from typing import List, Dict, Any, Optional
import json
import re
from src.domain.models.conversation import ConversationContext, ConversationTurn, QueryClassification
from src.application.services.conversation_manager import ConversationManager
from src.infrastructure.providers.llm_provider import LLMClient
from config import settings
from config.conversation_config import conversation_config

class HistoryQueryHandler:
    """Handles queries that can be answered from conversation history."""
    
    def __init__(self, conversation_manager: ConversationManager):
        self.conversation_manager = conversation_manager
        self.llm_client = LLMClient()
    
    async def handle_history_query(self, user_message: str, session_id: str, 
                                 classification: QueryClassification) -> str:
        """Handle a query that references conversation history."""
        
        conversation = self.conversation_manager.get_or_create_conversation(session_id)
        
        # Find relevant historical context
        relevant_context = await self._find_relevant_context(user_message, conversation, classification)
        
        if not relevant_context:
            return await self._handle_no_context(user_message)
        
        # Generate response using LLM with historical context
        return await self._generate_history_response(user_message, relevant_context)
    
    async def _find_relevant_context(self, user_message: str, conversation: ConversationContext, 
                                   classification: QueryClassification) -> Optional[Dict[str, Any]]:
        """Find relevant context from conversation history."""
        
        context = {
            "relevant_turns": [],
            "entity_contexts": {},
            "recent_summary": "",
            "confidence": classification.confidence
        }
        
        # 1. Search for relevant turns based on query content
        max_turns = conversation_config.history_handler.max_turns_in_prompt
        relevant_turns = self.conversation_manager.search_conversation_history(
            conversation.session_id, user_message, limit=max_turns
        )
        context["relevant_turns"] = relevant_turns
        
        # 2. Get context for referenced entities
        for entity_ref in classification.referenced_entities:
            entity_context = self._find_entity_context(entity_ref, conversation)
            if entity_context:
                context["entity_contexts"][entity_ref] = entity_context
        
        # 3. Handle specific reference patterns
        context.update(await self._handle_specific_references(user_message, conversation))
        
        # 4. Get recent conversation summary
        max_summary_turns = conversation_config.memory.max_context_summary_turns
        context["recent_summary"] = conversation.get_context_summary(max_turns=max_summary_turns)
        
        return context if (relevant_turns or context["entity_contexts"] or context.get("specific_data")) else None
    
    def _find_entity_context(self, entity_ref: str, conversation: ConversationContext) -> Optional[Dict[str, Any]]:
        """Find context for a specific entity reference."""
        
        # Try to find entity by different patterns
        entity_patterns = [
            ("user_handle", r'\b(user|member|handle)\b'),
            ("challenge_id", r'\b(challenge|contest)\b'),
            ("skill_name", r'\b(skill|technology)\b'),
        ]
        
        for entity_type, pattern in entity_patterns:
            if re.search(pattern, entity_ref, re.IGNORECASE):
                entity = conversation.find_entity(entity_type)
                if entity:
                    context_str = self.conversation_manager.get_context_for_entity(
                        conversation.session_id, entity_type, entity.name
                    )
                    return {
                        "entity": entity,
                        "context": context_str,
                        "type": entity_type
                    }
        
        return None
    
    async def _handle_specific_references(self, user_message: str, conversation: ConversationContext) -> Dict[str, Any]:
        """Handle specific reference patterns in the user message."""
        result = {}
        message_lower = user_message.lower()
        
        # "Last/Previous" references
        if re.search(r'\b(last|previous|recent)\b', message_lower):
            max_recent = conversation_config.memory.max_recent_turns_for_context
            recent_turns = conversation.get_recent_turns(max_recent)
            tool_turns = [turn for turn in recent_turns if turn.tool_used]
            
            if tool_turns:
                last_tool_turn = tool_turns[-1]
                result["specific_data"] = {
                    "type": "last_result",
                    "turn": last_tool_turn,
                    "description": f"Last {last_tool_turn.tool_used} result"
                }
        
        # "That/It" references  
        elif re.search(r'\b(that|it|this)\b', message_lower):
            # Use smaller window for direct references
            recent_turns = conversation.get_recent_turns(2)
            if recent_turns:
                last_turn = recent_turns[-1]
                result["specific_data"] = {
                    "type": "reference",
                    "turn": last_turn,
                    "description": "Referenced item from recent conversation"
                }
        
        # Specific count/number questions
        elif re.search(r'\b(how many|count|total|number)\b', message_lower):
            # Look for turns with list results
            for turn in reversed(conversation.turns):
                if turn.tool_used and "query" in turn.tool_used:
                    # Try to extract count information
                    count_info = self._extract_count_from_turn(turn)
                    if count_info:
                        result["specific_data"] = {
                            "type": "count",
                            "turn": turn,
                            "count_info": count_info,
                            "description": f"Count information from {turn.tool_used}"
                        }
                        break
        
        return result
    
    def _extract_count_from_turn(self, turn: ConversationTurn) -> Optional[Dict[str, Any]]:
        """Extract count information from a conversation turn."""
        if not turn.full_response:
            return None
        
        try:
            # Try to parse as JSON to get list length
            if turn.full_response.startswith('[') or turn.full_response.startswith('{'):
                data = json.loads(turn.full_response)
                if isinstance(data, list):
                    return {"count": len(data), "items": "results"}
                elif isinstance(data, dict) and "result" in data:
                    result = data["result"]
                    if isinstance(result, list):
                        return {"count": len(result), "items": "results"}
        except json.JSONDecodeError:
            pass
        
        # Try to extract count from response summary
        count_match = re.search(r'(\d+)\s*(challenges?|members?|skills?|results?)', 
                               turn.response_summary, re.IGNORECASE)
        if count_match:
            return {
                "count": int(count_match.group(1)),
                "items": count_match.group(2)
            }
        
        return None
    
    async def _generate_history_response(self, user_message: str, context: Dict[str, Any]) -> str:
        """Generate a response using LLM with historical context."""
        
        if not settings.HF_TOKEN:
            return self._generate_simple_history_response(context)
        
        # Prepare context for LLM
        context_prompt = self._build_context_prompt(user_message, context)
        
        try:
            messages = [
                {
                    "role": "system",
                    "content": "You are a helpful Topcoder assistant. Answer the user's question using only the provided conversation history and context. Be conversational and helpful."
                },
                {
                    "role": "user",
                    "content": context_prompt
                }
            ]
            
            response = await self.llm_client.chat(messages)
            return response
            
        except Exception as e:
            print(f"LLM history response failed: {e}")
            return self._generate_simple_history_response(context)
    
    def _build_context_prompt(self, user_message: str, context: Dict[str, Any]) -> str:
        """Build a prompt for the LLM with historical context."""
        
        prompt_parts = [
            f"USER'S QUESTION: {user_message}",
            "",
            "CONVERSATION HISTORY:"
        ]
        
        # Add relevant turns
        if context["relevant_turns"]:
            for i, turn in enumerate(context["relevant_turns"], 1):
                prompt_parts.append(f"Turn {i}:")
                prompt_parts.append(f"  User asked: {turn.user_message}")
                if turn.tool_used:
                    prompt_parts.append(f"  Tool used: {turn.tool_used}")
                    if turn.tool_params:
                        prompt_parts.append(f"  Parameters: {json.dumps(turn.tool_params)}")
                    prompt_parts.append(f"  Result: {turn.response_summary}")
                else:
                    max_chars = conversation_config.history_handler.max_response_chars_for_display
                    prompt_parts.append(f"  Response: {turn.full_response[:max_chars]}")
                prompt_parts.append("")
        
        # Add entity contexts
        if context["entity_contexts"]:
            prompt_parts.append("ENTITY CONTEXT:")
            for entity_ref, entity_context in context["entity_contexts"].items():
                prompt_parts.append(f"  {entity_ref}: {entity_context['context']}")
            prompt_parts.append("")
        
        # Add specific data
        if context.get("specific_data"):
            specific = context["specific_data"]
            prompt_parts.append("SPECIFIC REFERENCE:")
            prompt_parts.append(f"  Type: {specific['type']}")
            prompt_parts.append(f"  Description: {specific['description']}")
            
            if specific["type"] == "count" and "count_info" in specific:
                count_info = specific["count_info"]
                prompt_parts.append(f"  Count: {count_info['count']} {count_info['items']}")
            elif "turn" in specific:
                turn = specific["turn"]
                prompt_parts.append(f"  From: {turn.user_message}")
                prompt_parts.append(f"  Result: {turn.response_summary}")
            prompt_parts.append("")
        
        prompt_parts.extend([
            "INSTRUCTIONS:",
            "- Answer the user's question using only the information above",
            "- Be conversational and helpful",
            "- If the question can't be fully answered from the history, say so",
            "- Reference specific results when appropriate",
            "- If asking about counts or numbers, provide the specific count if available"
        ])
        
        return "\n".join(prompt_parts)
    
    def _generate_simple_history_response(self, context: Dict[str, Any]) -> str:
        """Generate a simple response without LLM when no token available."""
        
        if context.get("specific_data"):
            specific = context["specific_data"]
            
            if specific["type"] == "count" and "count_info" in specific:
                count_info = specific["count_info"]
                return f"Based on our previous search, there were {count_info['count']} {count_info['items']} found."
            
            elif specific["type"] == "last_result":
                turn = specific["turn"]
                return f"In our last {turn.tool_used} search: {turn.response_summary}"
            
            elif specific["type"] == "reference":
                turn = specific["turn"]
                if turn.tool_used:
                    return f"From the previous {turn.tool_used} result: {turn.response_summary}"
                else:
                    return turn.full_response[:300] + "..." if len(turn.full_response) > 300 else turn.full_response
        
        elif context["relevant_turns"]:
            last_relevant = context["relevant_turns"][-1]
            if last_relevant.tool_used:
                return f"From our previous {last_relevant.tool_used} search: {last_relevant.response_summary}"
            else:
                max_chars = conversation_config.history_handler.max_response_chars_for_display
                return f"As discussed earlier: {last_relevant.full_response[:max_chars]}..."
        
        return conversation_config.history_handler.no_context_message
    
    async def _handle_no_context(self, user_message: str) -> str:
        """Handle cases where no relevant context is found."""
        
        # Check if this might be a misclassified query
        if any(word in user_message.lower() for word in ['new', 'different', 'other', 'more']):
            return conversation_config.history_handler.misclassified_query_message
        
        return conversation_config.history_handler.no_context_message