"""
Router Agent - Intent recognition and routing
"""
import json
from typing import Any, Optional

from core.state import AgentState, Intent, ConversationState, set_intent, add_entity, set_language
from core.llm import get_llm_client, Message
from core.language_detector import get_cached_or_detect
from prompts import get_prompt
from utils.logger import get_logger
from utils.faq_library import get_faq_library

logger = get_logger(__name__)


async def classify_intent(state: AgentState) -> AgentState:
    """Classify user intent and extract entities

    This is the first node in the workflow that analyzes the user's message
    and determines which agent should handle it.

    Args:
        state: Current agent state

    Returns:
        Updated state with intent and entities
    """
    logger.info(
        "Classifying intent",
        conversation_id=state["conversation_id"],
        message=state["current_message"][:100]
    )

    state["state"] = ConversationState.CLASSIFYING.value
    state["step_count"] += 1

    # ========== FAST PATH: Check FAQ first BEFORE calling LLM ==========
    # This avoids slow LLM calls for common questions
    import re
    clean_message = re.sub(r'<[^>]+>', '', state["current_message"])
    clean_message = ' '.join(clean_message.split())

    faq_library = get_faq_library()
    faq_response = faq_library.find_match(clean_message)

    if faq_response:
        logger.info(
            "FAQ matched at router level, skipping LLM classification",
            conversation_id=state["conversation_id"],
            message=clean_message[:50]
        )
        # Set to customer service intent and store FAQ response
        state["intent"] = Intent.CUSTOMER_SERVICE.value
        state["intent_confidence"] = 1.0  # High confidence for FAQ matches
        state["faq_response"] = faq_response  # Store FAQ response for later use
        return state
    # ==============================================================

    # Detect language
    detected_locale = get_cached_or_detect(state, state["current_message"])
    confidence = 0.85  # Default confidence for language detection
    state = set_language(state, detected_locale, confidence)

    logger.info(
        "Language detected",
        locale=detected_locale,
        confidence=confidence
    )

    # Build context from conversation history
    context_summary = ""
    if state["context"]:
        context_parts = []
        if state["context"].get("order_id"):
            context_parts.append(f"Current order: {state['context']['order_id']}")
        if state["context"].get("product_id"):
            context_parts.append(f"Current product: {state['context']['product_id']}")
        if context_parts:
            context_summary = "\n".join(context_parts)

    # Load prompt in detected language
    classification_prompt = get_prompt("router", detected_locale)

    # Build messages for LLM
    messages = [
        Message(role="system", content=classification_prompt),
    ]
    
    # Add recent conversation history for context
    for msg in state["messages"][-6:]:  # Last 3 turns
        messages.append(Message(role=msg["role"], content=msg["content"]))
    
    # Add current message with context
    user_content = f"User message: {state['current_message']}"
    if context_summary:
        user_content += f"\n\nCurrent context:\n{context_summary}"
    
    messages.append(Message(role="user", content=user_content))
    
    try:
        llm = get_llm_client()
        response = await llm.chat(messages, temperature=0.3)
        
        # Parse JSON response
        content = response.content.strip()

        # Log raw response for debugging
        logger.info(
            "LLM response for intent classification",
            response_preview=content[:500] if content else "EMPTY",
            content_length=len(content) if content else 0
        )
        
        # Handle markdown code blocks
        if content.startswith("```"):
            content = content.split("```")[1]
            if content.startswith("json"):
                content = content[4:]
        
        # Check for empty response
        if not content:
            logger.warning("LLM returned empty response for intent classification")
            state["intent"] = Intent.CUSTOMER_SERVICE.value  # Default to customer service
            state["intent_confidence"] = 0.5
            return state
        
        result = json.loads(content)
        
        # Extract intent
        intent_str = result.get("intent", "unknown")
        try:
            intent = Intent(intent_str)
        except ValueError:
            intent = Intent.UNKNOWN
        
        confidence = float(result.get("confidence", 0.5))
        sub_intent = result.get("sub_intent")
        
        # Set intent in state
        state = set_intent(state, intent, confidence, sub_intent)
        
        # Extract entities
        entities = result.get("entities", {})
        for entity_type, entity_value in entities.items():
            if entity_value:
                state = add_entity(state, entity_type, entity_value)
        
        logger.info(
            "Intent classified",
            intent=intent.value,
            confidence=confidence,
            entities=list(entities.keys())
        )
        
        # Check if human handoff is needed
        if intent == Intent.HUMAN_HANDOFF or confidence < 0.5:
            state["requires_human"] = True
            state["handoff_reason"] = result.get("reasoning", "Intent unclear")
        
        return state
        
    except json.JSONDecodeError as e:
        logger.error("Failed to parse intent response", error=str(e))
        state["intent"] = Intent.UNKNOWN.value
        state["intent_confidence"] = 0.0
        return state
        
    except Exception as e:
        logger.error("Intent classification failed", error=str(e))
        state["error"] = str(e)
        state["intent"] = Intent.UNKNOWN.value
        return state


def route_by_intent(state: AgentState) -> str:
    """Route to appropriate agent based on intent
    
    This is the routing function used by conditional edges in the graph.
    
    Args:
        state: Current agent state
        
    Returns:
        Name of the next node to execute
    """
    intent = state.get("intent")
    requires_human = state.get("requires_human", False)
    
    # Human handoff takes priority
    if requires_human:
        return "human_handoff"
    
    # Route based on intent
    routing_map = {
        Intent.CUSTOMER_SERVICE.value: "customer_service_agent",
        Intent.ORDER.value: "order_agent",
        Intent.AFTERSALE.value: "aftersale_agent",
        Intent.PRODUCT.value: "product_agent",
        Intent.HUMAN_HANDOFF.value: "human_handoff",
        Intent.UNKNOWN.value: "customer_service_agent"  # Default to customer service
    }
    
    return routing_map.get(intent, "customer_service_agent")