"""The langchain module integrates Langchain support with Panel."""

from __future__ import annotations

import typing as t

try:
    from langchain.callbacks.base import BaseCallbackHandler
    from langchain.schema import AgentAction, AgentFinish, LLMResult
except ImportError:
    BaseCallbackHandler = object
    AgentAction = None
    AgentFinish = None
    LLMResult = None

from ..chat.message import DEFAULT_AVATARS
from ..layout import Accordion
from ..util.warnings import deprecated

if t.TYPE_CHECKING:
    from ..chat.feed import ChatFeed
    from ..chat.interface import ChatInterface


class PanelCallbackHandler(BaseCallbackHandler):
    """
    The Langchain `PanelCallbackHandler` itself is not a widget or pane, but is useful for rendering
    and streaming the *chain of thought* from Langchain Tools, Agents, and Chains
    as `ChatMessage` objects.

    Reference: https://panel.holoviz.org/reference/chat/PanelCallbackHandler.html

    :Example:

    >>> chat_interface = pn.widgets.ChatInterface(callback=callback, callback_user="Langchain")
    >>> callback_handler = pn.widgets.langchain.PanelCallbackHandler(instance=chat_interface)
    >>> llm = ChatOpenAI(streaming=True, callbacks=[callback_handler])
    >>> chain = ConversationChain(llm=llm)

    """

    def __init__(
        self,
        instance: ChatFeed | ChatInterface,
        user: str = "LangChain",
        avatar: str = DEFAULT_AVATARS["langchain"],
    ):
        if BaseCallbackHandler is object:
            raise ImportError(
                "LangChainCallbackHandler requires `langchain` to be installed."
            )
        deprecated(
            '1.10.0', 'panel.chat.langchain.PanelCallbackHandler',
            warn_version='1.8.0'
        )
        self.instance = instance
        self._message = None
        self._active_user = user
        self._active_avatar = avatar
        self._disabled_state = self.instance.disabled
        self._is_streaming = None

        self._input_user = user  # original user
        self._input_avatar = avatar

    def _update_active(self, avatar: str, label: str):
        """
        Prevent duplicate labels from being appended to the same user.
        """
        # not a typo; Langchain passes a string :/
        if label == "None":
            return

        self._active_avatar = avatar
        if f"- {label}" not in self._active_user:
            self._active_user = f"{self._active_user} - {label}"

    def _reset_active(self):
        self._active_user = self._input_user
        self._active_avatar = self._input_avatar
        self._message = None

    def _on_start(self, serialized, kwargs):
        model = kwargs.get("invocation_params", {}).get("model_name", "")
        self._is_streaming = serialized.get("kwargs", {}).get("streaming")
        messages = self.instance.objects
        if messages[-1].user != self._active_user:
            self._message = None
        if self._active_user and model not in self._active_user:
            self._active_user = f"{self._active_user} ({model})"

    def _stream(self, message: str):
        if message:
            return self.instance.stream(
                message,
                user=self._active_user,
                avatar=self._active_avatar,
                message=self._message,
            )
        return self._message

    def on_llm_start(self, serialized: dict[str, t.Any], *args, **kwargs):
        self._on_start(serialized, kwargs)
        return super().on_llm_start(serialized, *args, **kwargs)

    def on_llm_new_token(self, token: str, **kwargs) -> None:
        self._message = self._stream(token)
        return super().on_llm_new_token(token, **kwargs)

    def on_llm_end(self, response: LLMResult, *args, **kwargs):
        if not self._is_streaming:
            # on_llm_new_token does not get called if not streaming
            self._stream(response.generations[0][0].text)

        self._reset_active()
        return super().on_llm_end(response, *args, **kwargs)

    def on_llm_error(self, error: Exception | KeyboardInterrupt, *args, **kwargs):
        return super().on_llm_error(error, *args, **kwargs)

    def on_agent_action(self, action: AgentAction, *args, **kwargs: t.Any) -> t.Any:
        return super().on_agent_action(action, *args, **kwargs)

    def on_agent_finish(self, finish: AgentFinish, *args, **kwargs: t.Any) -> t.Any:
        return super().on_agent_finish(finish, *args, **kwargs)

    def on_tool_start(
        self, serialized: dict[str, t.Any], input_str: str, *args, **kwargs
    ):
        self._update_active(DEFAULT_AVATARS["tool"], serialized["name"])
        self._stream(f"Tool input: {input_str}")
        return super().on_tool_start(serialized, input_str, *args, **kwargs)

    def on_tool_end(self, output: str, *args, **kwargs):
        self._stream(output)
        self._reset_active()
        return super().on_tool_end(output, *args, **kwargs)

    def on_tool_error(
        self, error: Exception | KeyboardInterrupt, *args, **kwargs
    ):
        return super().on_tool_error(error, *args, **kwargs)

    def on_chain_start(
        self, serialized: dict[str, t.Any], inputs: dict[str, t.Any], *args, **kwargs
    ):
        self._disabled_state = self.instance.disabled
        self.instance.disabled = True
        return super().on_chain_start(serialized, inputs, *args, **kwargs)

    def on_chain_end(self, outputs: dict[str, t.Any], *args, **kwargs):
        self.instance.disabled = self._disabled_state
        return super().on_chain_end(outputs, *args, **kwargs)

    def on_retriever_error(
        self, error: Exception | KeyboardInterrupt, **kwargs: t.Any
    ) -> t.Any:
        """Run when Retriever errors."""
        return super().on_retriever_error(error, **kwargs)

    def on_retriever_end(self, documents, **kwargs: t.Any) -> t.Any:
        """Run when Retriever ends running."""
        objects = [(f"Document {index}", document.page_content) for index, document in enumerate(documents)]
        message = Accordion(*objects, sizing_mode="stretch_width", margin=(10,13,10,5))
        self.instance.send(
            message,
            user="LangChain (retriever)",
            avatar=DEFAULT_AVATARS["retriever"],
            respond=False,
        )
        return super().on_retriever_end(documents=documents, **kwargs)

    def on_text(self, text: str, **kwargs: t.Any):
        """Run when text is received."""
        return super().on_text(text, **kwargs)

    def on_chat_model_start(
        self,
        serialized: dict[str, t.Any],
        messages: list,
        **kwargs: t.Any
    ) -> None:
        """
        To prevent the inherited class from raising
        NotImplementedError, will not call super() here.
        """
        self._on_start(serialized, kwargs)
