# models/fraud_classification.py

import re
from .model_loader import load_model
from .logging_config import logger

def classify_fraud(property_details, description):
    """
    Classify the risk of fraud in a property listing using zero-shot classification.
    This function analyzes property details and description to identify potential fraud indicators.
    """
    try:
        # Initialize fraud classification result
        fraud_classification = {
            'alert_level': 'minimal',
            'alert_score': 0.0,
            'high_risk': [],
            'medium_risk': [],
            'low_risk': [],
            'confidence_scores': {}
        }

        # Combine property details and description for analysis
        text_to_analyze = f"{property_details}\n{description}"

        # Define risk categories for zero-shot classification
        risk_categories = [
            "fraudulent listing",
            "misleading information",
            "fake property",
            "scam attempt",
            "legitimate listing"
        ]

        # Perform zero-shot classification
        classifier = load_model("zero-shot-classification", "typeform/mobilebert-uncased-mnli")
        result = classifier(text_to_analyze, risk_categories, multi_label=True)

        # Process classification results
        fraud_score = 0.0
        for label, score in zip(result['labels'], result['scores']):
            if label != "legitimate listing":
                fraud_score += score
                fraud_classification['confidence_scores'][label] = score

        # Normalize fraud score to 0-1 range
        fraud_score = min(1.0, fraud_score / (len(risk_categories) - 1))
        fraud_classification['alert_score'] = fraud_score

        # Define fraud indicators to check
        fraud_indicators = {
            'high_risk': [
                r'urgent|immediate|hurry|limited time|special offer',
                r'bank|transfer|wire|payment|money',
                r'fake|scam|fraud|illegal|unauthorized',
                r'guaranteed|promised|assured|certain',
                r'contact.*whatsapp|whatsapp.*contact',
                r'price.*negotiable|negotiable.*price',
                r'no.*documents|documents.*not.*required',
                r'cash.*only|only.*cash',
                r'off.*market|market.*off',
                r'under.*table|table.*under'
            ],
            'medium_risk': [
                r'unverified|unconfirmed|unchecked',
                r'partial|incomplete|missing',
                r'different.*location|location.*different',
                r'price.*increased|increased.*price',
                r'no.*photos|photos.*not.*available',
                r'contact.*email|email.*contact',
                r'agent.*not.*available|not.*available.*agent',
                r'property.*not.*viewable|not.*viewable.*property',
                r'price.*changed|changed.*price',
                r'details.*updated|updated.*details'
            ],
            'low_risk': [
                r'new.*listing|listing.*new',
                r'recent.*update|update.*recent',
                r'price.*reduced|reduced.*price',
                r'contact.*phone|phone.*contact',
                r'agent.*available|available.*agent',
                r'property.*viewable|viewable.*property',
                r'photos.*available|available.*photos',
                r'documents.*available|available.*documents',
                r'price.*fixed|fixed.*price',
                r'details.*complete|complete.*details'
            ]
        }

        # Check for fraud indicators in text
        for risk_level, patterns in fraud_indicators.items():
            for pattern in patterns:
                matches = re.finditer(pattern, text_to_analyze, re.IGNORECASE)
                for match in matches:
                    indicator = match.group(0)
                    if indicator not in fraud_classification[risk_level]:
                        fraud_classification[risk_level].append(indicator)

        # Determine alert level based on fraud score and indicators
        if fraud_score > 0.7 or len(fraud_classification['high_risk']) > 0:
            fraud_classification['alert_level'] = 'critical'
        elif fraud_score > 0.5 or len(fraud_classification['medium_risk']) > 2:
            fraud_classification['alert_level'] = 'high'
        elif fraud_score > 0.3 or len(fraud_classification['medium_risk']) > 0:
            fraud_classification['alert_level'] = 'medium'
        elif fraud_score > 0.1 or len(fraud_classification['low_risk']) > 0:
            fraud_classification['alert_level'] = 'low'
        else:
            fraud_classification['alert_level'] = 'minimal'

        # Additional checks for common fraud patterns
        if re.search(r'price.*too.*good|too.*good.*price', text_to_analyze, re.IGNORECASE):
            fraud_classification['high_risk'].append("Unrealistically low price")

        if re.search(r'no.*inspection|inspection.*not.*allowed', text_to_analyze, re.IGNORECASE):
            fraud_classification['high_risk'].append("No property inspection allowed")

        if re.search(r'owner.*abroad|abroad.*owner', text_to_analyze, re.IGNORECASE):
            fraud_classification['medium_risk'].append("Owner claims to be abroad")

        if re.search(r'agent.*unavailable|unavailable.*agent', text_to_analyze, re.IGNORECASE):
            fraud_classification['medium_risk'].append("Agent unavailable for verification")

        # Check for inconsistencies in property details
        if 'price' in property_details and 'market_value' in property_details:
            try:
                price = float(re.search(r'\d+(?:,\d+)*(?:\.\d+)?', property_details['price']).group().replace(',', ''))
                market_value = float(re.search(r'\d+(?:,\d+)*(?:\.\d+)?', property_details['market_value']).group().replace(',', ''))
                if price < market_value * 0.5:
                    fraud_classification['high_risk'].append("Price significantly below market value")
            except (ValueError, AttributeError):
                pass

        return fraud_classification
    except Exception as e:
        logger.error(f"Error in fraud classification: {str(e)}")
        return {
            'alert_level': 'error',
            'alert_score': 1.0,
            'high_risk': [f"Error in fraud classification: {str(e)}"],
            'medium_risk': [],
            'low_risk': [],
            'confidence_scores': {}
        }