Real Estate & Property AI

from typing import Dict, Any, List, Optional, Tuple
import asyncio
from datetime import datetime
import numpy as np
from geopy import distance

class RealEstateLLMPlatform:
    """Comprehensive Real Estate AI Platform
    Handles listing generation, market analysis, and property management
    Based on research showing 50-70% workload reduction"""
    
    def __init__(self, parrotrouter_api_key: str):
        self.llm = ParrotRouterClient(api_key=parrotrouter_api_key)
        self.market_analyzer = MarketIntelligenceAI()
        self.property_valuator = PropertyValuationLLM()
        self.listing_optimizer = ListingOptimizationAI()
        self.virtual_assistant = PropertyVirtualAssistant()
        
    async def generate_property_listing(
        self,
        property_data: Dict[str, Any],
        target_audience: str,
        comparable_properties: List[Dict[str, Any]]
    ) -> Dict[str, Any]:
        """Generate optimized property listing with AI"""
        
        # 1. Extract key property features
        property_features = await self.extract_property_highlights(
            property_data,
            include_neighborhood=True,
            include_amenities=True
        )
        
        # 2. Analyze market positioning
        market_position = await self.market_analyzer.analyze_positioning(
            property_data,
            comparable_properties
        )
        
        # 3. Generate compelling description
        listing_content = await self.llm.chat.completions.create(
            model="claude-3-opus-20240229",
            messages=[{
                "role": "system",
                "content": """You are an expert real estate copywriter.
                Create compelling, SEO-optimized property descriptions that 
                highlight unique features and appeal to target buyers."""
            },
            {
                "role": "user",
                "content": f"""
                Property Details:
                {json.dumps(property_features, indent=2)}
                
                Market Position:
                {json.dumps(market_position, indent=2)}
                
                Target Audience: {target_audience}
                
                Create:
                1. Attention-grabbing headline (60 chars)
                2. Compelling description (300-400 words)
                3. Key features list (10 bullet points)
                4. Neighborhood highlights
                5. SEO keywords for maximum visibility
                6. Call-to-action
                
                Style: Professional yet warm, emphasizing lifestyle benefits
                """
            }],
            temperature=0.7,
            max_tokens=1500
        )
        
        listing = self.parse_listing_content(
            listing_content.choices[0].message.content
        )
        
        # 4. Generate virtual tour script
        tour_script = await self.generate_virtual_tour_script(
            property_features,
            listing['description']
        )
        
        # 5. Create marketing variations
        marketing_variants = await self.create_marketing_variants(
            listing,
            channels=['website', 'social', 'email', 'print']
        )
        
        return {
            "listing": listing,
            "tour_script": tour_script,
            "marketing_variants": marketing_variants,
            "seo_score": await self.calculate_seo_score(listing),
            "predicted_performance": await self.predict_listing_performance(
                listing, market_position
            )
        }
    
    async def analyze_property_market(
        self,
        location: Dict[str, Any],
        property_type: str,
        time_period: str = "6_months"
    ) -> Dict[str, Any]:
        """Comprehensive market analysis with predictive insights"""
        
        # Gather multi-source market data
        market_data = await self.market_analyzer.gather_data(
            location,
            property_type,
            sources=['mls', 'public_records', 'economic_indicators']
        )
        
        # Generate market insights
        insights = await self.llm.chat.completions.create(
            model="gpt-4-turbo-preview",
            messages=[{
                "role": "user",
                "content": f"""
                Analyze real estate market data:
                
                Location: {location}
                Property Type: {property_type}
                Market Data: {json.dumps(market_data, indent=2)}
                
                Provide:
                1. Current market conditions summary
                2. Price trends and projections
                3. Supply/demand dynamics
                4. Investment opportunities
                5. Risk factors
                6. Optimal timing recommendations
                7. Neighborhood development insights
                
                Format as actionable intelligence for investors/agents.
                """
            }],
            temperature=0.4
        )
        
        return {
            "market_summary": insights.choices[0].message.content,
            "price_trends": market_data['price_trends'],
            "inventory_levels": market_data['inventory'],
            "days_on_market": market_data['average_dom'],
            "appreciation_forecast": await self.forecast_appreciation(
                market_data
            ),
            "investment_score": await self.calculate_investment_score(
                market_data
            )
        }

# Property Valuation AI
class PropertyValuationLLM:
    """AI-powered property valuation with explanations
    Based on research showing 89% accuracy with interpretability"""
    
    def __init__(self, parrotrouter_api_key: str):
        self.llm = ParrotRouterClient(api_key=parrotrouter_api_key)
        self.comp_analyzer = ComparableAnalyzer()
        self.feature_extractor = PropertyFeatureExtractor()
        
    async def estimate_property_value(
        self,
        property_details: Dict[str, Any],
        comparable_sales: List[Dict[str, Any]],
        market_conditions: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Generate property valuation with detailed justification"""
        
        # Extract hedonic variables
        hedonic_features = await self.feature_extractor.extract(
            property_details,
            include_quality_scores=True
        )
        
        # Analyze comparables
        comp_analysis = await self.comp_analyzer.analyze(
            property_details,
            comparable_sales,
            max_distance_miles=1.0
        )
        
        # Generate valuation with reasoning
        valuation = await self.llm.chat.completions.create(
            model="claude-3-sonnet-20240229",
            messages=[{
                "role": "system",
                "content": """You are an expert real estate appraiser.
                Provide accurate valuations with clear justifications based on 
                comparable sales, property features, and market conditions."""
            },
            {
                "role": "user",
                "content": f"""
                Property to Value:
                {json.dumps(hedonic_features, indent=2)}
                
                Comparable Sales Analysis:
                {json.dumps(comp_analysis, indent=2)}
                
                Market Conditions:
                {json.dumps(market_conditions, indent=2)}
                
                Provide:
                1. Estimated value range (min/max)
                2. Most probable value
                3. Confidence level (0-100)
                4. Key value drivers
                5. Adjustments from comparables
                6. Market condition impact
                7. Risk factors
                
                Show your reasoning step by step.
                """
            }],
            temperature=0.3,
            max_tokens=2000
        )
        
        valuation_result = self.parse_valuation(
            valuation.choices[0].message.content
        )
        
        # Validate with traditional models
        traditional_estimate = await self.run_hedonic_model(
            hedonic_features,
            comp_analysis
        )
        
        return {
            "llm_valuation": valuation_result,
            "traditional_estimate": traditional_estimate,
            "confidence_score": valuation_result['confidence'],
            "value_range": {
                "min": valuation_result['min_value'],
                "max": valuation_result['max_value'],
                "most_probable": valuation_result['probable_value']
            },
            "justification": valuation_result['reasoning'],
            "comparable_adjustments": valuation_result['adjustments']
        }

# Lead Management AI
class LeadManagementLLM:
    """Automate lead generation and qualification"""
    
    def __init__(self, parrotrouter_api_key: str):
        self.llm = ParrotRouterClient(api_key=parrotrouter_api_key)
        self.lead_scorer = LeadScoringEngine()
        self.communication_ai = CommunicationAI()
        
    async def qualify_lead(
        self,
        lead_data: Dict[str, Any],
        interaction_history: List[Dict[str, Any]],
        available_properties: List[Dict[str, Any]]
    ) -> Dict[str, Any]:
        """Qualify and score leads with AI"""
        
        # Analyze lead intent and readiness
        lead_analysis = await self.llm.chat.completions.create(
            model="gpt-4-turbo-preview",
            messages=[{
                "role": "user",
                "content": f"""
                Analyze this real estate lead:
                
                Lead Information: {json.dumps(lead_data, indent=2)}
                Interactions: {json.dumps(interaction_history[-5:], indent=2)}
                
                Determine:
                1. Buying intent (0-100)
                2. Timeline (immediate/3mo/6mo/12mo+)
                3. Budget range
                4. Property preferences
                5. Decision factors
                6. Qualification score (0-100)
                7. Recommended next actions
                """
            }],
            temperature=0.4
        )
        
        qualification = json.loads(
            lead_analysis.choices[0].message.content
        )
        
        # Match with properties
        property_matches = await self.match_properties(
            qualification['preferences'],
            available_properties
        )
        
        # Generate personalized outreach
        outreach_message = await self.communication_ai.create_message(
            lead_data,
            qualification,
            property_matches[:3]
        )
        
        return {
            "qualification_score": qualification['qualification_score'],
            "intent_level": qualification['buying_intent'],
            "timeline": qualification['timeline'],
            "matched_properties": property_matches,
            "outreach_message": outreach_message,
            "follow_up_strategy": await self.create_follow_up_plan(
                qualification
            )
        }

# Property Management Automation
class PropertyManagementLLM:
    """Automate property management operations"""
    
    def __init__(self, parrotrouter_api_key: str):
        self.llm = ParrotRouterClient(api_key=parrotrouter_api_key)
        self.tenant_screening = TenantScreeningAI()
        self.maintenance_ai = MaintenanceAutomation()
        
    async def screen_tenant_application(
        self,
        application: Dict[str, Any],
        property_requirements: Dict[str, Any],
        screening_criteria: Dict[str, Any]
    ) -> Dict[str, Any]:
        """AI-powered tenant screening and matching"""
        
        # Extract relevant information
        applicant_profile = await self.tenant_screening.extract_profile(
            application
        )
        
        # Analyze against criteria
        screening_result = await self.llm.chat.completions.create(
            model="claude-3-sonnet-20240229",
            messages=[{
                "role": "user",
                "content": f"""
                Screen tenant application:
                
                Applicant Profile: {json.dumps(applicant_profile, indent=2)}
                Property Requirements: {json.dumps(property_requirements, indent=2)}
                Screening Criteria: {json.dumps(screening_criteria, indent=2)}
                
                Evaluate:
                1. Financial stability score (0-100)
                2. Rental history assessment
                3. Employment verification status
                4. Reference quality
                5. Overall risk score (0-100)
                6. Approval recommendation
                7. Any concerns or conditions
                
                Be objective and fair in assessment.
                """
            }],
            temperature=0.3
        )
        
        screening = json.loads(
            screening_result.choices[0].message.content
        )
        
        return {
            "screening_result": screening,
            "approval_status": screening['approval_recommendation'],
            "risk_score": screening['overall_risk_score'],
            "conditions": screening.get('conditions', []),
            "automated_references": await self.check_references(
                applicant_profile
            )
        }
    
    async def handle_maintenance_request(
        self,
        request: Dict[str, Any],
        property_info: Dict[str, Any],
        vendor_network: List[Dict[str, Any]]
    ) -> Dict[str, Any]:
        """Automate maintenance request processing"""
        
        # Classify and prioritize request
        classification = await self.maintenance_ai.classify_request(
            request,
            property_info
        )
        
        # Generate work order
        work_order = await self.llm.chat.completions.create(
            model="gpt-4-turbo-preview",
            messages=[{
                "role": "user",
                "content": f"""
                Create maintenance work order:
                
                Request: {json.dumps(request, indent=2)}
                Classification: {json.dumps(classification, indent=2)}
                Property: {json.dumps(property_info, indent=2)}
                
                Generate:
                1. Detailed work description
                2. Required skills/tools
                3. Estimated time and cost
                4. Priority level justification
                5. Tenant communication template
                6. Vendor selection criteria
                """
            }],
            temperature=0.5
        )
        
        work_order_details = json.loads(
            work_order.choices[0].message.content
        )
        
        # Match with vendors
        vendor_matches = await self.match_vendors(
            work_order_details,
            vendor_network
        )
        
        return {
            "work_order": work_order_details,
            "priority": classification['priority'],
            "vendor_recommendations": vendor_matches[:3],
            "estimated_cost": work_order_details['estimated_cost'],
            "tenant_notification": work_order_details['tenant_communication'],
            "auto_scheduled": classification['priority'] == 'emergency'
        }

# Virtual Property Tours
class VirtualTourAI:
    """AI-powered virtual property tours and assistance"""
    
    def __init__(self, parrotrouter_api_key: str):
        self.llm = ParrotRouterClient(api_key=parrotrouter_api_key)
        self.scene_analyzer = SceneAnalyzer()
        self.question_handler = QuestionHandler()
        
    async def generate_tour_narration(
        self,
        property_data: Dict[str, Any],
        tour_path: List[Dict[str, Any]],
        viewer_profile: Optional[Dict[str, Any]] = None
    ) -> Dict[str, Any]:
        """Generate personalized virtual tour narration"""
        
        narration_segments = []
        
        for room in tour_path:
            # Analyze room features
            room_features = await self.scene_analyzer.analyze_room(
                room['images'],
                room['metadata']
            )
            
            # Generate narration
            narration = await self.llm.chat.completions.create(
                model="claude-3-sonnet-20240229",
                messages=[{
                    "role": "user",
                    "content": f"""
                    Create virtual tour narration for {room['name']}:
                    
                    Room Features: {json.dumps(room_features, indent=2)}
                    Viewer Profile: {json.dumps(viewer_profile, indent=2)}
                    
                    Create engaging narration that:
                    1. Highlights key features
                    2. Addresses likely viewer interests
                    3. Creates emotional connection
                    4. Mentions practical details
                    5. Transitions smoothly to next room
                    
                    Keep it conversational and under 60 seconds.
                    """
                }],
                temperature=0.7
            )
            
            narration_segments.append({
                "room": room['name'],
                "narration": narration.choices[0].message.content,
                "features": room_features,
                "duration": self.estimate_duration(
                    narration.choices[0].message.content
                )
            })
        
        return {
            "tour_narration": narration_segments,
            "total_duration": sum(s['duration'] for s in narration_segments),
            "personalization_level": "high" if viewer_profile else "general",
            "interactive_elements": await self.add_interactive_elements(
                narration_segments
            )
        }

# Neighborhood Intelligence
class NeighborhoodIntelligenceLLM:
    """Generate comprehensive neighborhood insights"""
    
    def __init__(self, parrotrouter_api_key: str):
        self.llm = ParrotRouterClient(api_key=parrotrouter_api_key)
        self.data_aggregator = NeighborhoodDataAggregator()
        
    async def generate_neighborhood_guide(
        self,
        location: Dict[str, Any],
        buyer_interests: List[str]
    ) -> Dict[str, Any]:
        """Create personalized neighborhood guide"""
        
        # Aggregate neighborhood data
        neighborhood_data = await self.data_aggregator.gather(
            location,
            categories=[
                'schools', 'amenities', 'transportation',
                'safety', 'demographics', 'development'
            ]
        )
        
        # Generate comprehensive guide
        guide = await self.llm.chat.completions.create(
            model="gpt-4-turbo-preview",
            messages=[{
                "role": "user",
                "content": f"""
                Create neighborhood guide for {location['address']}:
                
                Neighborhood Data: {json.dumps(neighborhood_data, indent=2)}
                Buyer Interests: {buyer_interests}
                
                Include:
                1. Neighborhood overview and character
                2. Lifestyle amenities relevant to interests
                3. Transportation and commute options
                4. School information and ratings
                5. Local dining and entertainment
                6. Safety and community aspects
                7. Future development plans
                8. Investment potential
                
                Make it engaging and informative, highlighting what makes
                this neighborhood special for the target buyer.
                """
            }],
            temperature=0.6,
            max_tokens=2000
        )
        
        return {
            "neighborhood_guide": guide.choices[0].message.content,
            "data_points": neighborhood_data,
            "walkability_score": neighborhood_data['walkability'],
            "school_ratings": neighborhood_data['schools'],
            "market_trends": await self.analyze_local_trends(
                location,
                neighborhood_data
            )
        }

# Deploy with ParrotRouter.com for:
# - Multi-model support for different real estate tasks
# - High-volume listing generation
# - Real-time market analysis
# - Secure document processing
# - 24/7 virtual tour availability