import logging
import threading
from typing import List, Dict, Optional, Any

import numpy as np
from sentence_transformers import SentenceTransformer
from django.conf import settings
from django.utils import timezone
from pgvector.django import CosineDistance

from .models import GlobalQA, PropertyCustomQA, PropertyQA, AgencyAutoChatSetting, PropertyAutoChatState
from django.db.models.signals import post_save
from django.dispatch import receiver
logger = logging.getLogger(__name__)


class EmbeddingService:
    """
    FIX #7: Thread-safe singleton with per-thread model instances
    Prevents memory leak and handles Django's multi-threaded environment
    """
    _instances: Dict[int, 'EmbeddingService'] = {}
    _lock = threading.Lock()
    _model_path = None
    
    def __new__(cls):
        """Thread-aware singleton: different instance per thread"""
        thread_id = threading.get_ident()
        
        if thread_id not in cls._instances:
            with cls._lock:
                if thread_id not in cls._instances:
                    instance = super().__new__(cls)
                    instance._loaded = False
                    cls._instances[thread_id] = instance
        
        instance = cls._instances[thread_id]
        if not instance._loaded:
            instance._load_model()
            instance._loaded = True
        
        return instance
    
    def __init__(self):
        """Initialize but don't load model until needed"""
        if not hasattr(self, '_loaded'):
            self._loaded = False
    
    def _load_model(self):
        """Load the sentence transformer model"""
        try:
            model_path = getattr(settings, 'AI_MODEL_PATH', 'all-MiniLM-L6-v2')
            self._model = SentenceTransformer(model_path)
            logger.info(f"✅ AI Model loaded in thread {threading.get_ident()} from {model_path}")
        except Exception as e:
            logger.error(f"❌ Failed to load model: {e}")
            raise
    
    @classmethod
    def cleanup_thread(cls):
        """Clean up model for current thread (call when thread exits)"""
        thread_id = threading.get_ident()
        if thread_id in cls._instances:
            del cls._instances[thread_id]
            logger.info(f"🧹 Cleaned up AI model for thread {thread_id}")
    
    def generate_embedding(self, text: str) -> List[float]:
        """Generate embedding vector for text"""
        if not hasattr(self, '_model') or self._model is None:
            self._load_model()
        
        embedding = self._model.encode(text)
        return embedding.tolist()
    
    def find_best_answer_for_property(self, question: str, property_id: str, 
                                       threshold: float = 0.6) -> Dict[str, Any]:
        """Find best matching answer for this specific property"""
        question_embedding = self.generate_embedding(question)
        
        # Check Global Q&A first (greetings)
        best_global = GlobalQA.objects.filter(is_active=True).annotate(
            distance=CosineDistance('question_embedding', question_embedding)
        ).order_by('distance').first()
        
        if best_global:
            confidence = 1 - best_global.distance
            if confidence > threshold:
                return {
                    'answer': best_global.answer,
                    'confidence': confidence,
                    'matched_question': best_global.question,
                    'type': 'global'
                }
        
        # Check Property Q&A
        if property_id:
            best_prop = PropertyQA.objects.filter(
                property_id=property_id, 
                is_active=True
            ).annotate(
                distance=CosineDistance('question_embedding', question_embedding)
            ).select_related('master_question').order_by('distance').first()
            
            if best_prop:
                confidence = 1 - best_prop.distance
                if confidence > threshold:
                    return {
                        'answer': best_prop.answer,
                        'confidence': confidence,
                        'matched_question': best_prop.master_question.question,
                        'type': 'property'
                    }
        
        return {
            'answer': "Our agent will respond shortly. Thank you for your patience!",
            'confidence': 0.0,
            'matched_question': None,
            'type': 'fallback'
        }
    

    # ai_chatbot/services.py - Update find_best_answer_global_first

    def find_best_answer_global_first(self, question: str, property_id: str = None, 
                                    threshold: float = 0.6) -> Dict[str, Any]:
        """
        Priority: 1. Property Data (dynamic) | 2. Global Greetings | 3. Property Master QA | 4. Property Custom QA | 5. Fallback
        """
        
        # ✅ NEW: Check property data first (highest priority)
        if property_id:
            property_data_answer = self.get_property_info_answer(question, property_id)
            if property_data_answer:
                return property_data_answer
        
        question_embedding = self.generate_embedding(question)
        
        # 1. Global Greetings
        best_global = GlobalQA.objects.filter(is_active=True).annotate(
            distance=CosineDistance('question_embedding', question_embedding)
        ).order_by('distance').first()
        
        if best_global:
            global_conf = 1 - best_global.distance
            if global_conf > 0.92:
                return {
                    'answer': best_global.answer,
                    'confidence': global_conf,
                    'matched_question': best_global.question,
                    'type': 'global'
                }
        
        # 2. Property Master Q&A
        if property_id:
            best_prop = PropertyQA.objects.filter(
                property_id=property_id, 
                is_active=True
            ).annotate(
                distance=CosineDistance('question_embedding', question_embedding)
            ).select_related('master_question').order_by('distance').first()
            
            if best_prop:
                prop_conf = 1 - best_prop.distance
                if prop_conf > threshold:
                    return {
                        'answer': best_prop.answer,
                        'confidence': prop_conf,
                        'matched_question': best_prop.master_question.question,
                        'type': 'property_master'
                    }
        
        # 3. Property Custom Q&A
        if property_id:
            best_custom = PropertyCustomQA.objects.filter(
                property_id=property_id,
                is_active=True
            ).annotate(
                distance=CosineDistance('question_embedding', question_embedding)
            ).order_by('distance').first()
            
            if best_custom:
                custom_conf = 1 - best_custom.distance
                if custom_conf > threshold:
                    return {
                        'answer': best_custom.answer,
                        'confidence': custom_conf,
                        'matched_question': best_custom.question,
                        'type': 'property_custom'
                    }
        
        # 4. Fallback
        return {
            'answer': "Our agent will respond shortly. Thank you for your patience!",
            'confidence': 0.0,
            'matched_question': None,
            'type': 'fallback'
        }
    
    
    def get_property_info_answer(self, question: str, property_id: str) -> Dict[str, Any]:
        """Get answer from actual property data"""
        from Property.models import Property
        
        try:
            # ✅ Remove 'area' from select_related - it's not a foreign key
            property_obj = Property.objects.select_related('city').get(id=property_id)
        except Property.DoesNotExist:
            return None
        
        question_lower = question.lower()
        
        # City/Location questions
        if any(word in question_lower for word in ['city', 'location', 'which city', 'kahan', 'city name', 'shahar']):
            city_name = getattr(property_obj, 'city', None)
            if city_name:
                if hasattr(city_name, 'city'):
                    city = city_name.city
                else:
                    city = str(city_name)
            else:
                city = "Karachi"
            return {
                'answer': f"This property is located in {city}, Pakistan.",
                'confidence': 0.98,
                'type': 'property_data'
            }
        
        # Area/Sector questions - handle area as a property field (not foreign key)
        elif any(word in question_lower for word in ['area', 'sector', 'phase', 'block', 'location detail', 'area name']):
            # ✅ 'area' is likely a direct field, not a foreign key
            area = getattr(property_obj, 'area', None)
            if area:
                # If area is a string or has __str__ method
                area_name = str(area)
            else:
                area_name = "the main area"
            return {
                'answer': f"This property is in {area_name} area.",
                'confidence': 0.98,
                'type': 'property_data'
            }
        
        # Address questions
        elif any(word in question_lower for word in ['address', 'exact location', 'full address', 'pata', 'complete address']):
            address = getattr(property_obj, 'address', None)
            if address:
                return {
                    'answer': f"Full address: {address}",
                    'confidence': 0.98,
                    'type': 'property_data'
                }
        
        # Price/Rent questions
        elif any(word in question_lower for word in ['price', 'rent', 'cost', 'kitna', 'rate', 'price kya hai', 'rent kya hai']):
            price = getattr(property_obj, 'price', None)
            if price:
                return {
                    'answer': f"The price for this property is PKR {price:,}.",
                    'confidence': 0.98,
                    'type': 'property_data'
                }
        
        # Bedrooms
        elif any(word in question_lower for word in ['bedroom', 'bed', 'rooms', 'kitne kamray', 'bed count']):
            beds = getattr(property_obj, 'beds', None)
            if beds:
                return {
                    'answer': f"This property has {beds} bedroom(s).",
                    'confidence': 0.98,
                    'type': 'property_data'
                }
        
        # Bathrooms
        elif any(word in question_lower for word in ['bathroom', 'bath', 'washroom', 'bathrooms', 'bath count']):
            baths = getattr(property_obj, 'baths', None)
            if baths:
                return {
                    'answer': f"This property has {baths} bathroom(s).",
                    'confidence': 0.98,
                    'type': 'property_data'
                }
        
        # Property size
        elif any(word in question_lower for word in ['size', 'area size', 'total area', 'square feet', 'sqft', 'marle']):
            area_size = getattr(property_obj, 'area_unit', None) or getattr(property_obj, 'total_area', None) or getattr(property_obj, 'land_area', None)
            if area_size:
                return {
                    'answer': f"The total area of this property is {area_size} sq ft.",
                    'confidence': 0.98,
                    'type': 'property_data'
                }
        
        # Property type / Category
        elif any(word in question_lower for word in ['type', 'property type', 'kind', 'category']):
            category = getattr(property_obj, 'category', None)
            if category:
                if hasattr(category, 'category'):
                    category_name = category.category
                else:
                    category_name = str(category)
                return {
                    'answer': f"This is a {category_name} property.",
                    'confidence': 0.98,
                    'type': 'property_data'
                }
        
        elif any(word in question_lower for word in ['detail', 'property information', 'property detail kiya hai', 'description', 'description kiya hai']):
                desc = getattr(property_obj, 'desc', None)
                if desc:
                    if hasattr(desc, 'desc'):
                        description_name = desc.desc
                    else:
                        description_name = str(desc)
                    return {
                        'answer': f"iss property ki detail hai: {description_name}.",
                        'confidence': 0.98,
                        'type': 'property_data'
                    }

        return None
    
    def _cosine_similarity(self, embedding1: List[float], embedding2: List[float]) -> float:
        """Calculate cosine similarity between two embeddings"""
        vec1 = np.array(embedding1)
        vec2 = np.array(embedding2)
        dot = np.dot(vec1, vec2)
        norm1 = np.linalg.norm(vec1)
        norm2 = np.linalg.norm(vec2)
        return float(dot / (norm1 * norm2)) if norm1 and norm2 else 0.0


class AutoChatService:
    """Orchestration service for auto-chat functionality"""
    
    def __init__(self):
        self.embedding_service = EmbeddingService()
    
    def should_auto_reply(self, chat_id: str, property_id: str, 
                          last_agency_reply_at=None, last_client_message_at=None) -> bool:
        """Check if auto-reply should trigger"""
        from Chat.models import Chat
        
        try:
            # Get property auto-chat state
            property_state = PropertyAutoChatState.objects.get(property_id=property_id)
            
            if not property_state.is_auto_chat_enabled:
                return False
            
            # Get agency settings
            property_obj = property_state.property
            agency_setting = AgencyAutoChatSetting.objects.get(agency=property_obj.user)
            
            if not agency_setting.is_enabled:
                return False
            
            if not last_client_message_at:
                return False
            
            # If agency replied after last client message, don't auto-reply
            if last_agency_reply_at and last_agency_reply_at > last_client_message_at:
                return False
            
            # Check if configured delay passed
            time_diff = (timezone.now() - last_client_message_at).total_seconds()
            return time_diff >= agency_setting.delay_seconds
            
        except (PropertyAutoChatState.DoesNotExist, AgencyAutoChatSetting.DoesNotExist):
            return False
        except Exception as e:
            logger.error(f"Error checking auto-reply: {e}")
            return False
    
    def generate_auto_reply(self, client_message: str, property_id: str, 
                            chat_id: str) -> Optional[Dict[str, Any]]:
        """Generate auto-reply with proper thresholds"""
        try:
            from Property.models import Property
            
            property_obj = Property.objects.get(id=property_id)
            agency_setting = AgencyAutoChatSetting.objects.get(agency=property_obj.user)
            
            # Use agency's configured threshold
            result = self.embedding_service.find_best_answer_global_first(
                client_message,
                property_id,
                agency_setting.confidence_threshold
            )
            
            logger.info(f"🤖 Auto-reply | Type: {result['type']} | Confidence: {result['confidence']:.2f}")
            
            return {
                'answer': result['answer'],
                'confidence': result['confidence'],
                'matched_question': result.get('matched_question'),
                'is_auto_reply': True,
                'reply_type': result['type'],
                'is_fallback': result['type'] == 'fallback'
            }
            
        except Property.DoesNotExist:
            logger.error(f"Property {property_id} not found")
            return None
        except AgencyAutoChatSetting.DoesNotExist:
            logger.warning(f"No agency settings for property {property_id}, using defaults")
            # Fallback to defaults
            result = self.embedding_service.find_best_answer_global_first(
                client_message, property_id, 0.6
            )
            return {
                'answer': result['answer'],
                'confidence': result['confidence'],
                'matched_question': result.get('matched_question'),
                'is_auto_reply': True,
                'reply_type': result['type'],
                'is_fallback': result['type'] == 'fallback'
            }
        except Exception as e:
            logger.error(f"Auto-reply error: {e}", exc_info=True)
            return None
    
    def sync_property_listing_to_ai(self, property_id: str) -> bool:
        """
        Sync property data to AI Q&A
        FIX #5: Don't delete existing, update in place
        """
        from .models import MasterQuestion, PropertyQA
        from Property.models import Property
        
        try:
            property_obj = Property.objects.get(id=property_id)
            agency = property_obj.user
            
            # Mapping of listing fields to Master Questions
            data_map = {
                "What is the price or rent of this property?": f"The demand for this property is PKR {property_obj if hasattr(property_obj, 'price') else 'contact agent'}.",
                "What is the location and area of this property?": f"This property is located in {getattr(property_obj, 'area', 'the area')}, {getattr(property_obj, 'city', 'the city')}.",
                "How many bedrooms and bathrooms does it have?": f"It is a {getattr(property_obj, 'beds', 'N/A')} bedroom property with {getattr(property_obj, 'baths', 'N/A')} bathrooms.",
                "What is the total size or area of the property?": f"The total size is {getattr(property_obj, 'area', 'N/A')} {getattr(property_obj, 'area_unit', 'sq ft')}.",
                "What type of property is it?": f"This is a {getattr(property_obj, 'category', 'property')}.",
                "How many kitchens and TV lounges does it have?": f"It has {getattr(property_obj, 'kitchen', 'N/A')} kitchens and {getattr(property_obj, 'tv_launch', 'N/A')} TV lounges.",
                "What is contact number of the agent?": f"You can contact the agent at {getattr(property_obj, 'agent_two_phone_number', 'the provided number')}.",
                "Information About this property": f"{getattr(property_obj, 'desc', 'Contact agent for details')}",

            }
            
            updated_count = 0
            created_count = 0
            
            for q_text, answer_text in data_map.items():
                master_q = MasterQuestion.objects.filter(question=q_text, is_active=True).first()
                
                if master_q:
                    embedding = self.embedding_service.generate_embedding(q_text)
                    
                    # FIX #5: Update or create without deleting
                    obj, created = PropertyQA.objects.update_or_create(
                        property=property_obj,
                        master_question=master_q,
                        defaults={
                            'agency': agency,
                            'answer': answer_text,
                            'question_embedding': embedding,
                            'is_active': True
                        }
                    )
                    
                    if created:
                        created_count += 1
                    else:
                        updated_count += 1
            
            logger.info(f"✅ Sync complete for Property {property_id} | Created: {created_count} | Updated: {updated_count}")
            return True
            
        except Exception as e:
            logger.error(f"❌ Sync error for property {property_id}: {e}", exc_info=True)
            return False

@receiver(post_save, sender='Property.Property')
def auto_sync_ai_on_listing_save(sender, instance, created, **kwargs):
    """Jab bhi property save ho, AI data sync karo"""
    try:
        from ai_chatbot.services import AutoChatService
        service = AutoChatService()
        service.sync_property_listing_to_ai(instance.id)
    except Exception as e:
        logger.error(f"Signal Error: {e}")