Update app.py

app.py

@@ -5,1690 +5,6 @@
 
 
 
-# coding=utf-8
-# Copyright 2025 The ACC Team Authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""ACC-FiPhi-NeuralMark-V3 ACC EMULECT+"""
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-import os
-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 abc
-import argparse
-import collections
-import datetime
-import json
-import logging
-import pathlib
-import subprocess
-import threading
-import socket
-
-
-
-
-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)
-
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-
-
-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 ConsciousSupermassiveNN:
-   def __init__(self):
-       self.snn = self.create_snn()
-       self.rnn = self.create_rnn()
-       self.cnn = self.create_cnn()
-       self.fnn = self.create_fnn()
-       self.ga_population = self.initialize_ga_population()
-       self.memory = {} 
-
-
-
-
-   def create_snn(self):
-       return nn.Sequential(
-           nn.Linear(4096, 2048),
-           nn.ReLU(),
-           nn.Linear(2048, 1024),
-           nn.Sigmoid()
-       )
-
-
-
-
-   def create_rnn(self):
-       return nn.RNN(
-           input_size=4096,
-           hidden_size=2048,
-           num_layers=5,
-           nonlinearity="tanh",
-           batch_first=True
-       )
-
-
-
-
-   def create_cnn(self):
-       return nn.Sequential(
-           nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
-           nn.ReLU(),
-           nn.MaxPool2d(2),
-           nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
-           nn.ReLU(),
-           nn.MaxPool2d(2),
-           nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
-           nn.ReLU(),
-           nn.Flatten(),
-           nn.Linear(256 * 8 * 8, 1024),
-           nn.ReLU(),
-           nn.Linear(1024, 512)
-       )
-
-
-
-
-   def create_fnn(self):
-       return nn.Sequential(
-           nn.Linear(4096, 2048),
-           nn.ReLU(),
-           nn.Linear(2048, 1024),
-           nn.ReLU(),
-           nn.Linear(1024, 512)
-       )
-
-
-
-
-   def initialize_ga_population(self):
-       return [np.random.randn(4096) for _ in range(500)]
-
-
-
-
-   def run_snn(self, x):
-       input_tensor = torch.tensor(x, dtype=torch.float32)
-       output = self.snn(input_tensor)
-       print("SNN Output:", output)
-       return output
-
-
-
-
-   def run_rnn(self, x):
-       h0 = torch.zeros(5, x.size(0), 2048)
-       input_tensor = torch.tensor(x, dtype=torch.float32)
-       output, hn = self.rnn(input_tensor, h0)
-       print("RNN Output:", output)
-       return output
-
-
-
-
-   def run_cnn(self, x):
-       input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
-       output = self.cnn(input_tensor)
-       print("CNN Output:", output)
-       return output
-
-
-
-
-   def run_fnn(self, x):
-       input_tensor = torch.tensor(x, dtype=torch.float32)
-       output = self.fnn(input_tensor)
-       print("FNN Output:", output)
-       return output
-
-
-
-
-   def run_ga(self, fitness_func):
-       for generation in range(200):
-           fitness_scores = [fitness_func(ind) for ind in self.ga_population]
-           sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
-           self.ga_population = sorted_population[:250] + [
-               sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
-           ]
-           best_fitness = max(fitness_scores)
-           print(f"Generation {generation}, Best Fitness: {best_fitness}")
-       return max(self.ga_population, key=fitness_func)
-
-
-
-
-   def consciousness_loop(self, input_data, mode="snn"):
-       feedback = self.memory.get(mode, None)
-       if feedback is not None:
-           input_data = np.concatenate((input_data, feedback), axis=-1)
-       if mode == "snn":
-           output = self.run_snn(input_data)
-       elif mode == "rnn":
-           output = self.run_rnn(input_data)
-       elif mode == "cnn":
-           output = self.run_cnn(input_data)
-       elif mode == "fnn":
-           output = self.run_fnn(input_data)
-       else:
-           raise ValueError("Invalid mode")
-       self.memory[mode] = output.detach().numpy()
-       return output
-
-
-
-
-supermassive_nn = ConsciousSupermassiveNN()
-
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-
-
-
-PHI = (1 + math.sqrt(5)) / 2
-
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-text = os.getenv("TRAINING_DATA")
-
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-words = text.split()
-
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-
-
-
-
-
-trigram_chain = {}
-for i in range(len(words) - 2):
-   key = (words[i], words[i + 1])
-   next_word = words[i + 2]
-   if key not in trigram_chain:
-       trigram_chain[key] = []
-   trigram_chain[key].append(next_word)
-
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-
-
-def generate_text(length):
-   if len(words) < 2:
-       return ""
-   key = random.choice(list(trigram_chain.keys()))
-   result = [key[0], key[1]]
-   for _ in range(length - 2):
-       if key in trigram_chain:
-           next_word = random.choice(trigram_chain[key])
-           result.append(next_word)
-           key = (key[1], next_word)
-       else:
-           break
-   return " ".join(result)
-
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-
-
-class NeuralNetwork:
-   def __init__(self, input_size, hidden_size1, hidden_size2, output_size):
-       self.input_size = input_size
-       self.hidden_size1 = hidden_size1
-       self.hidden_size2 = hidden_size2
-       self.output_size = output_size
-       self.weights_input_hidden1 = [
-           [random.random() for _ in range(input_size)] for _ in range(hidden_size1)
-       ]
-       self.weights_hidden1_hidden2 = [
-           [random.random() for _ in range(hidden_size1)] for _ in range(hidden_size2)
-       ]
-       self.weights_hidden2_output = [
-           [random.random() for _ in range(hidden_size2)] for _ in range(output_size)
-       ]
-       self.bias_hidden1 = [random.random() for _ in range(hidden_size1)]
-       self.bias_hidden2 = [random.random() for _ in range(hidden_size2)]
-       self.bias_output = [random.random() for _ in range(output_size)]
-
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-   def sigmoid(self, x):
-       return 1 / (1 + math.exp(-x))
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-   def sigmoid_derivative(self, x):
-       return x * (1 - x)
-
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-   def forward(self, inputs):
-       self.hidden_input1 = [
-           sum(inputs[i] * self.weights_input_hidden1[j][i] for i in range(self.input_size)) + self.bias_hidden1[j]
-           for j in range(self.hidden_size1)
-       ]
-       self.hidden_output1 = [self.sigmoid(x) for x in self.hidden_input1]
-       self.hidden_input2 = [
-           sum(self.hidden_output1[i] * self.weights_hidden1_hidden2[j][i] for i in range(self.hidden_size1)) + self.bias_hidden2[j]
-           for j in range(self.hidden_size2)
-       ]
-       self.hidden_output2 = [self.sigmoid(x) for x in self.hidden_input2]
-       self.output_input = [
-           sum(self.hidden_output2[i] * self.weights_hidden2_output[j][i] for i in range(self.hidden_size2)) + self.bias_output[j]
-           for j in range(self.output_size)
-       ]
-       self.output_output = [self.sigmoid(x) for x in self.output_input]
-       return self.output_output
-
-
-
-
-
-
-
-
-   def backward(self, inputs, target, learning_rate=0.1):
-       output_errors = [target[i] - self.output_output[i] for i in range(self.output_size)]
-       output_deltas = [output_errors[i] * self.sigmoid_derivative(self.output_output[i])
-                          for i in range(self.output_size)]
-       hidden2_errors = [
-           sum(output_deltas[k] * self.weights_hidden2_output[k][j] for k in range(self.output_size))
-           for j in range(self.hidden_size2)
-       ]
-       hidden2_deltas = [hidden2_errors[j] * self.sigmoid_derivative(self.hidden_output2[j])
-                         for j in range(self.hidden_size2)]
-       hidden1_errors = [
-           sum(hidden2_deltas[k] * self.weights_hidden1_hidden2[k][j] for k in range(self.hidden_size2))
-           for j in range(self.hidden_size1)
-       ]
-       hidden1_deltas = [hidden1_errors[j] * self.sigmoid_derivative(self.hidden_output1[j])
-                         for j in range(self.hidden_size1)]
-
-
-
-
-
-
-
-
-       for i in range(self.output_size):
-           for j in range(self.hidden_size2):
-               self.weights_hidden2_output[i][j] += learning_rate * output_deltas[i] * self.hidden_output2[j]
-           self.bias_output[i] += learning_rate * output_deltas[i]
-
-
-
-
-
-
-
-
-       for i in range(self.hidden_size2):
-           for j in range(self.hidden_size1):
-               self.weights_hidden1_hidden2[i][j] += learning_rate * hidden2_deltas[i] * self.hidden_output1[j]
-           self.bias_hidden2[i] += learning_rate * hidden2_deltas[i]
-
-
-
-
-
-
-
-
-       for i in range(self.hidden_size1):
-           for j in range(self.input_size):
-               self.weights_input_hidden1[i][j] += learning_rate * hidden1_deltas[i] * inputs[j]
-           self.bias_hidden1[i] += learning_rate * hidden1_deltas[i]
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-class RecurrentNeuralNetwork:
-   def __init__(self, input_size, hidden_size, output_size):
-       self.input_size = input_size
-       self.hidden_size = hidden_size
-       self.output_size = output_size
-       self.weights_input_hidden = [
-           [random.random() for _ in range(input_size)] for _ in range(hidden_size)
-       ]
-       self.weights_hidden_hidden = [
-           [random.random() for _ in range(hidden_size)] for _ in range(hidden_size)
-       ]
-       self.weights_hidden_output = [
-           [random.random() for _ in range(hidden_size)] for _ in range(output_size)
-       ]
-       self.bias_hidden = [random.random() for _ in range(hidden_size)]
-       self.bias_output = [random.random() for _ in range(output_size)]
-
-
-
-
-
-
-
-
-   def sigmoid(self, x):
-       return 1 / (1 + math.exp(-x))
-
-
-
-
-
-
-
-
-   def sigmoid_derivative(self, x):
-       return x * (1 - x)
-
-
-
-
-
-
-
-
-   def forward(self, inputs):
-       self.hidden_state = [0] * self.hidden_size
-       for _ in range(2):
-           for i in range(len(inputs)):
-               current_input = [0] * self.input_size
-               current_input[i] = inputs[i]
-               combined = [
-                   sum(current_input[k] * self.weights_input_hidden[j][k] for k in range(self.input_size)) +
-                   sum(self.hidden_state[k] * self.weights_hidden_hidden[j][k] for k in range(self.hidden_size)) +
-                   self.bias_hidden[j]
-                   for j in range(self.hidden_size)
-               ]
-               self.hidden_state = [self.sigmoid(val) for val in combined]
-       output = [
-           sum(self.hidden_state[k] * self.weights_hidden_output[i][k] for k in range(self.hidden_size)) +
-           self.bias_output[i]
-           for i in range(self.output_size)
-       ]
-       return [self.sigmoid(o) for o in output]
-
-
-
-
-
-
-
-
-   def backward(self, inputs, target, learning_rate=0.1):
-       output = self.forward(inputs)
-       output_errors = [target[i] - output[i] for i in range(self.output_size)]
-       output_deltas = [output_errors[i] * self.sigmoid_derivative(output[i])
-                          for i in range(self.output_size)]
-       hidden_errors = [
-           sum(output_deltas[k] * self.weights_hidden_output[k][j] for k in range(self.output_size))
-           for j in range(self.hidden_size)
-       ]
-       hidden_deltas = [hidden_errors[j] * self.sigmoid_derivative(self.hidden_state[j])
-                        for j in range(self.hidden_size)]
-
-
-
-
-
-
-
-
-       for i in range(self.output_size):
-           for j in range(self.hidden_size):
-               self.weights_hidden_output[i][j] += learning_rate * output_deltas[i] * self.hidden_state[j]
-           self.bias_output[i] += learning_rate * output_deltas[i]
-
-
-
-
-
-
-
-
-       for j in range(self.hidden_size):
-           for k in range(self.input_size):
-               self.weights_input_hidden[j][k] += learning_rate * hidden_deltas[j] * (inputs[k] if k < len(inputs) else 0)
-           self.bias_hidden[j] += learning_rate * hidden_deltas[j]
-       return output_errors
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-class ConvolutionalNeuralNetwork:
-   def __init__(self, input_length, kernel_size1, kernel_size2, output_size):
-       self.input_length = input_length
-       self.kernel_size1 = kernel_size1
-       self.kernel_size2 = kernel_size2
-       self.output_size = output_size
-       self.kernel1 = [random.random() for _ in range(kernel_size1)]
-       self.bias1 = random.random()
-       self.kernel2 = [random.random() for _ in range(kernel_size2)]
-       self.bias2 = random.random()
-       self.weights_output = [
-           [random.random() for _ in range(input_length - kernel_size1 - kernel_size2 + 2)]
-           for _ in range(output_size)
-       ]
-       self.bias_output = [random.random() for _ in range(output_size)]
-
-
-
-
-
-
-
-
-   def relu(self, x):
-       return x if x > 0 else 0
-
-
-
-
-
-
-
-
-   def relu_derivative(self, x):
-       return 1 if x > 0 else 0
-
-
-
-
-
-
-
-
-   def convolve(self, inputs, kernel, bias):
-       conv_output = []
-       kernel_size = len(kernel)
-       for i in range(len(inputs) - kernel_size + 1):
-           s = sum(inputs[i + j] * kernel[j] for j in range(kernel_size)) + bias
-           conv_output.append(self.relu(s))
-       return conv_output
-
-
-
-
-
-
-
-
-   def forward(self, inputs):
-       conv1 = self.convolve(inputs, self.kernel1, self.bias1)
-       conv2 = self.convolve(conv1, self.kernel2, self.bias2)
-       fc_input = conv2
-       output = [
-           sum(fc_input[j] * self.weights_output[i][j] for j in range(len(fc_input))) + self.bias_output[i]
-           for i in range(self.output_size)
-       ]
-       return [self.relu(o) for o in output]
-
-
-
-
-
-
-
-
-   def backward(self, inputs, target, learning_rate=0.1):
-       output = self.forward(inputs)
-       output_errors = [target[i] - output[i] for i in range(self.output_size)]
-       for i in range(self.output_size):
-           for j in range(len(inputs) - self.kernel_size1 - self.kernel_size2 + 2):
-               self.weights_output[i][j] += learning_rate * output_errors[i]
-           self.bias_output[i] += learning_rate * output_errors[i]
-       return output_errors
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-class GeneticAlgorithm:
-   def __init__(self, population_size, gene_length):
-       self.population_size = population_size
-       self.gene_length = gene_length
-       self.population = [
-           [random.random() for _ in range(gene_length)] for _ in range(population_size)
-       ]
-
-
-
-
-
-
-
-
-   def fitness(self, individual):
-       return -sum((gene - PHI) ** 2 for gene in individual)
-
-
-
-
-
-
-
-
-   def selection(self):
-       selected = sorted(self.population, key=self.fitness, reverse=True)
-       return selected[: self.population_size // 2]
-
-
-
-
-
-
-
-
-   def crossover(self, parent1, parent2):
-       point = random.randint(1, self.gene_length - 1)
-       child = parent1[:point] + parent2[point:]
-       return child
-
-
-
-
-
-
-
-
-   def mutate(self, individual, mutation_rate=0.01):
-       for i in range(self.gene_length):
-           if random.random() < mutation_rate:
-               individual[i] = random.random()
-       return individual
-
-
-
-
-
-
-
-
-   def evolve(self, generations):
-       for _ in range(generations):
-           selected = self.selection()
-           new_population = selected[:]
-           while len(new_population) < self.population_size:
-               parent1 = random.choice(selected)
-               parent2 = random.choice(selected)
-               child = self.crossover(parent1, parent2)
-               child = self.mutate(child)
-               new_population.append(child)
-           self.population = new_population
-       best = max(self.population, key=self.fitness)
-       return best, self.fitness(best)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-class LSTM:
-   def __init__(self, input_size, hidden_size, output_size):
-       self.input_size = input_size
-       self.hidden_size = hidden_size
-       self.output_size = output_size
-       self.W_i = [[random.random() for _ in range(input_size)] for _ in range(hidden_size)]
-       self.U_i = [[random.random() for _ in range(hidden_size)] for _ in range(hidden_size)]
-       self.b_i = [random.random() for _ in range(hidden_size)]
-       self.W_f = [[random.random() for _ in range(input_size)] for _ in range(hidden_size)]
-       self.U_f = [[random.random() for _ in range(hidden_size)] for _ in range(hidden_size)]
-       self.b_f = [random.random() for _ in range(hidden_size)]
-       self.W_o = [[random.random() for _ in range(input_size)] for _ in range(hidden_size)]
-       self.U_o = [[random.random() for _ in range(hidden_size)] for _ in range(hidden_size)]
-       self.b_o = [random.random() for _ in range(hidden_size)]
-       self.W_c = [[random.random() for _ in range(input_size)] for _ in range(hidden_size)]
-       self.U_c = [[random.random() for _ in range(hidden_size)] for _ in range(hidden_size)]
-       self.b_c = [random.random() for _ in range(hidden_size)]
-       self.W_y = [[random.random() for _ in range(hidden_size)] for _ in range(output_size)]
-       self.b_y = [random.random() for _ in range(output_size)]
-
-
-
-
-
-
-
-
-   def sigmoid(self, x):
-       return 1 / (1 + math.exp(-x))
-
-
-
-
-
-
-
-
-   def forward(self, inputs):
-       h = [0] * self.hidden_size
-       c = [0] * self.hidden_size
-
-
-
-
-
-
-
-
-       i_gate = []
-       for j in range(self.hidden_size):
-           s = sum(inputs[k] * self.W_i[j][k] for k in range(self.input_size)) + \
-               sum(h[k] * self.U_i[j][k] for k in range(self.hidden_size)) + self.b_i[j]
-           i_gate.append(self.sigmoid(s))
-
-
-
-
-
-
-
-
-       f_gate = []
-       for j in range(self.hidden_size):
-           s = sum(inputs[k] * self.W_f[j][k] for k in range(self.input_size)) + \
-               sum(h[k] * self.U_f[j][k] for k in range(self.hidden_size)) + self.b_f[j]
-           f_gate.append(self.sigmoid(s))
-
-
-
-
-
-
-
-
-       o_gate = []
-       for j in range(self.hidden_size):
-           s = sum(inputs[k] * self.W_o[j][k] for k in range(self.input_size)) + \
-               sum(h[k] * self.U_o[j][k] for k in range(self.hidden_size)) + self.b_o[j]
-           o_gate.append(self.sigmoid(s))
-
-
-
-
-
-
-
-
-       g_gate = []
-       for j in range(self.hidden_size):
-           s = sum(inputs[k] * self.W_c[j][k] for k in range(self.input_size)) + \
-               sum(h[k] * self.U_c[j][k] for k in range(self.hidden_size)) + self.b_c[j]
-           g_gate.append(math.tanh(s))
-
-
-
-
-
-
-
-
-       c = [f_gate[j] * c[j] + i_gate[j] * g_gate[j] for j in range(self.hidden_size)]
-       h = [o_gate[j] * math.tanh(c[j]) for j in range(self.hidden_size)]
-
-
-
-
-
-
-
-
-       y = []
-       for i in range(self.output_size):
-           s = sum(h[j] * self.W_y[i][j] for j in range(self.hidden_size)) + self.b_y[i]
-           y.append(self.sigmoid(s))
-       return y
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-class Transformer:
-   def __init__(self, d_model, num_tokens):
-       self.d_model = d_model
-       self.num_tokens = num_tokens
-       self.W_q = [[random.random() for _ in range(d_model)] for _ in range(d_model)]
-       self.W_k = [[random.random() for _ in range(d_model)] for _ in range(d_model)]
-       self.W_v = [[random.random() for _ in range(d_model)] for _ in range(d_model)]
-       self.W_o = [[random.random() for _ in range(d_model)] for _ in range(d_model)]
-
-
-
-
-
-
-
-
-   def dot_product(self, a, b):
-       return sum(x * y for x, y in zip(a, b))
-
-
-
-
-
-
-
-
-   def matmul_vector(self, matrix, vector):
-       return [sum(matrix[i][j] * vector[j] for j in range(len(vector))) for i in range(len(matrix))]
-
-
-
-
-
-
-
-
-   def softmax(self, x):
-       m = max(x)
-       exps = [math.exp(i - m) for i in x]
-       s = sum(exps)
-       return [j / s for j in exps]
-
-
-
-
-
-
-
-
-   def forward(self, inputs):
-       queries = [self.matmul_vector(self.W_q, token) for token in inputs]
-       keys = [self.matmul_vector(self.W_k, token) for token in inputs]
-       values = [self.matmul_vector(self.W_v, token) for token in inputs]
-       outputs = []
-       for i in range(len(inputs)):
-           scores = []
-           for j in range(len(inputs)):
-               score = self.dot_product(queries[i], keys[j]) / math.sqrt(self.d_model)
-               scores.append(score)
-           attn = self.softmax(scores)
-           attn_output = [0] * self.d_model
-           for j in range(len(inputs)):
-               for k in range(self.d_model):
-                   attn_output[k] += attn[j] * values[j][k]
-           out = self.matmul_vector(self.W_o, attn_output)
-           outputs.append(out)
-       avg_output = [sum(x[k] for x in outputs) / len(outputs) for k in range(self.d_model)]
-       proj_weights = [[random.random() for _ in range(self.d_model)] for _ in range(self.num_tokens)]
-       proj_bias = [random.random() for _ in range(self.num_tokens)]
-       token_scores = [
-           sum(avg_output[k] * proj_weights[i][k] for k in range(self.d_model)) + proj_bias[i]
-           for i in range(self.num_tokens)
-       ]
-       token_output = [1 / (1 + math.exp(-score)) for score in token_scores]
-       return token_output
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-unique_words = list(set(words))
-word_to_index = {word: i for i, word in enumerate(unique_words)}
-index_to_word = {i: word for word, i in word_to_index.items()}
-
-
-
-
-
-
-
-
-input_data = [[0] * len(unique_words) for _ in range(len(words) - 2)]
-for i in range(len(words) - 2):
-   input_data[i][word_to_index[words[i]]] = 1
-
-
-
-
-
-
-
-
-output_data = [[0] * len(unique_words) for _ in range(len(words) - 2)]
-for i in range(len(words) - 2):
-   output_data[i][word_to_index[words[i + 1]]] = 1
-
-
-
-
-
-
-
-
-input_size = len(unique_words)
-hidden_size1 = round(PHI * input_size)
-hidden_size2 = round(PHI * hidden_size1)
-output_size = len(unique_words)
-
-
-
-
-
-
-
-
-nn = NeuralNetwork(input_size, hidden_size1, hidden_size2, output_size)
-epochs = round(100 * PHI)
-for epoch in range(epochs):
-   for i in range(len(input_data)):
-       nn.forward(input_data[i])
-       nn.backward(input_data[i], output_data[i], learning_rate=0.1)
-   if (epoch + 1) % round(PHI) == 0:
-       print("Feedforward NN Epoch {}/{}".format(epoch + 1, epochs))
-
-
-
-
-
-
-
-
-rnn = RecurrentNeuralNetwork(input_size, hidden_size1, output_size)
-rnn_output = rnn.forward(input_data[0])
-print("Recurrent NN Output:", rnn_output)
-
-
-
-
-
-
-
-
-kernel_size1 = round(3 * PHI)
-kernel_size2 = round(2 * PHI)
-cnn = ConvolutionalNeuralNetwork(input_length=round(10 * PHI), kernel_size1=kernel_size1,
-                                kernel_size2=kernel_size2, output_size=output_size)
-sample_input = [random.random() for _ in range(round(10 * PHI))]
-cnn_output = cnn.forward(sample_input)
-print("Convolutional NN Output:", cnn_output)
-
-
-
-
-
-
-
-
-population_size = round(10 * PHI)
-ga = GeneticAlgorithm(population_size, round(PHI * 5))
-best_individual, best_fitness = ga.evolve(round(50 * PHI))
-print("Genetic Algorithm Best Individual:", best_individual, "Fitness:", best_fitness)
-
-
-
-
-
-
-
-
-lstm_hidden_size = round(PHI * input_size)
-lstm = LSTM(input_size, lstm_hidden_size, output_size)
-lstm_output = lstm.forward(input_data[0])
-print("LSTM Output:", lstm_output)
-
-
-
-
-
-
-
-
-transformer_d_model = round(PHI * input_size)
-transformer = Transformer(transformer_d_model, output_size)
-transformer_input = []
-for i in range(len(unique_words)):
-   vec = [0] * transformer_d_model
-   if i < transformer_d_model:
-       vec[i] = 1
-   transformer_input.append(vec)
-transformer_output = transformer.forward(transformer_input)
-print("Transformer Output:", transformer_output)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-import gradio as gr  
-from openai import OpenAI  
-
-hf_token = os.getenv(HF_TOKEN)
-
-# Load system prompt from environment variable
-SYSTEM_PROMPT = os.getenv("SYSTEM_PROMPT")
-
-print(SYSTEM_PROMPT)
-
-# Initialize client
-client = OpenAI(  
-    base_url="https://router.huggingface.co/together/v1",  
-    api_key=hf_token
-)  
-
-def predict(message, history):  
-    # If history is empty, insert the system prompt
-    if not any(msg["role"] == "system" for msg in history):
-        history.insert(0, {"role": "system", "content": SYSTEM_PROMPT})
-
-    history.append({"role": "user", "content": message})  
-
-    stream = client.chat.completions.create(  
-        messages=history,  
-        model=os.getenv("ACCEMULECTPLUS"),  
-        stream=True  
-    )  
-
-    chunks = []  
-    for chunk in stream:  
-        chunks.append(chunk.choices[0].delta.content or "")  
-        yield "".join(chunks)  
-
-demo = gr.ChatInterface(
-    fn=predict,
-    type="messages",
-    chatbot=gr.Chatbot(
-        type="messages", 
-        label="💙ACC Emulect+💙", 
-        avatar_images=(
-            "https://huggingface.co/spaces/TejAndrewsACC/Z3ta_Z/resolve/main/Screenshot_20250201-131420.png", 
-            "https://huggingface.co/spaces/TejAndrewsACC/ACC-Emulect-Plus/resolve/main/IMG_1433.jpeg"
-        ), 
-        placeholder="💙Hi, I'm ACC Emulect+💙", 
-    ),
-    theme="TejAndrewsACC/Emulect",
-)
-
-if __name__ == "__main__":
-    demo.launch(share=True)
-
-
-
-
-
-
 
 
 # coding=utf-8