIO_ROOT_PATH", "")
        else:
            self.root_path = root_path

        self.show_api = show_api

        self.allowed_paths = allowed_paths or []
        self.blocked_paths = blocked_paths or []

        if not isinstance(self.allowed_paths, list):
            raise ValueError("`allowed_paths` must be a list of directories.")
        if not isinstance(self.blocked_paths, list):
            raise ValueError("`blocked_paths` must be a list of directories.")

        self.validate_queue_settings()

        self.config = self.get_config_file()
        self.max_threads = max(self._queue.max_thread_count, max_threads)

        if self.is_running:
            if not isinstance(self.local_url, str):
                raise ValueError(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:
            if wasm_utils.IS_WASM:
                server_name = "xxx"
                server_port = 99999
                local_url = ""
                server = None

                # In the Wasm environment, we only need the app object
                # which the frontend app will directly communicate with through the Worker API,
                # and we don't need to start a server.
                # So we just create the app object and register it here,
                # and avoid using `networking.start_server` that would start a server that don't work in the Wasm env.
                from gradio.routes import App

                app = App.create_app(self, app_kwargs=app_kwargs)
                wasm_utils.register_app(app)
            else:
                (
                    server_name,
                    server_port,
                    local_url,
                    app,
                    server,
                ) = networking.start_server(
                    self,
                    server_name,
                    server_port,
                    ssl_keyfile,
                    ssl_certfile,
                    ssl_keyfile_password,
                    app_kwargs=app_kwargs,
                )
            self.server_name = server_name
            self.local_url = local_url
            self.server_port = server_port
            self.server_app = (
                self.app
            ) = app  # server_app is included for backwards compatibility
            self.server = server
            self.is_running = True
            self.is_colab = utils.colab_check()
            self.is_kaggle = utils.kaggle_check()
            self.share_server_address = share_server_address
            self.share_server_protocol = share_server_protocol or (
                "http" if share_server_address is not None else "https"
            )

            self.protocol = (
                "https"
                if self.local_url.startswith("https") or self.is_colab
                else "http"
            )
            if not wasm_utils.IS_WASM and not self.is_colab:
                print(
                    strings.en["RUNNING_LOCALLY_SEPARATED"].format(
                        self.protocol, self.server_name, self.server_port
                    )
                )

            self._queue.set_server_app(self.server_app)

            if not wasm_utils.IS_WASM:
                # 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", verify=ssl_verify)
            else:
                # NOTE: One benefit of the code above dispatching `startup_events()` via a self HTTP request is
                # that `self._queue.start()` is called in another thread which is managed by the HTTP server, `uvicorn`
                # so all the asyncio tasks created by the queue runs in an event loop in that thread and
                # will be cancelled just by stopping the server.
                # In contrast, in the Wasm env, we can't do that because `threading` is not supported and all async tasks will run in the same event loop, `pyodide.webloop.WebLoop` in the main thread.
                # So we need to manually cancel them. See `self.close()`..
                self.startup_events()

        utils.launch_counter()
        self.is_sagemaker = utils.sagemaker_check()
        if share is None:
            if self.is_colab:
                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 wasm_utils.IS_WASM
            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 or check your proxy settings to allow access to localhost."
            )

        if self.is_colab and 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.share:
            if self.space_id:
                warnings.warn(
                    "Setting share=True is not supported on Hugging Face Spaces"
                )
                self.share = False
            if wasm_utils.IS_WASM:
                warnings.warn(
                    "Setting share=True is not supported in the Wasm environment"
                )
                self.share = False

        if self.share:
            try:
                if self.share_url is None:
                    share_url = networking.setup_tunnel(
                        local_host=self.server_name,
                        local_port=self.server_port,
                        share_token=self.share_token,
                        share_server_address=self.share_server_address,
                    )
                    parsed_url = urlparse(share_url)
                    self.share_url = urlunparse(
                        (self.share_server_protocol,) + parsed_url[1:]
                    )
                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:
                    analytics.error_analytics("Not able to set up tunnel")
                self.share_url = None
                self.share = False
                if Path(BINARY_PATH).exists():
                    print(strings.en["COULD_NOT_GET_SHARE_LINK"])
                else:
                    print(
                        strings.en["COULD_NOT_GET_SHARE_LINK_MISSING_FILE"].format(
                            BINARY_PATH,
                            BINARY_URL,
                            BINARY_FILENAME,
                            BINARY_FOLDER,
                        )
                    )
        else:
            if not quiet and not wasm_utils.IS_WASM:
                print(strings.en["PUBLIC_SHARE_TRUE"])
            self.share_url = None

        if inbrowser and not wasm_utils.IS_WASM:
            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()
        if inline:
            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)
                    artifact = 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),
                    )

                    artifact = Javascript(code)
                else:
                    artifact = 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>'
                    )
                self.artifact = artifact
                display(artifact)
            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": True,
                "server_name": server_name,
                "server_port": server_port,
                "is_space": self.space_id is not None,
                "mode": self.mode,
            }
            analytics.launched_analytics(self, data)

        # Block main thread if debug==True
        if debug or int(os.getenv("GRADIO_DEBUG", 0)) == 1 and not wasm_utils.IS_WASM:
            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
            # In the Wasm env, we don't have to block the main thread because the server won't be shut down after the execution finishes.
            # Moreover, we MUST NOT do it because there is only one thread in the Wasm env and blocking it will stop the subsequent code from running.
            and not wasm_utils.IS_WASM
        ):
            self.block_thread()

        return TupleNoPrint((self.server_app, self.local_url, self.share_url))  # type: ignore

    def integrate(
        self,
        comet_ml=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(f"gradio: {self.share_url}")
                comet_ml.end()
            elif self.local_url:
                comet_ml.log_text(f"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}
            analytics.integration_analytics(data)

    def close(self, verbose: bool = True) -> None:
        """
        Closes the Interface that was launched and frees the port.
        """
        try:
            if wasm_utils.IS_WASM:
                # NOTE:
                # Normally, queue-related async tasks (e.g. continuous events created by `gr.Blocks.load(..., every=interval)`, whose async tasks are started at the `/queue/join` endpoint function)
                # are running in an event loop in the server thread,
                # so they will be cancelled by `self.server.close()` below.
                # However, in the Wasm env, we don't have the `server` and
                # all async tasks are running in the same event loop, `pyodide.webloop.WebLoop` in the main thread,
                # so we have to cancel them explicitly so that these tasks won't run after a new app is launched.
                self._queue._cancel_asyncio_tasks()
                self.server_app._cancel_asyncio_tasks()
            self._queue.close()
            if self.server:
                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(f"Closing server running on port: {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.")
            if self.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.Component)
                    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(
                        [EventListenerMethod(self, "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."""
        self._queue.start()
        # So that processing can resume in case the queue was stopped
        self._queue.stopped = False
        self.create_limiter()

    def queue_enabled_for_fn(self, fn_index: int):
        return self.dependencies[fn_index]["queue"] is not False

    def get_api_info(self):
        """
        Gets the information needed to generate the API docs from a Blocks.
        """
        config = self.config
        api_info = {"named_endpoints": {}, "unnamed_endpoints": {}}
        mode = config.get("mode", None)

        for d, dependency in enumerate(config["dependencies"]):
            dependency_info = {"parameters": [], "returns": []}
            skip_endpoint = False

            inputs = dependency["inputs"]
            for i in inputs:
                for component in config["components"]:
                    if component["id"] == i:
                        break
                else:
                    skip_endpoint = True  # if component not found, skip endpoint
                    break
                type = component["type"]
                if self.blocks[component["id"]].skip_api:
                    continue
                label = component["props"].get("label", f"parameter_{i}")
                # The config has the most specific API info (taking into account the parameters
                # of the component), so we use that if it exists. Otherwise, we fallback to the
                # Serializer's API info.
                info = self.get_component(component["id"]).api_info()
                example = self.get_component(component["id"]).example_inputs()
                python_type = client_utils.json_schema_to_python_type(info)
                dependency_info["parameters"].append(
                    {
                        "label": label,
                        "type": info,
                        "python_type": {
                            "type": python_type,
                            "description": info.get("description", ""),
                        },
                        "component": type.capitalize(),
                        "example_input": example,
                    }
                )

            outputs = dependency["outputs"]
            for o in outputs:
                for component in config["components"]:
                    if component["id"] == o:
                        break
                else:
                    skip_endpoint = True  # if component not found, skip endpoint
                    break
                type = component["type"]
                if self.blocks[component["id"]].skip_api:
                    continue
                label = component["props"].get("label", f"value_{o}")
                info = self.get_component(component["id"]).api_info()
                example = self.get_component(component["id"]).example_inputs()
                python_type = client_utils.json_schema_to_python_type(info)
                dependency_info["returns"].append(
                    {
                        "label": label,
                        "type": info,
                        "python_type": {
                            "type": python_type,
                            "description": info.get("description", ""),
                        },
                        "component": type.capitalize(),
                    }
                )

            if not dependency["backend_fn"]:
                skip_endpoint = True

            if skip_endpoint:
                continue
            if (
                dependency["api_name"] is not None
                and dependency["api_name"] is not False
            ):
                api_info["named_endpoints"][
                    f"/{dependency['api_name']}"
                ] = dependency_info
            elif (
                dependency["api_name"] is False
                or mode == "interface"
                or mode == "tabbed_interface"
            ):
                pass  # Skip unnamed endpoints in interface mode
            else:
                api_info["unnamed_endpoints"][str(d)] = dependency_info

        return api_info