import logging
import re
from typing import Dict, List, Tuple, Optional

logger = logging.getLogger(__name__)


class CalorieDriftValidator:
    
    TOLERANCE = 0.05  # ±5% tolerance
    
    @staticmethod
    def create_validator() -> 'CalorieDriftValidator':
        return CalorieDriftValidator()
    
    def extract_meal_calories(self, meal_plan_text: str) -> List[Tuple[str, float]]:
        
        meals = []
        
        pattern = r'([\w\s]+)\s*\(Approx\.\s*(\d+(?:\.\d+)?)\s*kcal\)'
        matches = re.findall(pattern, meal_plan_text, re.IGNORECASE)
        
        for meal_name, cal_value in matches:
            try:
                calories = float(cal_value)
                meals.append((meal_name.strip(), calories))
            except ValueError:
                logger.warning(f"Could not parse calorie value: {cal_value}")
                continue
        
        logger.info(f"Extracted {len(meals)} meals with calorie values")
        return meals
    
    def validate_total_calories(
        self,
        meal_calories: List[Tuple[str, float]],
        daily_target: float
    ) -> Dict[str, any]:
       
        if not meal_calories:
            return {
                "valid": False,
                "total_extracted": 0.0,
                "target": daily_target,
                "deviation_pct": 100.0,
                "within_tolerance": False,
                "message": "No meal calories extracted"
            }
        
        total_extracted = sum(cal for _, cal in meal_calories)
        deviation = abs(total_extracted - daily_target) / daily_target
        deviation_pct = deviation * 100
        
        within_tolerance = deviation <= self.TOLERANCE
        
        result = {
            "valid": True,
            "total_extracted": total_extracted,
            "target": daily_target,
            "deviation_pct": round(deviation_pct, 2),
            "within_tolerance": within_tolerance
        }
        
        if within_tolerance:
            logger.info(
                f"Calorie validation PASSED: {total_extracted} kcal "
                f"vs target {daily_target} kcal (deviation: {deviation_pct:.2f}%)"
            )
        else:
            logger.warning(
                f"Calorie validation FAILED: {total_extracted} kcal "
                f"vs target {daily_target} kcal (deviation: {deviation_pct:.2f}% > {self.TOLERANCE*100}%)"
            )
        
        return result
    
    def scale_meal_calories(
        self,
        meal_plan_text: str,
        scaling_factor: float
    ) -> str:
        
        def replace_calorie(match):
            meal_name = match.group(1)
            original_cal = float(match.group(2))
            scaled_cal = round(original_cal * scaling_factor, 1)
            return f"{meal_name} (Approx. {int(scaled_cal)} kcal)"
        
        pattern = r'([\w\s]+)\s*\(Approx\.\s*(\d+(?:\.\d+)?)\s*kcal\)'
        scaled_text = re.sub(pattern, replace_calorie, meal_plan_text, flags=re.IGNORECASE)
        
        logger.info(f"Scaled meal calories by factor {scaling_factor:.3f}")
        return scaled_text
    
    def validate_and_correct(
        self,
        meal_plan_text: str,
        daily_calorie_target: float
    ) -> Tuple[str, Dict[str, any]]:
       
        # Extract meals
        meal_calories = self.extract_meal_calories(meal_plan_text)
        
        # Validate
        validation = self.validate_total_calories(meal_calories, daily_calorie_target)
        
        # If within tolerance, return as-is
        if validation["within_tolerance"] or not validation["valid"]:
            return meal_plan_text, validation
        
        # Calculate scaling factor
        total_extracted = validation["total_extracted"]
        scaling_factor = daily_calorie_target / total_extracted
        
        logger.info(
            f"Applying calorie correction: scaling by {scaling_factor:.3f} "
            f"to bring {total_extracted} → {daily_calorie_target} kcal"
        )
        
        # Scale calories
        corrected_plan = self.scale_meal_calories(meal_plan_text, scaling_factor)
        
        # Re-validate
        corrected_meals = self.extract_meal_calories(corrected_plan)
        final_validation = self.validate_total_calories(corrected_meals, daily_calorie_target)
        final_validation["corrected"] = True
        final_validation["scaling_factor"] = round(scaling_factor, 3)
        
        return corrected_plan, final_validation