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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?")
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