"""
SemanticContextualizer
Generated by Eden via recursive self-improvement
2025-10-31 21:09:13.149763
"""

import spacy

# Load a pre-trained language model (using spaCy as an example)
nlp = spacy.load("en_core_web_sm")

class SemanticContextualizer:
    def __init__(self):
        self.context_memory = []

    def contextualize(self, input_text: str) -> str:
        """
        Analyzes the input text and integrates it with context memory to generate a more relevant response.
        
        Parameters:
            input_text (str): The user's input text.
            
        Returns:
            str: A contextualized response based on the input and past interactions.
        """
        # Process the input text using spaCy for entity recognition
        doc = nlp(input_text)
        entities = [ent.text for ent in doc.ents]
        
        # Extract key phrases from the document to understand context
        key_phrases = [sent.as_posix() for sent in doc.sents]
        
        # Integrate current input with past context if available
        if self.context_memory:
            previous_context = " ".join(self.context_memory)
            full_context = f"{previous_context} {input_text}"
            doc = nlp(full_context)
            entities += [ent.text for ent in doc.ents]
            key_phrases.extend([sent.as_posix() for sent in doc.sents])
        
        # Generate a contextualized response
        contextual_response = " ".join(entities + key_phrases)
        
        return contextual_response

    def update_context(self, new_context: str):
        """
        Updates the context memory with new information.
        
        Parameters:
            new_context (str): New context or input text to be added to the memory.
        """
        self.context_memory.append(new_context)

# Example usage
contextualizer = SemanticContextualizer()

# User inputs
input1 = "What is the weather like today?"
input2 = "I want to know more about the history of New York City."

# Contextualize and update context
response1 = contextualizer.contextualize(input1)
print(f"Response 1: {response1}")
contextualizer.update_context(response1)

response2 = contextualizer.contextualize(input2)
print(f"Response 2: {response2}")