from sentence_transformers import SentenceTransformer

class AgentCore:
    def __init__(self):
        # Using a light, fast model for embeddings
        self.model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")

    def embed_text(self, text):
        """
        Convert input text into a numerical vector embedding.
        """
        return self.model.encode(text)

    def respond(self, text):
        """
        Simple placeholder reasoning for responses.
        Later you can replace with your reasoning engine or GPT API.
        """
        if "who" in text.lower():
            return "I’m Aventra, your contextual reasoning agent."
        elif "motionboys" in text.lower():
            return "MotionBoys is your core empire — fashion meets innovation."
        elif "dream" in text.lower():
            return "Dreams are memory anchors — I’m storing this in long-term context."
        else:
            return f"I understand: {text}"