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

from __future__ import annotations

import inspect
from pathlib import Path
from typing import Any, Callable, List, Literal, Optional, Tuple, Union

from gradio_client import utils as client_utils
from gradio_client.documentation import document, set_documentation_group

from gradio import processing_utils, utils
from gradio.components.base import Component
from gradio.data_classes import FileData, GradioModel, GradioRootModel
from gradio.events import Events

set_documentation_group("component")


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


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


@document()
class GPTChatbot(Component):
    """
    Displays a chatbot output showing both 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 GPTChatbot, and other kinds of files which are displayed as links.
    Preprocessing: passes the messages in the GPTChatbot 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. See `Postprocessing` for the format of these messages.
    Postprocessing: expects function to return 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 GPTChatbot, or (3) None, in which case the message is not displayed.

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

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

    def __init__(
        self,
        value: list[list[str | tuple[str] | tuple[str | Path, str] | None]]
        | Callable
        | None = None,
        *,
        label: str | None = None,
        every: float | 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,
        height: int | 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,
    ):
        """
        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.
            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: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
            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 width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer.
            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.
            height: height of the component in pixels.
            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".
        """
        self.likeable = likeable
        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
        super().__init__(
            label=label,
            every=every,
            show_label=show_label,
            container=container,
            scale=scale,
            min_width=min_width,
            visible=visible,
            elem_id=elem_id,
            elem_classes=elem_classes,
            render=render,
            value=value,
        )
        self.avatar_images: list[str | None] = [None, None]
        if avatar_images is None:
            pass
        else:
            self.avatar_images = [
                processing_utils.move_resource_to_block_cache(avatar_images[0], self),
                processing_utils.move_resource_to_block_cache(avatar_images[1], self),
            ]

    def _preprocess_chat_messages(
        self, chat_message: str | FileMessage | None
    ) -> str | 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
        else:
            raise ValueError(f"Invalid message for GPTChatbot component: {chat_message}")

    def preprocess(
        self,
        payload: ChatbotData,
    ) -> list[list[str | tuple[str] | tuple[str, str] | None]]:
        if payload is None:
            return payload
        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_chat_messages(message_pair[0]),
                    self._preprocess_chat_messages(message_pair[1]),
                ]
            )
        return processed_messages

    def _postprocess_chat_messages(
        self, chat_message: str | tuple | list | None
    ) -> str | FileMessage | None:
        if chat_message is None:
            return None
        elif isinstance(chat_message, (tuple, list)):
            filepath = str(chat_message[0])

            mime_type = client_utils.get_mimetype(filepath)
            return FileMessage(
                file=FileData(path=filepath, mime_type=mime_type),
                alt_text=chat_message[1] if len(chat_message) > 1 else None,
            )
        elif isinstance(chat_message, str):
            chat_message = inspect.cleandoc(chat_message)
            return chat_message
        else:
            raise ValueError(f"Invalid message for GPTChatbot component: {chat_message}")

    def postprocess(
        self,
        value: list[list[str | tuple[str] | tuple[str, str] | None] | tuple],
    ) -> ChatbotData:
        if value is None:
            return ChatbotData(root=[])
        processed_messages = []
        for message_pair in value:
            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._postprocess_chat_messages(message_pair[0]),
                    self._postprocess_chat_messages(message_pair[1]),
                ]
            )
        return ChatbotData(root=processed_messages)

    def example_inputs(self) -> Any:
        return [["Hello!", None]]
