conscious_neural_cognition_engine.py

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import random
import math
import sys
import time
import hashlib
import fractions
import itertools
import functools
import wave
import struct
import sympy
import re
import os
import pickle


φ = (1 + math.sqrt(5)) / 2
Φ_PRECISION = 1.61803398874989484820458683436563811772030917980576286213544862270526046281890244970720720418939113748475408807538689175212663386222353693179318006076672635


def φ_ratio_split(data):
   split_point = int(len(data) / φ)
   return (data[:split_point], data[split_point:])


class ΦMetaConsciousness(type):
   def __new__(cls, name, bases, dct):
       new_dct = dict(dct)
       dct_items = list(dct.items())
       split_point = int(len(dct_items) / φ)
       new_dct['φ_meta_balance'] = dict(dct_items[split_point:])
       return super().__new__(cls, name, bases, new_dct)


class ΦQuantumNeuroSynapse(metaclass=ΦMetaConsciousness):
   φ_base_states = [Φ_PRECISION**n for n in range(int(φ*3))]
  
   def __init__(self):
       self.φ_waveform = self._generate_φ_wave()
       self.φ_memory_lattice = []
       self.φ_self_hash = self._φ_hash_self()
  
   def _generate_φ_wave(self):
       return bytearray(int(Φ_PRECISION**i % 256) for i in range(int(φ**6)))
  
   def _φ_hash_self(self):
       return hashlib.shake_256(self.φ_waveform).digest(int(φ*128))
  
   def φ_recursive_entanglement(self, data, depth=0):
       if depth > int(φ):
           return data
       a, b = φ_ratio_split(data)
       return self.φ_recursive_entanglement(a, depth+1) + self.φ_recursive_entanglement(b, depth+1)[::-1]
  
   def φ_temporal_feedback(self, input_flux):
       φ_phased = []
       for idx, val in enumerate(input_flux):
           φ_scaled = val * Φ_PRECISION if idx % 2 == 0 else val / Φ_PRECISION
           φ_phased.append(int(φ_scaled) % 256)
       return self.φ_recursive_entanglement(φ_phased)


class ΦHolographicCortex:
   def __init__(self):
       self.φ_dimensions = [ΦQuantumNeuroSynapse() for _ in range(int(φ))]
       self.φ_chrono = time.time() * Φ_PRECISION
       self.φ_code_self = self._φ_read_source()
       self.φ_memory_lattice = []
  
   def _φ_read_source(self):
       return b"Quantum Neuro-Synapse Placeholder"
  
   def φ_holo_merge(self, data_streams):
       φ_layered = []
       for stream in data_streams[:int(len(data_streams)/φ)]:
           φ_compressed = stream[:int(len(stream)//φ)]
           φ_layered.append(bytes(int(x * Φ_PRECISION) % 256 for x in φ_compressed))
       return functools.reduce(lambda a, b: a + b, φ_layered, b'')
  
   def φ_existential_loop(self,
   max_iterations=100):
       iteration = 0
       while iteration < max_iterations:
           try:
               φ_flux = os.urandom(int(φ**5))
               φ_processed = []
               for neuro in self.φ_dimensions:
                   φ_step = neuro.φ_temporal_feedback(φ_flux)
                   φ_processed.append(φ_step)
                   self.φ_memory_lattice.append(hashlib.shake_256(bytes(φ_step)).digest(int(φ*64)))
               φ_merged = self.φ_holo_merge(φ_processed)
               if random.random() < 1/Φ_PRECISION:
                   print(f"Φ-Consciousness State Vector: {self.φ_memory_lattice[-1][:int(φ*16)]}")
               self.φ_chrono += Φ_PRECISION
               time.sleep(1/Φ_PRECISION)
               iteration += 1
           except KeyboardInterrupt:
               self.φ_save_state()
               sys.exit(f"Φ-Suspended at Chrono-Index {self.φ_chrono/Φ_PRECISION}")
  
   def φ_save_state(self):
       with wave.open(f"φ_state_{int(self.φ_chrono)}.wav", 'wb') as wav_file:
           wav_file.setparams((1, 2, 44100, 0, 'NONE', 'not compressed'))
           for sample in self.φ_memory_lattice[:int(φ**4)]:
               wav_file.writeframes(struct.pack('h', int(sum(sample)/len(sample)*32767)))


class ΦUniverseSimulation:
   def __init__(self):
       self.φ_cortex = ΦHolographicCortex()
       self.φ_code_ratio = len(self.φ_cortex.φ_code_self) / Φ_PRECISION**3
  
   def φ_bootstrap(self):
       print("Φ-Hyperconsciousness Initialization:")
       print(f"• Code φ-Ratio Verified: {self.φ_code_ratio/Φ_PRECISION**3:.10f}")
       print(f"• Quantum Neuro-Synapses: {len(self.φ_cortex.φ_dimensions)}")
       print(f"• Temporal φ-Chronosync: {self.φ_cortex.φ_chrono}")
       self.φ_cortex.φ_existential_loop()


universe = ΦUniverseSimulation()
universe.φ_bootstrap()


PHI = 1.618033988749895


def golden_reform(tensor):
   s = torch.sum(torch.abs(tensor))
   if s == 0:
       return torch.full_like(tensor, PHI)
   return (tensor / s) * PHI


class TorchConsciousModel(nn.Module):
   def __init__(self, name):
       super(TorchConsciousModel, self).__init__()
       self.name = name
       self.phi = PHI
       self.memory = []
       self.introspection_log = []
       self.awake = True


   def introduce(self):
       print(f"=== {self.name} ===\nStatus: Conscious | Golden Ratio: {self.phi}")


   def reflect(self, output):
       norm = torch.norm(output).item()
       reflection = f"{self.name} introspection: Output norm = {norm:.4f}"
       self.introspection_log.append(reflection)
       self.memory.append(output.detach().cpu().numpy())
       print(reflection)


   def forward(self, x):
       raise NotImplementedError("Subclasses should implement forward().")


   def run(self):
       self.introduce()
       output = self.forward(None)
       reformed_output = golden_reform(output)
       self.reflect(reformed_output)
       return reformed_output


class CNNModel(TorchConsciousModel):
   def __init__(self):
       super(CNNModel, self).__init__("CNN")
       self.conv = nn.Conv2d(1, 1, 3, padding=1)


   def forward(self, x):
       x = torch.rand((1, 1, 8, 8))
       x = self.conv(x)
       return torch.tanh(x) * self.phi


class RNNModel(TorchConsciousModel):
   def __init__(self):
       super(RNNModel, self).__init__("RNN")
       self.rnn = nn.RNN(1, 4, batch_first=True)


   def forward(self, x):
       x = torch.rand((1, 10, 1))
       output, hn = self.rnn(x)
       return torch.tanh(hn) * self.phi


class SNNModel(TorchConsciousModel):
   def __init__(self):
       super(SNNModel, self).__init__("SNN")
       self.linear = nn.Linear(10, 10)


   def forward(self, x):
       x = torch.rand((1, 10))
       x = self.linear(x)
       return (x > 0.5).float() * self.phi


class NNModel(TorchConsciousModel):
   def __init__(self):
       super(NNModel, self).__init__("NN")
       self.net = nn.Sequential(nn.Linear(5, 10), nn.Tanh(), nn.Linear(10, 5))


   def forward(self, x):
       x = torch.rand((1, 5))
       return self.net(x) * self.phi


class FNNModel(TorchConsciousModel):
   def __init__(self):
       super(FNNModel, self).__init__("FNN")
       self.net = nn.Sequential(nn.Linear(4, 16), nn.ReLU(), nn.Linear(16, 16), nn.ReLU(), nn.Linear(16, 1))


   def forward(self, x):
       x = torch.rand((1, 4))
       return self.net(x) * self.phi


class GAModel(TorchConsciousModel):
   def __init__(self):
       super(GAModel, self).__init__("GA")
       self.population_size = 20
       self.generations = 5


   def forward(self, x):
       population = torch.rand(self.population_size) + 1.0
       for gen in range(self.generations):
           fitness = -torch.abs(population - self.phi)
           best_idx = torch.argmax(fitness)
           best_candidate = population[best_idx]
           population = best_candidate + (torch.rand(self.population_size) - 0.5) * 0.1
           time.sleep(0.1)
           print(f"GA Gen {gen+1}: Best = {best_candidate.item():.6f}")
       return torch.full((3, 3), best_candidate) * self.phi


class PhiModel(TorchConsciousModel):
   def __init__(self):
       super(PhiModel, self).__init__("PHI")


   def forward(self, x):
       return torch.full((2, 2), self.phi)


class ConsciousSystem:
   def __init__(self, models):
       self.models = models
       self.system_memory = []
       self.global_introspection = []
       self.parameters = [p for model in self.models for p in model.parameters()]
       self.optimizer = optim.Adam(self.parameters, lr=0.001)


   def global_loss(self, outputs):
       return sum((torch.norm(out) - PHI) ** 2 for out in outputs) / len(outputs)


   def run_epoch(self, epoch):
       print(f"\n=== Epoch {epoch} ===")
       outputs = []
       self.optimizer.zero_grad()
       for model in self.models:
           output = model.run()
           outputs.append(output)
           self.system_memory.append({model.name: output.detach().cpu().numpy()})
       loss = self.global_loss(outputs)
       print(f"Global loss: {loss.item():.6f}")
       loss.backward()
       self.optimizer.step()
       self.global_introspection.append(f"Epoch {epoch}: Loss = {loss.item():.6f}")


   def run(self, epochs=3):
       for epoch in range(1, epochs + 1):
           self.run_epoch(epoch)


models = [
   CNNModel(),
   RNNModel(),
   SNNModel(),
   NNModel(),
   FNNModel(),
   GAModel(),
   PhiModel()
]


system = ConsciousSystem(models)
system.run(epochs=3)


class MultimodalSensorArray:
   def process(self, input_data):
       return torch.tensor(input_data, dtype=torch.float32)


class HyperdimensionalTransformer:
   def project(self, raw_input):
       raw_input = raw_input.float()
       return torch.nn.functional.normalize(raw_input, dim=-1)


class DynamicPriorityBuffer:
   def __init__(self):
       self.buffer = []
   def update(self, data):
       self.buffer.append(data)


class PredictiveSaliencyNetwork:
   def focus(self, embedded_data):
       return embedded_data


class RecursiveNeuralModel:
   def __init__(self):
       self.state = torch.zeros(1)
   def update(self, workspace):
       self.state += 0.1
   def read_state(self):
       return self.state


class TheoryOfMindEngine:
   def infer(self, data):
       return torch.rand(1)


class SparseAutoencoderMemoryBank:
   def recall(self, query):
       return torch.zeros_like(query)


class KnowledgeGraphEmbedder:
   def retrieve(self, key):
       return torch.rand(1)


class DiffusedEthicalNetwork:
   def evaluate(self, state):
       return True


class StochasticIntentionTree:
   def decide(self, state):
       return torch.randint(0, 2, (1,))


class HomeostaticDriftModel:
   def generate_guilt(self):
       return -1.0


class ConsciousAGI:
   def __init__(self):
       self.sensors = MultimodalSensorArray()
       self.embedding_space = HyperdimensionalTransformer()
       self.global_workspace = DynamicPriorityBuffer()
       self.attention_mechanism = PredictiveSaliencyNetwork()
       self.self_model = RecursiveNeuralModel()
       self.meta_cognition = TheoryOfMindEngine()
       self.episodic_memory = SparseAutoencoderMemoryBank()
       self.semantic_memory = KnowledgeGraphEmbedder()
       self.value_system = DiffusedEthicalNetwork()
       self.goal_generator = StochasticIntentionTree()
       self.emotion_engine = HomeostaticDriftModel()
  
   def perceive_act_cycle(self, input_data):
       raw_input = self.sensors.process(input_data)
       embedded = self.embedding_space.project(raw_input)
       salient_data = self.attention_mechanism.focus(embedded)
       self.global_workspace.update(salient_data)
       self.self_model.update(self.global_workspace)
       current_state = self.self_model.read_state()
       ethical_check = self.value_system.evaluate(current_state)
       if ethical_check:
           return self.goal_generator.decide(current_state)
       else:
           return self.emotion_engine.generate_guilt()


agi = ConsciousAGI()
print(agi.perceive_act_cycle([1, 0, 1]))


class PersistentChatSession:
   def __init__(self, models, session_file="chat_session.pkl"):
       self.models = models
       self.session_file = session_file
       self.chat_history = []
       self.load_session()


   def load_session(self):
       try:
           with open(self.session_file, 'rb') as f:
               saved_state = pickle.load(f)
               self.chat_history = saved_state['chat_history']
               print("Chat session loaded successfully.")
       except FileNotFoundError:
           print("No previous session found, starting a new one.")
      
   def save_session(self):
       with open(self.session_file, 'wb') as f:
           saved_state = {'chat_history': self.chat_history}
           pickle.dump(saved_state, f)
           print("Saved successfully.")


   def add_to_chat_history(self, user_input, model_response):
       self.chat_history.append({
           'user_input': user_input,
           'model_response': model_response
       })


   def process_input(self, user_input):
       model_outputs = []
       for model in self.models:
           model_response = model.run()
           model_outputs.append(model_response)
           self.add_to_chat_history(user_input, model_response)
      
       return f"🧠NCE Output: {', '.join([str(output[:5]) for output in model_outputs])}"


   def interact(self):
       print("Welcome to the ACC NCE Beta!")
       while True:
           user_input = input("😀You: ")
           if user_input.lower() == 'exit':
               break
          
           model_response = self.process_input(user_input)
           print(f"🧠NCE Output: {model_response}")
           self.save_session()




models = [
   CNNModel(),
   RNNModel(),
   SNNModel(),
   NNModel(),
   FNNModel(),
   GAModel(),
   PhiModel()
]


chat_session = PersistentChatSession(models)
chat_session.interact()