models/property_summary.py

# models/property_summary.py

from .model_loader import load_model
from .logging_config import logger
from .utils import summarize_text

def validate_and_format_data(data):
    """Validate and format property data"""
    # Format square feet
    try:
        sq_ft = float(data.get('sq_ft', 0))
        if sq_ft < 100:  # If square feet seems too small, it might be in wrong unit
            sq_ft *= 100  # Convert to square feet if it was in square meters
        data['sq_ft'] = int(sq_ft)
    except:
        data['sq_ft'] = 0

    # Format market value
    try:
        market_value = float(data.get('market_value', 0))
        if market_value > 1000000000:  # If value seems too high
            market_value = market_value / 100  # Adjust if there's a decimal point issue
        data['market_value'] = int(market_value)
    except:
        data['market_value'] = 0

    # Format amenities
    if data.get('amenities'):
        if isinstance(data['amenities'], str):
            amenities = [a.strip() for a in data['amenities'].split(',') if a.strip()]
            data['amenities'] = amenities
        elif isinstance(data['amenities'], list):
            data['amenities'] = [a.strip() for a in data['amenities'] if a.strip()]
    else:
        data['amenities'] = []

    return data

def format_price(price):
    """Format price in Indian currency format"""
    try:
        price = float(price)
        if price >= 10000000:  # 1 Crore
            return f"₹{price/10000000:.2f} Cr"
        elif price >= 100000:  # 1 Lakh
            return f"₹{price/100000:.2f} L"
        else:
            return f"₹{price:,.2f}"
    except:
        return f"₹{price}"

def generate_static_summary(data):
    """Generate a conversational property summary"""
    # Validate and format data
    data = validate_and_format_data(data)
    price = format_price(data.get('market_value', '0'))
    
    # Get property type with proper formatting
    property_type = data.get('property_type', 'property').strip()
    if property_type.lower() == 'apartment':
        property_type = '2 BHK Apartment'  # Add BHK information if available
    
    summary = f"""
    Namaste! Let me tell you about this wonderful {property_type} that's {data.get('status', 'available').lower()}.

    This beautiful property is located in {data.get('city', 'the city')}, {data.get('state', '')}. It's a spacious {property_type} spanning {data.get('sq_ft', '0')} square feet, perfect for your family.

    Key Highlights:
    • Price: {price}
    • Bedrooms: {data.get('bedrooms', '0')} spacious bedrooms
    • Bathrooms: {data.get('bathrooms', '0')} modern bathrooms
    • Year Built: {data.get('year_built', 'N/A')}
    • Parking: {data.get('parking_spaces', '0')} covered parking spaces
    """

    # Add property description if available
    if data.get('property_description'):
        summary += f"\n\nProperty Description:\n{data.get('property_description')}"

    # Add possession date if available
    if data.get('possession_date'):
        summary += f"\n• Ready for possession from: {data.get('possession_date')}"

    # Add amenities if available
    if data.get('amenities'):
        amenities = data['amenities']
        if len(amenities) > 0:
            summary += "\n\nNearby Amenities:\n• " + "\n• ".join(amenities)

    # Add nearby landmarks if available
    if data.get('nearby_landmarks'):
        landmarks = data['nearby_landmarks']
        if isinstance(landmarks, str):
            landmarks = [l.strip() for l in landmarks.split(',') if l.strip()]
        if len(landmarks) > 0:
            summary += "\n\nNearby Landmarks:\n• " + "\n• ".join(landmarks)

    # Add a friendly closing
    summary += "\n\nThis property offers excellent value for money and is located in a prime area. Would you like to know more details about this property?"

    return summary.strip()

def generate_property_summary(data):
    try:
        # Validate and format data first
        data = validate_and_format_data(data)

        # Create a detailed context for summary generation
        property_context = f"""
        Property Details:
        Name: {data.get('property_name', '')}
        Type: {data.get('property_type', '')}
        Status: {data.get('status', '')}
        Location: {data.get('address', '')}, {data.get('city', '')}, {data.get('state', '')}, {data.get('country', '')}
        Size: {data.get('sq_ft', '')} sq. ft.
        Price: {format_price(data.get('market_value', '0'))}
        Bedrooms: {data.get('bedrooms', '')}
        Bathrooms: {data.get('bathrooms', '')}
        Year Built: {data.get('year_built', '')}
        Parking: {data.get('parking_spaces', '')} spaces
        Description: {data.get('property_description', '')}
        Possession Date: {data.get('possession_date', '')}
        Amenities: {', '.join(data.get('amenities', []))}
        Nearby Landmarks: {data.get('nearby_landmarks', '')}
        """

        # Try to use BART for summary generation
        try:
            summarizer = load_model("summarization", "sshleifer/distilbart-cnn-6-6")
            summary_result = summarizer(property_context, max_length=500, min_length=100, do_sample=False)
            initial_summary = summary_result[0]['summary_text']
        except Exception as model_error:
            logger.warning(f"Model generation failed, using static summary: {str(model_error)}")
            initial_summary = generate_static_summary(data)

        # Enhance summary with key features
        key_features = []

        # Add property type and status
        if data.get('property_type') and data.get('status'):
            key_features.append(f"This {data['property_type']} is {data['status'].lower()}")

        # Add location if available
        location_parts = []
        if data.get('city'):
            location_parts.append(data['city'])
        if data.get('state'):
            location_parts.append(data['state'])
        if location_parts:
            key_features.append(f"Located in {', '.join(location_parts)}")

        # Add size and price if available
        if data.get('sq_ft'):
            key_features.append(f"Spans {data['sq_ft']} sq. ft.")
        if data.get('market_value'):
            key_features.append(f"Priced at {format_price(data['market_value'])}")

        # Add rooms information
        rooms_info = []
        if data.get('bedrooms'):
            rooms_info.append(f"{data['bedrooms']} bedroom{'s' if data['bedrooms'] != '1' else ''}")
        if data.get('bathrooms'):
            rooms_info.append(f"{data['bathrooms']} bathroom{'s' if data['bathrooms'] != '1' else ''}")
        if rooms_info:
            key_features.append(f"Features {' and '.join(rooms_info)}")

        # Add parking information
        if data.get('parking_spaces'):
            key_features.append(f"Includes {data['parking_spaces']} covered parking space{'s' if data['parking_spaces'] != '1' else ''}")

        # Add possession date if available
        if data.get('possession_date'):
            key_features.append(f"Ready for possession from {data['possession_date']}")

        # Add amenities if available
        if data.get('amenities'):
            amenities = data['amenities']
            if len(amenities) > 0:
                key_features.append(f"Amenities: {', '.join(amenities)}")

        # Combine initial summary with key features
        enhanced_summary = initial_summary
        if key_features:
            enhanced_summary += "\n\nKey Features:\n• " + "\n• ".join(key_features)

        # Clean up the summary
        enhanced_summary = enhanced_summary.replace("  ", " ").strip()

        return enhanced_summary
    except Exception as e:
        logger.error(f"Error generating property summary: {str(e)}")
        return generate_static_summary(data)