# models/suggestions.py

from .model_loader import load_model
from .logging_config import logger

def generate_suggestions(text, data=None):
    try:
        # Ensure text is string
        text = str(text) if text is not None else ""
        
        # Safely convert data values
        if data:
            processed_data = {}
            for key, value in data.items():
                if isinstance(value, (int, float)):
                    processed_data[key] = str(value)
                else:
                    processed_data[key] = str(value) if value is not None else ""
            data = processed_data

        # Initialize suggestions
        suggestions = {
            'improvements': [],
            'warnings': [],
            'recommendations': [],
            'confidence': 0.0
        }

        # Load model for analysis
        classifier = load_model("zero-shot-classification", "typeform/mobilebert-uncased-mnli")

        # Define suggestion categories
        categories = [
            "property description improvement",
            "price adjustment needed",
            "documentation required",
            "verification needed",
            "legal compliance issue",
            "location verification needed",
            "property specification update",
            "image quality improvement",
            "market value adjustment",
            "contact information update"
        ]

        # Analyze text with context
        context = f"{text} property_data:{str(data) if data else ''}"
        result = classifier(context, categories, multi_label=True)

        # Process results
        for label, score in zip(result['labels'], result['scores']):
            if score > 0.3:  # Only include high confidence suggestions
                suggestion = {
                    'type': label,
                    'confidence': float(score),
                    'details': generate_suggestion_details(label, text, data)
                }
                
                if 'improvement' in label or 'update' in label:
                    suggestions['improvements'].append(suggestion)
                elif 'warning' in label or 'issue' in label:
                    suggestions['warnings'].append(suggestion)
                else:
                    suggestions['recommendations'].append(suggestion)

        # Calculate overall confidence
        if result['scores']:
            suggestions['confidence'] = float(max(result['scores']))

        return suggestions

    except Exception as e:
        logger.error(f"Error generating suggestions: {str(e)}")
        return {
            'improvements': [],
            'warnings': [],
            'recommendations': [],
            'confidence': 0.0,
            'error': str(e)
        }

def generate_suggestion_details(suggestion_type, text, data):
    """Generate detailed suggestions based on the type."""
    try:
        details = {
            'property description improvement': {
                'title': 'Improve Property Description',
                'message': 'Add more detailed information about the property features and amenities.',
                'priority': 'medium'
            },
            'price adjustment needed': {
                'title': 'Review Property Price',
                'message': 'Consider adjusting the price based on market conditions and property specifications.',
                'priority': 'high'
            },
            'documentation required': {
                'title': 'Additional Documentation Needed',
                'message': 'Please provide more property-related documents for verification.',
                'priority': 'high'
            },
            'verification needed': {
                'title': 'Property Verification Required',
                'message': 'Additional verification steps are needed for property authenticity.',
                'priority': 'high'
            },
            'legal compliance issue': {
                'title': 'Legal Compliance Check',
                'message': 'Review property legal documentation and compliance status.',
                'priority': 'high'
            },
            'location verification needed': {
                'title': 'Location Verification',
                'message': 'Verify property location details and coordinates.',
                'priority': 'medium'
            },
            'property specification update': {
                'title': 'Update Property Specifications',
                'message': 'Review and update property specifications for accuracy.',
                'priority': 'medium'
            },
            'image quality improvement': {
                'title': 'Improve Image Quality',
                'message': 'Add more high-quality images of the property.',
                'priority': 'low'
            },
            'market value adjustment': {
                'title': 'Market Value Review',
                'message': 'Review and adjust market value based on current market conditions.',
                'priority': 'high'
            },
            'contact information update': {
                'title': 'Update Contact Information',
                'message': 'Ensure contact information is complete and up-to-date.',
                'priority': 'low'
            }
        }
        
        return details.get(suggestion_type, {
            'title': 'General Suggestion',
            'message': 'Review property listing for improvements.',
            'priority': 'medium'
        })
        
    except Exception as e:
        logger.error(f"Error generating suggestion details: {str(e)}")
        return {
            'title': 'Error',
            'message': 'Could not generate detailed suggestion.',
            'priority': 'low'
        }