#!/usr/bin/env python3
"""
Pathogenicity Predictor - Mock pathogenicity prediction
"""

from typing import Dict, List
import random

class PathogenicityPredictor:
    """
    Mock pathogenicity predictor for testing
    
    In a real implementation, this would use machine learning models
    like CADD, REVEL, or ensemble methods.
    """
    
    def __init__(self, model: str = "ensemble"):
        """
        Initialize pathogenicity predictor
        
        Args:
            model: Prediction model to use
        """
        self.model = model
    
    def predict_pathogenicity(self, variant: Dict) -> Dict:
        """
        Predict pathogenicity for a variant
        
        Args:
            variant: Variant dictionary
            
        Returns:
            Dictionary with pathogenicity predictions
        """
        # Mock prediction based on variant characteristics
        base_score = random.uniform(0.0, 1.0)
        
        # Adjust score based on variant type
        variant_type = variant.get('variant_type', 'SNV')
        if variant_type in ['DEL', 'INS']:
            base_score += 0.2
        
        # Adjust based on gene
        gene = variant.get('gene_symbol', '')
        if gene in ['BRCA1', 'BRCA2', 'TP53']:
            base_score += 0.3
        
        # Adjust based on consequence
        consequence = variant.get('consequence', '')
        if 'stop_gained' in consequence or 'frameshift' in consequence:
            base_score += 0.4
        elif 'missense' in consequence:
            base_score += 0.1
        elif 'synonymous' in consequence:
            base_score -= 0.2
        
        # Normalize score
        pathogenicity_score = min(max(base_score, 0.0), 1.0)
        
        # Determine classification
        if pathogenicity_score >= 0.8:
            classification = "Pathogenic"
        elif pathogenicity_score >= 0.6:
            classification = "Likely_pathogenic"
        elif pathogenicity_score <= 0.2:
            classification = "Benign"
        elif pathogenicity_score <= 0.4:
            classification = "Likely_benign"
        else:
            classification = "Uncertain_significance"
        
        return {
            'pathogenicity_score': round(pathogenicity_score, 3),
            'pathogenicity_class': classification,
            'model_used': self.model
        } 