gradio-env/Lib/site-packages/gradio/components/chatbot.pyi

"""gr.Chatbot() component."""

from __future__ import annotations

import inspect
from dataclasses import dataclass, field
from pathlib import Path
from typing import (
    TYPE_CHECKING,
    Any,
    Callable,
    Dict,
    List,
    Literal,
    Optional,
    Sequence,
    Tuple,
    Type,
    TypedDict,
    Union,
    cast,
)

from gradio_client import utils as client_utils
from gradio_client.documentation import document
from pydantic import Field
from typing_extensions import NotRequired

from gradio import utils
from gradio.component_meta import ComponentMeta
from gradio.components import (
    Component as GradioComponent,
)
from gradio.components.base import Component
from gradio.data_classes import FileData, GradioModel, GradioRootModel
from gradio.events import Events
from gradio.exceptions import Error
from gradio.processing_utils import move_resource_to_block_cache


class MetadataDict(TypedDict):
    title: Union[str, None]


class FileDataDict(TypedDict):
    path: str  # server filepath
    url: NotRequired[Optional[str]]  # normalised server url
    size: NotRequired[Optional[int]]  # size in bytes
    orig_name: NotRequired[Optional[str]]  # original filename
    mime_type: NotRequired[Optional[str]]
    is_stream: NotRequired[bool]
    meta: dict[Literal["_type"], Literal["gradio.FileData"]]


class MessageDict(TypedDict):
    content: str | FileDataDict | tuple | Component
    role: Literal["user", "assistant", "system"]
    metadata: NotRequired[MetadataDict]


class FileMessage(GradioModel):
    file: FileData
    alt_text: Optional[str] = None


class ComponentMessage(GradioModel):
    component: str
    value: Any
    constructor_args: Dict[str, Any]
    props: Dict[str, Any]


class ChatbotDataTuples(GradioRootModel):
    root: List[
        Tuple[
            Union[str, FileMessage, ComponentMessage, None],
            Union[str, FileMessage, ComponentMessage, None],
        ]
    ]


class Metadata(GradioModel):
    title: Optional[str] = None


class Message(GradioModel):
    role: str
    metadata: Metadata = Field(default_factory=Metadata)
    content: Union[str, FileMessage, ComponentMessage]


@dataclass
class ChatMessage:
    role: Literal["user", "assistant", "system"]
    content: str | FileData | Component | FileDataDict | tuple | list
    metadata: MetadataDict | Metadata = field(default_factory=Metadata)


class ChatbotDataMessages(GradioRootModel):
    root: List[Message]


TupleFormat = List[List[Union[str, Tuple[str], Tuple[str, str], None]]]

if TYPE_CHECKING:
    from gradio.components import Timer


def import_component_and_data(
    component_name: str,
) -> GradioComponent | ComponentMeta | Any | None:
    try:
        for component in utils.get_all_components():
            if component_name == component.__name__ and isinstance(
                component, ComponentMeta
            ):
                return component
    except ModuleNotFoundError as e:
        raise ValueError(f"Error importing {component_name}: {e}") from e
    except AttributeError:
        pass

from gradio.events import Dependency

@document()
class Chatbot(Component):
    """
    Creates a chatbot that displays user-submitted messages and responses. Supports a subset of Markdown including bold, italics, code, tables.
    Also supports audio/video/image files, which are displayed in the Chatbot, and other kinds of files which are displayed as links. This
    component is usually used as an output component.

    Demos: chatbot_simple, chatbot_core_components_simple
    Guides: creating-a-chatbot
    """

    EVENTS = [Events.change, Events.select, Events.like]

    def __init__(
        self,
        value: (
            Sequence[
                Sequence[
                    str | GradioComponent | tuple[str] | tuple[str | Path, str] | None
                ]
            ]
            | Callable
            | None
        ) = None,
        *,
        type: Literal["messages", "tuples"] = "tuples",
        label: str | None = None,
        every: Timer | float | None = None,
        inputs: Component | Sequence[Component] | set[Component] | None = None,
        show_label: bool | None = None,
        container: bool = True,
        scale: int | None = None,
        min_width: int = 160,
        visible: bool = True,
        elem_id: str | None = None,
        elem_classes: list[str] | str | None = None,
        render: bool = True,
        key: int | str | None = None,
        height: int | str | None = None,
        latex_delimiters: list[dict[str, str | bool]] | None = None,
        rtl: bool = False,
        show_share_button: bool | None = None,
        show_copy_button: bool = False,
        avatar_images: tuple[str | Path | None, str | Path | None] | None = None,
        sanitize_html: bool = True,
        render_markdown: bool = True,
        bubble_full_width: bool = True,
        line_breaks: bool = True,
        likeable: bool = False,
        layout: Literal["panel", "bubble"] | None = None,
        placeholder: str | None = None,
        show_copy_all_button=False,
    ):
        """
        Parameters:
            value: Default value to show in chatbot. If callable, the function will be called whenever the app loads to set the initial value of the component.
            type: The format of the messages. If 'tuples', expects a `list[list[str | None | tuple]]`, i.e. a list of lists. The inner list should have 2 elements: the user message and the response message. The individual messages can be (1) strings in valid Markdown, (2) tuples if sending files: (a filepath or URL to a file, [optional string alt text]) -- if the file is image/video/audio, it is displayed in the Chatbot, or (3) None, in which case the message is not displayed. If 'messages', passes the value as a list of dictionaries with 'role' and 'content' keys. The `content' key's value supports everything the 'tuples' format supports. The 'role' key should be one of 'user' or 'assistant'. Any other roles will not be displayed in the output.
            label: The label for this component. Appears above the component and is also used as the header if there are a table of examples for this component. If None and used in a `gr.Interface`, the label will be the name of the parameter this component is assigned to.
            every: Continously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer.
            inputs: Components that are used as inputs to calculate `value` if `value` is a function (has no effect otherwise). `value` is recalculated any time the inputs change.
            show_label: if True, will display label.
            container: If True, will place the component in a container - providing some extra padding around the border.
            scale: relative size compared to adjacent Components. For example if Components A and B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide as B. Should be an integer. scale applies in Rows, and to top-level Components in Blocks where fill_height=True.
            min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
            visible: If False, component will be hidden.
            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
            elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
            render: If False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.
            key: if assigned, will be used to assume identity across a re-render. Components that have the same key across a re-render will have their value preserved.
            height: The height of the component, specified in pixels if a number is passed, or in CSS units if a string is passed.
            latex_delimiters: A list of dicts of the form {"left": open delimiter (str), "right": close delimiter (str), "display": whether to display in newline (bool)} that will be used to render LaTeX expressions. If not provided, `latex_delimiters` is set to `[{ "left": "$$", "right": "$$", "display": True }]`, so only expressions enclosed in $$ delimiters will be rendered as LaTeX, and in a new line. Pass in an empty list to disable LaTeX rendering. For more information, see the [KaTeX documentation](https://katex.org/docs/autorender.html).
            rtl: If True, sets the direction of the rendered text to right-to-left. Default is False, which renders text left-to-right.
            show_share_button: If True, will show a share icon in the corner of the component that allows user to share outputs to Hugging Face Spaces Discussions. If False, icon does not appear. If set to None (default behavior), then the icon appears if this Gradio app is launched on Spaces, but not otherwise.
            show_copy_button: If True, will show a copy button for each chatbot message.
            avatar_images: Tuple of two avatar image paths or URLs for user and bot (in that order). Pass None for either the user or bot image to skip. Must be within the working directory of the Gradio app or an external URL.
            sanitize_html: If False, will disable HTML sanitization for chatbot messages. This is not recommended, as it can lead to security vulnerabilities.
            render_markdown: If False, will disable Markdown rendering for chatbot messages.
            bubble_full_width: If False, the chat bubble will fit to the content of the message. If True (default), the chat bubble will be the full width of the component.
            line_breaks: If True (default), will enable Github-flavored Markdown line breaks in chatbot messages. If False, single new lines will be ignored. Only applies if `render_markdown` is True.
            likeable: Whether the chat messages display a like or dislike button. Set automatically by the .like method but has to be present in the signature for it to show up in the config.
            layout: If "panel", will display the chatbot in a llm style layout. If "bubble", will display the chatbot with message bubbles, with the user and bot messages on alterating sides. Will default to "bubble".
            placeholder: a placeholder message to display in the chatbot when it is empty. Centered vertically and horizontally in the Chatbot. Supports Markdown and HTML. If None, no placeholder is displayed.
            show_copy_all_button: If True, will show a copy all button that copies all chatbot messages to the clipboard.
        """
        self.likeable = likeable
        if type not in ["messages", "tuples"]:
            raise ValueError("type must be 'messages' or 'tuples', received: {type}")
        self.type: Literal["tuples", "messages"] = type
        if type == "messages":
            self.data_model = ChatbotDataMessages
        else:
            self.data_model = ChatbotDataTuples
        self.height = height
        self.rtl = rtl
        if latex_delimiters is None:
            latex_delimiters = [{"left": "$$", "right": "$$", "display": True}]
        self.latex_delimiters = latex_delimiters
        self.show_share_button = (
            (utils.get_space() is not None)
            if show_share_button is None
            else show_share_button
        )
        self.render_markdown = render_markdown
        self.show_copy_button = show_copy_button
        self.sanitize_html = sanitize_html
        self.bubble_full_width = bubble_full_width
        self.line_breaks = line_breaks
        self.layout = layout
        self.show_copy_all_button = show_copy_all_button
        super().__init__(
            label=label,
            every=every,
            inputs=inputs,
            show_label=show_label,
            container=container,
            scale=scale,
            min_width=min_width,
            visible=visible,
            elem_id=elem_id,
            elem_classes=elem_classes,
            render=render,
            key=key,
            value=value,
        )
        self.avatar_images: list[dict | None] = [None, None]
        if avatar_images is None:
            pass
        else:
            self.avatar_images = [
                self.serve_static_file(avatar_images[0]),
                self.serve_static_file(avatar_images[1]),
            ]
        self.placeholder = placeholder

    @staticmethod
    def _check_format(messages: list[Any], type: Literal["messages", "tuples"]):
        if type == "messages":
            all_valid = all(
                isinstance(message, dict)
                and "role" in message
                and "content" in message
                or isinstance(message, ChatMessage)
                for message in messages
            )
            if not all_valid:
                raise Error(
                    "Data incompatible with messages format. Each message should be a dictionary with 'role' and 'content' keys or a ChatMessage object."
                )
        elif not all(
            isinstance(message, (tuple, list)) and len(message) == 2
            for message in messages
        ):
            raise Error(
                "Data incompatible with tuples format. Each message should be a list of length 2."
            )

    def _preprocess_content(
        self,
        chat_message: str | FileMessage | ComponentMessage | None,
    ) -> str | GradioComponent | tuple[str | None] | tuple[str | None, str] | None:
        if chat_message is None:
            return None
        elif isinstance(chat_message, FileMessage):
            if chat_message.alt_text is not None:
                return (chat_message.file.path, chat_message.alt_text)
            else:
                return (chat_message.file.path,)
        elif isinstance(chat_message, str):
            return chat_message
        elif isinstance(chat_message, ComponentMessage):
            capitalized_component = (
                chat_message.component.upper()
                if chat_message.component in ("json", "html")
                else chat_message.component.capitalize()
            )
            component = import_component_and_data(capitalized_component)
            if component is not None:
                instance = component()  # type: ignore
                if not instance.data_model:
                    payload = chat_message.value
                elif issubclass(instance.data_model, GradioModel):
                    payload = instance.data_model(**chat_message.value)
                elif issubclass(instance.data_model, GradioRootModel):
                    payload = instance.data_model(root=chat_message.value)
                else:
                    payload = chat_message.value
                value = instance.preprocess(payload)
                return component(value=value, **chat_message.constructor_args)  # type: ignore
            else:
                raise ValueError(
                    f"Invalid component for Chatbot component: {chat_message.component}"
                )
        else:
            raise ValueError(f"Invalid message for Chatbot component: {chat_message}")

    def _preprocess_messages_tuples(
        self, payload: ChatbotDataTuples
    ) -> list[list[str | tuple[str] | tuple[str, str] | None]]:
        processed_messages = []
        for message_pair in payload.root:
            if not isinstance(message_pair, (tuple, list)):
                raise TypeError(
                    f"Expected a list of lists or list of tuples. Received: {message_pair}"
                )
            if len(message_pair) != 2:
                raise TypeError(
                    f"Expected a list of lists of length 2 or list of tuples of length 2. Received: {message_pair}"
                )
            processed_messages.append(
                [
                    self._preprocess_content(message_pair[0]),
                    self._preprocess_content(message_pair[1]),
                ]
            )
        return processed_messages

    def preprocess(
        self,
        payload: ChatbotDataTuples | ChatbotDataMessages | None,
    ) -> (
        list[list[str | tuple[str] | tuple[str, str] | None]] | list[MessageDict] | None
    ):
        """
        Parameters:
            payload: data as a ChatbotData object
        Returns:
            If type is 'tuples', passes the messages in the chatbot as a `list[list[str | None | tuple]]`, i.e. a list of lists. The inner list has 2 elements: the user message and the response message. Each message can be (1) a string in valid Markdown, (2) a tuple if there are displayed files: (a filepath or URL to a file, [optional string alt text]), or (3) None, if there is no message displayed. If type is 'messages', passes the value as a list of dictionaries with 'role' and 'content' keys. The `content` key's value supports everything the `tuples` format supports.
        """
        if payload is None:
            return payload
        if self.type == "tuples":
            if not isinstance(payload, ChatbotDataTuples):
                raise Error("Data incompatible with the tuples format")
            return self._preprocess_messages_tuples(cast(ChatbotDataTuples, payload))
        if not isinstance(payload, ChatbotDataMessages):
            raise Error("Data incompatible with the messages format")
        message_dicts = []
        for message in payload.root:
            message_dict = cast(MessageDict, message.model_dump())
            message_dict["content"] = self._preprocess_content(message.content)
            message_dicts.append(message_dict)
        return message_dicts

    @staticmethod
    def _get_alt_text(chat_message: dict | list | tuple | GradioComponent):
        if isinstance(chat_message, dict):
            return chat_message.get("alt_text")
        elif not isinstance(chat_message, GradioComponent) and len(chat_message) > 1:
            return chat_message[1]

    @staticmethod
    def _create_file_message(chat_message, filepath):
        mime_type = client_utils.get_mimetype(filepath)

        return FileMessage(
            file=FileData(path=filepath, mime_type=mime_type),
            alt_text=Chatbot._get_alt_text(chat_message),
        )

    def _postprocess_content(
        self,
        chat_message: str
        | tuple
        | list
        | FileDataDict
        | FileData
        | GradioComponent
        | None,
    ) -> str | FileMessage | ComponentMessage | None:
        if chat_message is None:
            return None
        elif isinstance(chat_message, FileMessage):
            return chat_message
        elif isinstance(chat_message, FileData):
            return FileMessage(file=chat_message)
        elif isinstance(chat_message, GradioComponent):
            component = import_component_and_data(type(chat_message).__name__)
            if component:
                component = chat_message.__class__(**chat_message.constructor_args)
                chat_message.constructor_args.pop("value", None)
                config = component.get_config()
                return ComponentMessage(
                    component=type(chat_message).__name__.lower(),
                    value=config.get("value", None),
                    constructor_args=chat_message.constructor_args,
                    props=config,
                )
        elif isinstance(chat_message, dict) and "path" in chat_message:
            filepath = chat_message["path"]
            return self._create_file_message(chat_message, filepath)
        elif isinstance(chat_message, (tuple, list)):
            filepath = str(chat_message[0])
            return self._create_file_message(chat_message, filepath)
        elif isinstance(chat_message, str):
            chat_message = inspect.cleandoc(chat_message)
            return chat_message
        else:
            raise ValueError(f"Invalid message for Chatbot component: {chat_message}")

    def _postprocess_messages_tuples(self, value: TupleFormat) -> ChatbotDataTuples:
        processed_messages = []
        for message_pair in value:
            processed_messages.append(
                [
                    self._postprocess_content(message_pair[0]),
                    self._postprocess_content(message_pair[1]),
                ]
            )
        return ChatbotDataTuples(root=processed_messages)

    def _postprocess_message_messages(
        self, message: MessageDict | ChatMessage
    ) -> list[Message]:
        if isinstance(message, dict):
            message["content"] = self._postprocess_content(message["content"])
            msg = Message(**message)  # type: ignore
        elif isinstance(message, ChatMessage):
            message.content = self._postprocess_content(message.content)  # type: ignore
            msg = Message(
                role=message.role,
                content=message.content,  # type: ignore
                metadata=message.metadata,  # type: ignore
            )
        else:
            raise Error(
                f"Invalid message for Chatbot component: {message}", visible=False
            )

        # extract file path from message
        new_messages = []
        if isinstance(msg.content, str):
            for word in msg.content.split(" "):
                filepath = Path(word)
                try:
                    is_file = filepath.is_file() and filepath.exists()
                except OSError:
                    is_file = False
                if is_file:
                    filepath = cast(
                        str, move_resource_to_block_cache(filepath, block=self)
                    )
                    mime_type = client_utils.get_mimetype(filepath)
                    new_messages.append(
                        Message(
                            role=msg.role,
                            metadata=msg.metadata,
                            content=FileMessage(
                                file=FileData(path=filepath, mime_type=mime_type)
                            ),
                        ),
                    )
        return [msg, *new_messages]

    def postprocess(
        self,
        value: TupleFormat | list[MessageDict | Message] | None,
    ) -> ChatbotDataTuples | ChatbotDataMessages:
        """
        Parameters:
            value: If type is `tuples`, expects a `list[list[str | None | tuple]]`, i.e. a list of lists. The inner list should have 2 elements: the user message and the response message. The individual messages can be (1) strings in valid Markdown, (2) tuples if sending files: (a filepath or URL to a file, [optional string alt text]) -- if the file is image/video/audio, it is displayed in the Chatbot, or (3) None, in which case the message is not displayed. If type is 'messages', passes the value as a list of dictionaries with 'role' and 'content' keys. The `content` key's value supports everything the `tuples` format supports.
        Returns:
            an object of type ChatbotData
        """
        data_model = cast(
            Union[Type[ChatbotDataTuples], Type[ChatbotDataMessages]], self.data_model
        )
        if value is None:
            return data_model(root=[])
        if self.type == "tuples":
            self._check_format(value, "tuples")
            return self._postprocess_messages_tuples(cast(TupleFormat, value))
        self._check_format(value, "messages")
        processed_messages = [
            msg
            for message in value
            for msg in self._postprocess_message_messages(cast(MessageDict, message))
        ]
        return ChatbotDataMessages(root=processed_messages)

    def example_payload(self) -> Any:
        if self.type == "messages":
            return [
                Message(role="user", content="Hello!").model_dump(),
                Message(role="assistant", content="How can I help you?").model_dump(),
            ]
        return [["Hello!", None]]

    def example_value(self) -> Any:
        if self.type == "messages":
            return [
                Message(role="user", content="Hello!").model_dump(),
                Message(role="assistant", content="How can I help you?").model_dump(),
            ]
        return [["Hello!", None]]
    from typing import Callable, Literal, Sequence, Any, TYPE_CHECKING
    from gradio.blocks import Block
    if TYPE_CHECKING:
        from gradio.components import Timer

    
    def change(self,
        fn: Callable[..., Any] | None = None,
        inputs: Block | Sequence[Block] | set[Block] | None = None,
        outputs: Block | Sequence[Block] | None = None,
        api_name: str | None | Literal[False] = None,
        scroll_to_output: bool = False,
        show_progress: Literal["full", "minimal", "hidden"] = "full",
        queue: bool | None = None,
        batch: bool = False,
        max_batch_size: int = 4,
        preprocess: bool = True,
        postprocess: bool = True,
        cancels: dict[str, Any] | list[dict[str, Any]] | None = None,
        every: Timer | float | None = None,
        trigger_mode: Literal["once", "multiple", "always_last"] | None = None,
        js: str | None = None,
        concurrency_limit: int | None | Literal["default"] = "default",
        concurrency_id: str | None = None,
        show_api: bool = True) -> Dependency:
        """
        Parameters:
            fn: the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
            inputs: list of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
            outputs: list of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
            api_name: defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
            scroll_to_output: if True, will scroll to output component on completion
            show_progress: how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all
            queue: if True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
            batch: if True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
            max_batch_size: maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
            preprocess: if False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
            postprocess: if False, will not run postprocessing of component data before returning 'fn' output to the browser.
            cancels: a list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
            every: continously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer.
            trigger_mode: if "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete.
            js: optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components.
            concurrency_limit: if set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default).
            concurrency_id: if set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit.
            show_api: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False.
        """
        ...
    
    def select(self,
        fn: Callable[..., Any] | None = None,
        inputs: Block | Sequence[Block] | set[Block] | None = None,
        outputs: Block | Sequence[Block] | None = None,
        api_name: str | None | Literal[False] = None,
        scroll_to_output: bool = False,
        show_progress: Literal["full", "minimal", "hidden"] = "full",
        queue: bool | None = None,
        batch: bool = False,
        max_batch_size: int = 4,
        preprocess: bool = True,
        postprocess: bool = True,
        cancels: dict[str, Any] | list[dict[str, Any]] | None = None,
        every: Timer | float | None = None,
        trigger_mode: Literal["once", "multiple", "always_last"] | None = None,
        js: str | None = None,
        concurrency_limit: int | None | Literal["default"] = "default",
        concurrency_id: str | None = None,
        show_api: bool = True) -> Dependency:
        """
        Parameters:
            fn: the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
            inputs: list of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
            outputs: list of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
            api_name: defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
            scroll_to_output: if True, will scroll to output component on completion
            show_progress: how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all
            queue: if True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
            batch: if True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
            max_batch_size: maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
            preprocess: if False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
            postprocess: if False, will not run postprocessing of component data before returning 'fn' output to the browser.
            cancels: a list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
            every: continously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer.
            trigger_mode: if "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete.
            js: optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components.
            concurrency_limit: if set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default).
            concurrency_id: if set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit.
            show_api: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False.
        """
        ...
    
    def like(self,
        fn: Callable[..., Any] | None = None,
        inputs: Block | Sequence[Block] | set[Block] | None = None,
        outputs: Block | Sequence[Block] | None = None,
        api_name: str | None | Literal[False] = None,
        scroll_to_output: bool = False,
        show_progress: Literal["full", "minimal", "hidden"] = "full",
        queue: bool | None = None,
        batch: bool = False,
        max_batch_size: int = 4,
        preprocess: bool = True,
        postprocess: bool = True,
        cancels: dict[str, Any] | list[dict[str, Any]] | None = None,
        every: Timer | float | None = None,
        trigger_mode: Literal["once", "multiple", "always_last"] | None = None,
        js: str | None = None,
        concurrency_limit: int | None | Literal["default"] = "default",
        concurrency_id: str | None = None,
        show_api: bool = True) -> Dependency:
        """
        Parameters:
            fn: the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
            inputs: list of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
            outputs: list of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
            api_name: defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
            scroll_to_output: if True, will scroll to output component on completion
            show_progress: how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all
            queue: if True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
            batch: if True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
            max_batch_size: maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
            preprocess: if False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
            postprocess: if False, will not run postprocessing of component data before returning 'fn' output to the browser.
            cancels: a list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
            every: continously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer.
            trigger_mode: if "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete.
            js: optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components.
            concurrency_limit: if set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default).
            concurrency_id: if set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit.
            show_api: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False.
        """
        ...