# fairness_aware implementation
import pandas as pd
from typing import Dict, List

class FAIRNESS_AWARE:
    def __init__(self):
        """
        Initialize the fairness-aware module.

        Attributes:
            fairness_metrics (dict): A dictionary to store calculated fairness metrics, 
            such as the distribution of recommendations across different user demographics.
        """
        self.fairness_metrics = {}

    def evaluate_fairness(self, recommendations: Dict[int, List[int]], user_attributes: pd.DataFrame) -> Dict[str, float]:
        """
        Evaluate the fairness of the recommendations across different user groups.

        Parameters:
            recommendations (dict): A dictionary mapping user IDs to lists of recommended item IDs.
            user_attributes (pd.DataFrame): A DataFrame containing user demographic information, 
            such as age, gender, and other relevant attributes.

        Returns:
            dict: A dictionary of fairness metrics, providing insights into how recommendations 
            are distributed across different user groups. For example, it may include the 
            distribution of recommendations by gender or age group.
        """
        # Example: Calculate the distribution of recommendations across gender
        gender_distribution = user_attributes['gender'].value_counts(normalize=True).to_dict()
        self.fairness_metrics['gender_distribution'] = gender_distribution
        return self.fairness_metrics

    def ensure_fairness(self, recommendations: Dict[int, List[int]], user_attributes: pd.DataFrame) -> Dict[int, List[int]]:
        """
        Adjust recommendations to ensure fairness across user groups.

        Parameters:
            recommendations (dict): A dictionary mapping user IDs to lists of recommended item IDs.
            user_attributes (pd.DataFrame): A DataFrame containing user demographic information, 
            such as age, gender, and other relevant attributes.

        Returns:
            dict: Adjusted recommendations ensuring fairness, potentially modifying the original 
            recommendations to achieve a more balanced distribution across user groups.
        """
        # Placeholder: Implement logic to adjust recommendations for fairness
        return recommendations
