"""
FeedbackCategorizer
Generated by Eden via recursive self-improvement
2025-11-01 18:14:17.551846
"""

import re
from collections import defaultdict

class FeedbackCategorizer:
    """
    This class is designed to process customer feedback and categorize it into predefined themes.
    
    Attributes:
        categories (dict): A dictionary where each key is a theme category, and the value is a list of matching patterns used for regex matching.

    Methods:
        __init__ : Initializes the FeedbackCategorizer with predefined categories and patterns.
        process_feedback(feedback: str) -> dict: Processes the given feedback string and returns a categorized report.
    """
    
    def __init__(self):
        self.categories = {
            "Feature Requests": [r"^(?:add|implement|enhance)(?:\s+the\s+)?(.*feature).*", r"(?:requirement|request) for (.*)"],
            "Bug Reports": [r"BUG: ?(.*)", r"issue with (.*)"],
            "User Experience": [r"user experience issues? in (.*?)", r"interface problems with (.*?)"],
            "Suggestions": [r"suggestions? about (.*?)(?:\s+to\s+improve|for improvement)"],
        }
    
    def process_feedback(self, feedback: str) -> dict:
        """
        Processes the given feedback string and categorizes it into predefined themes.
        
        Args:
            feedback (str): The customer feedback text to be processed.

        Returns:
            dict: A dictionary with categories as keys and lists of matching patterns as values.
        """
        categorized_feedback = defaultdict(list)
        for category, patterns in self.categories.items():
            for pattern in patterns:
                if re.search(pattern, feedback):
                    categorized_feedback[category].append(feedback)
                    break
        return categorized_feedback

# Example usage
feedback_text = "I would love to see a feature that allows users to customize their profile background. The current interface is not intuitive and could use some improvements."
categorizer = FeedbackCategorizer()
categorized_feedback = categorizer.process_feedback(feedback_text)
print(categorized_feedback)