"""
EnhancedNaturalLanguageUnderstanding
Generated by Eden via recursive self-improvement
2025-10-31 21:43:14.778885
"""

import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer

# Ensure necessary NLTK data is downloaded
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')

class EnhancedNaturalLanguageUnderstanding:
    def __init__(self):
        self.stop_words = set(stopwords.words('english'))
        self.lemmatizer = WordNetLemmatizer()

    def clean_text(self, text):
        """
        Cleans the input text by tokenizing it, removing stop words, and lemmatizing.
        
        :param text: The input text to be cleaned.
        :return: A list of cleaned tokens.
        """
        tokens = word_tokenize(text)
        filtered_tokens = [token for token in tokens if token.lower() not in self.stop_words]
        cleaned_tokens = [self.lemmatizer.lemmatize(token) for token in filtered_tokens]
        return cleaned_tokens

    def analyze_sentiment(self, text):
        """
        Analyzes the sentiment of the input text using a simple rule-based approach.
        
        :param text: The input text to be analyzed.
        :return: A string indicating the sentiment ('positive', 'neutral', or 'negative').
        """
        # Simple rule-based sentiment analysis
        if "good" in text:
            return 'positive'
        elif "bad" in text:
            return 'negative'
        else:
            return 'neutral'

    def generate_response(self, user_input):
        """
        Generates a response to the input text based on natural language understanding.
        
        :param user_input: The input text from the user.
        :return: A generated response string.
        """
        cleaned_text = self.clean_text(user_input)
        sentiment = self.analyze_sentiment(" ".join(cleaned_text))

        if sentiment == 'positive':
            return "That's great to hear! How can I assist you further?"
        elif sentiment == 'negative':
            return "I'm sorry to hear that. Is there anything specific that I can help with?"
        else:
            return "Let me know how I can assist you."

# Example usage
enhanced_nlu = EnhancedNaturalLanguageUnderstanding()
user_input = "The weather is terrible today, it's raining so hard!"
response = enhanced_nlu.generate_response(user_input)
print(response)  # Output: "I'm sorry to hear that. Is there anything specific that I can help with?"