"""Middleware for intercepting clarification requests and presenting them to the user."""

from collections.abc import Callable
from typing import override

from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langchain_core.messages import ToolMessage
from langgraph.graph import END
from langgraph.prebuilt.tool_node import ToolCallRequest
from langgraph.types import Command


class ClarificationMiddlewareState(AgentState):
    """Compatible with the `ThreadState` schema."""

    pass


class ClarificationMiddleware(AgentMiddleware[ClarificationMiddlewareState]):
    """Intercepts clarification tool calls and interrupts execution to present questions to the user.

    When the model calls the `ask_clarification` tool, this middleware:
    1. Intercepts the tool call before execution
    2. Extracts the clarification question and metadata
    3. Formats a user-friendly message
    4. Returns a Command that interrupts execution and presents the question
    5. Waits for user response before continuing

    This replaces the tool-based approach where clarification continued the conversation flow.
    """

    state_schema = ClarificationMiddlewareState

    def _is_chinese(self, text: str) -> bool:
        """Check if text contains Chinese characters.

        Args:
            text: Text to check

        Returns:
            True if text contains Chinese characters
        """
        return any("\u4e00" <= char <= "\u9fff" for char in text)

    def _format_clarification_message(self, args: dict) -> str:
        """Format the clarification arguments into a user-friendly message.

        Args:
            args: The tool call arguments containing clarification details

        Returns:
            Formatted message string
        """
        question = args.get("question", "")
        clarification_type = args.get("clarification_type", "missing_info")
        context = args.get("context")
        options = args.get("options", [])

        # Type-specific icons
        type_icons = {
            "missing_info": "❓",
            "ambiguous_requirement": "🤔",
            "approach_choice": "🔀",
            "risk_confirmation": "⚠️",
            "suggestion": "💡",
        }

        icon = type_icons.get(clarification_type, "❓")

        # Build the message naturally
        message_parts = []

        # Add icon and question together for a more natural flow
        if context:
            # If there's context, present it first as background
            message_parts.append(f"{icon} {context}")
            message_parts.append(f"\n{question}")
        else:
            # Just the question with icon
            message_parts.append(f"{icon} {question}")

        # Add options in a cleaner format
        if options and len(options) > 0:
            message_parts.append("")  # blank line for spacing
            for i, option in enumerate(options, 1):
                message_parts.append(f"  {i}. {option}")

        return "\n".join(message_parts)

    def _handle_clarification(self, request: ToolCallRequest) -> Command:
        """Handle clarification request and return command to interrupt execution.

        Args:
            request: Tool call request

        Returns:
            Command that interrupts execution with the formatted clarification message
        """
        # Extract clarification arguments
        args = request.tool_call.get("args", {})
        question = args.get("question", "")

        print("[ClarificationMiddleware] Intercepted clarification request")
        print(f"[ClarificationMiddleware] Question: {question}")

        # Format the clarification message
        formatted_message = self._format_clarification_message(args)

        # Get the tool call ID
        tool_call_id = request.tool_call.get("id", "")

        # Create a ToolMessage with the formatted question
        # This will be added to the message history
        tool_message = ToolMessage(
            content=formatted_message,
            tool_call_id=tool_call_id,
            name="ask_clarification",
        )

        # Return a Command that:
        # 1. Adds the formatted tool message
        # 2. Interrupts execution by going to __end__
        # Note: We don't add an extra AIMessage here - the frontend will detect
        # and display ask_clarification tool messages directly
        return Command(
            update={"messages": [tool_message]},
            goto=END,
        )

    @override
    def wrap_tool_call(
        self,
        request: ToolCallRequest,
        handler: Callable[[ToolCallRequest], ToolMessage | Command],
    ) -> ToolMessage | Command:
        """Intercept ask_clarification tool calls and interrupt execution (sync version).

        Args:
            request: Tool call request
            handler: Original tool execution handler

        Returns:
            Command that interrupts execution with the formatted clarification message
        """
        # Check if this is an ask_clarification tool call
        if request.tool_call.get("name") != "ask_clarification":
            # Not a clarification call, execute normally
            return handler(request)

        return self._handle_clarification(request)

    @override
    async def awrap_tool_call(
        self,
        request: ToolCallRequest,
        handler: Callable[[ToolCallRequest], ToolMessage | Command],
    ) -> ToolMessage | Command:
        """Intercept ask_clarification tool calls and interrupt execution (async version).

        Args:
            request: Tool call request
            handler: Original tool execution handler (async)

        Returns:
            Command that interrupts execution with the formatted clarification message
        """
        # Check if this is an ask_clarification tool call
        if request.tool_call.get("name") != "ask_clarification":
            # Not a clarification call, execute normally
            return await handler(request)

        return self._handle_clarification(request)