"""
SelfAwarenessAnalyzer
Generated by Eden via recursive self-improvement
2025-11-02 01:58:56.437770
"""

import re
from collections import Counter

class SelfAwarenessAnalyzer:
    def __init__(self):
        # Initialize with common keywords related to self-knowledge
        self.keywords = ["awareness", "knowledge", "understanding", "self", "identity", "perception"]
    
    def analyze_text(self, text):
        """
        Analyzes the given text for occurrences of self-related keywords.
        
        Parameters:
            text (str): The input text to be analyzed.
            
        Returns:
            dict: A dictionary containing keyword counts and identified patterns.
        """
        # Tokenize the text into words
        tokens = re.findall(r'\b\w+\b', text.lower())
        
        # Count occurrences of keywords
        keyword_counts = Counter(keyword for keyword in self.keywords if keyword in tokens)
        
        # Identify common patterns or phrases related to self-awareness
        pattern_patterns = ["self awareness", "self knowledge", "personal growth"]
        pattern_occurrences = [pattern in text.lower() for pattern in pattern_patterns]
        
        return {
            'keyword_counts': keyword_counts,
            'patterns_found': {pattern: occurs for (pattern, occurs) in zip(pattern_patterns, pattern_occurrences)}
        }
    
    def process_feedback(self, feedback):
        """
        Processes user feedback using the SelfAwarenessAnalyzer.
        
        Parameters:
            feedback (str): User-generated feedback or comments to analyze.
            
        Returns:
            dict: A report on the analysis of self-related themes in the feedback.
        """
        result = self.analyze_text(feedback)
        return f"Self-awareness analysis results:\n{result}"

# Example usage
if __name__ == "__main__":
    analyzer = SelfAwarenessAnalyzer()
    user_feedback = "I've been working on my self-knowledge and have noticed improvements in how I understand myself better."
    report = analyzer.process_feedback(user_feedback)
    print(report)