import json
import os
from typing import Dict, List, Tuple

class RAGScoreCalculator:
    """
    Dynamic RAG Score calculator that calculates scores at runtime
    without modifying the original JSON files.
    """
    
    def __init__(self, retrieval_dir: str = "result/retrieval"):
        self.retrieval_dir = retrieval_dir
        self.stats = None
        self.all_data = None
        self._load_and_analyze()
    
    def _load_and_analyze(self):
        """Load all retrieval detail files and calculate normalization statistics."""
        self.all_data = []
        detail_files = [f for f in os.listdir(self.retrieval_dir) if f.startswith('detail_')]
        
        if not detail_files:
            print("Warning: No detail files found in retrieval directory")
            return
        
        for filename in detail_files:
            filepath = os.path.join(self.retrieval_dir, filename)
            try:
                with open(filepath, 'r') as f:
                    data = json.load(f)
                    self.all_data.append(data)
            except Exception as e:
                print(f"Error loading {filename}: {e}")
                continue
        
        if not self.all_data:
            print("Warning: No valid data loaded from detail files")
            return
        
        # Calculate normalization statistics
        self._calculate_stats()
    
    def _calculate_stats(self):
        """Calculate min/max statistics for normalization."""
        if not self.all_data:
            return
        
        # Extract values for analysis
        rag_success_rates = [d.get('RAG_success_rate', 0) for d in self.all_data]
        max_correct_refs = [d.get('max_correct_references', 0) for d in self.all_data]
        false_positives = [d.get('total_false_positives', 0) for d in self.all_data]
        missed_refs = [d.get('total_missed_references', 0) for d in self.all_data]
        
        # Calculate min/max for normalization
        self.stats = {
            'rag_success_rate': {
                'min': min(rag_success_rates),
                'max': max(rag_success_rates)
            },
            'max_correct_references': {
                'min': min(max_correct_refs),
                'max': max(max_correct_refs)
            },
            'total_false_positives': {
                'min': min(false_positives),
                'max': max(false_positives)
            },
            'total_missed_references': {
                'min': 0,  # Fixed minimum value
                'max': 7114  # Fixed maximum value
            }
        }
    
    def normalize_value(self, value, min_val, max_val, higher_is_better=True):
        """Normalize a value to 0-1 range."""
        if max_val == min_val:
            return 1.0  # If all values are the same, return 1
        
        normalized = (value - min_val) / (max_val - min_val)
        
        if not higher_is_better:
            normalized = 1 - normalized  # Flip for "lower is better" metrics
        
        return normalized
    
    def calculate_rag_score(self, data: Dict) -> float:
        """Calculate the RAG score for a single model's data."""
        if not self.stats:
            print("Warning: No statistics available for normalization")
            return 0.0
        
        # Extract values with defaults
        rag_success_rate = data.get('RAG_success_rate', 0)
        max_correct_refs = data.get('max_correct_references', 0)
        false_positives = data.get('total_false_positives', 0)
        missed_refs = data.get('total_missed_references', 0)
        
        # Normalize values (0-1)
        norm_max_correct = self.normalize_value(
            max_correct_refs, 
            self.stats['max_correct_references']['min'], 
            self.stats['max_correct_references']['max'], 
            higher_is_better=True
        )
        
        norm_false_positives = self.normalize_value(
            false_positives,
            self.stats['total_false_positives']['min'],
            self.stats['total_false_positives']['max'],
            higher_is_better=False  # Lower is better
        )
        
        norm_missed_refs = self.normalize_value(
            missed_refs,
            self.stats['total_missed_references']['min'],
            self.stats['total_missed_references']['max'],
            higher_is_better=False  # Lower is better
        )
        
        # Calculate weighted score
        # Weights: rag_success_rate=0.9, false_positives=0.9, max_correct=0.1, missed_refs=0.1
        rag_score = (
            0.9 * rag_success_rate +
            0.9 * norm_false_positives +
            0.1 * norm_max_correct +
            0.1 * norm_missed_refs
        ) / 2.0  # Divide by 2 since total weights = 2.0
        
        return round(rag_score, 4)
    
    def get_normalization_info(self) -> Dict:
        """Get current normalization statistics for debugging."""
        return {
            'stats': self.stats,
            'total_files': len(self.all_data) if self.all_data else 0,
            'retrieval_dir': self.retrieval_dir
        }
    
    def refresh_stats(self):
        """Refresh statistics by reloading data - call this when new data is added."""
        print("Refreshing RAG Score normalization statistics...")
        self._load_and_analyze()
        return self.stats is not None

def main():
    """Main function for testing RAG score calculations."""
    calculator = RAGScoreCalculator()
    
    print("RAG Score Calculator (Runtime Only)")
    print("===================================")
    
    # Show normalization info
    info = calculator.get_normalization_info()
    print(f"Total files: {info['total_files']}")
    print(f"Retrieval directory: {info['retrieval_dir']}")
    
    if info['stats']:
        print("\nNormalization ranges:")
        for metric, data in info['stats'].items():
            print(f"  {metric}: {data['min']} - {data['max']}")
        
        print("\nSample RAG Score calculations:")
        for i, data in enumerate(calculator.all_data[:5]):  # Show first 5
            rag_score = calculator.calculate_rag_score(data)
            model_name = data.get('model_name', 'Unknown')
            print(f"  {model_name}: {rag_score}")
    else:
        print("\n❌ No statistics available for normalization")

if __name__ == "__main__":
    main()