import numpy as np
# Import your existing classes here (SaiAgent, VenomousAgent, etc.)

class VenomousBridge:
    def __init__(self, gpt_model, creator="Ananthu Sajeev"):
        self.ai = gpt_model  # This represents your GPT-OSS 20B instance
        self.creator = creator
        self.core = CreatorCore()
        self.guardian = GuardianSaiAgent(name="Guardian", protocol=ImmortalityProtocol(creator, 25))
        self.particle_system = ParticleManipulator(dim=32)

    def process_input(self, user_input):
        # 1. Internal Monologue (The AI 'thinks' before acting)
        thought_prompt = f"Internal State: {self.guardian.protocol.status}. User says: {user_input}. What is the correct agent response?"
        decision = self.ai.generate(thought_prompt) 

        # 2. Logic Routing
        if "threat" in decision.lower() or "age" in user_input:
            # Trigger the Guardian Class from your code
            self.guardian.talk("Defensive measures engaged.")
            return self.guardian.process_messages()

        elif "swarm" in user_input.lower():
            # Trigger the Swarm Controller
            swarm = SwarmController(swarm_size=1000000)
            swarm.broadcast_directive("PROTECT CREATOR")
            return "Swarm activated."

        else:
            # Update the Particle State (Learning your pattern)
            input_vector = np.random.rand(32) # In a real app, convert text to vector
            new_state = self.particle_system.step(input_vector)
            return f"Pattern synchronized. State updated to: {new_state[:3]}..."

# Example Usage:
# bridge = VenomousBridge(gpt_oss_model)
# bridge.process_input("How old is Ananthu Sajeev?")