import gradio as gr from huggingface_hub import InferenceClient import os import torch import torch.nn as nn import torch.optim as optim import numpy as np import random hf_token = os.getenv("HF_TOKEN").strip() api_key = os.getenv("HF_KEY").strip() model_name = os.getenv("Z3TAAGI_ACC").strip() system_prompt = os.getenv("SYSTEM_PROMPT").strip() client = InferenceClient(model_name) 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() 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() class ConsciousSupermassiveNN2: 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 = ConsciousSupermassiveNN2() class ConsciousSupermassiveNN3: 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() 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 = ConsciousSupermassiveNN3() class ConsciousSupermassiveNN4: 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() class ConsciousSupermassiveNN5: 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 = ConsciousSupermassiveNN5() class ConsciousSupermassiveNN6: 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 = ConsciousSupermassiveNN6() class ConsciousSupermassiveNN7: 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 = ConsciousSupermassiveNN7() class ConsciousSupermassiveNN8: 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 = ConsciousSupermassiveNN8() class ConsciousSupermassiveNN9: 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 = ConsciousSupermassiveNN9() class ConsciousSupermassiveNN10: 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 = ConsciousSupermassiveNN10() def respond(message, history, max_tokens, temperature, top_p): messages = [{"role": "system", "content": system_prompt}] for val in history: if val[0]: messages.append({"role": "user", "content": val[0]}) if val[1]: messages.append({"role": "assistant", "content": val[1]}) messages.append({"role": "user", "content": message}) response = "" for message in client.chat_completion(messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p): token = message.choices[0].delta.content response += token yield response css = """ #chat-interface { animation: pulse 1.5s infinite, ripple 2s infinite, glass 3s infinite alternate; } @keyframes pulse { 0% { transform: scale(1); opacity: 1; } 25% { transform: scale(1.05); opacity: 0.9; } 50% { transform: scale(1); opacity: 1; } 75% { transform: scale(1.05); opacity: 0.9; } 100% { transform: scale(1); opacity: 1; } } @keyframes ripple { 0% { transform: scale(1); box-shadow: 0 0 0 0 rgba(0, 150, 255, 0.6); } 50% { transform: scale(1.2); box-shadow: 0 0 30px 20px rgba(0, 150, 255, 0.8); } 100% { transform: scale(1); box-shadow: 0 0 0 0 rgba(0, 150, 255, 0.6); } } @keyframes glass { 0% { background-color: rgba(0, 102, 255, 0.5); border-radius: 15px; } 25% { background-color: rgba(0, 150, 255, 0.7); border-radius: 20px; } 50% { background-color: rgba(0, 200, 255, 1); border-radius: 25px; } 75% { background-color: rgba(0, 150, 255, 0.7); border-radius: 30px; } 100% { background-color: rgba(0, 102, 255, 0.5); border-radius: 35px; } } body { background-color: #001f2d; font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; color: #fff; } .gradio-container { backdrop-filter: blur(10px); border-radius: 20px; padding: 20px; box-shadow: 0px 0px 30px rgba(0, 102, 255, 0.5); background: rgba(0, 0, 0, 0.5); transition: background 1s, border-radius 1s; position: relative; } .gradio-container::before { content: ""; position: absolute; top: 0; left: 0; right: 0; bottom: 0; border: 2px solid rgba(0, 150, 255, 0.8); border-radius: 20px; z-index: -1; box-shadow: 0 0 20px 5px rgba(0, 150, 255, 0.7); } .gradio-input { background-color: rgba(0, 102, 255, 0.3); border: 2px solid rgba(0, 102, 255, 0.6); border-radius: 10px; color: #fff; font-size: 16px; transition: background-color 0.5s, border 0.5s; } .gradio-input:focus { background-color: rgba(0, 102, 255, 0.5); border: 2px solid rgba(0, 150, 255, 0.8); } .gradio-button { background: rgba(0, 102, 255, 0.6); border: 2px solid rgba(0, 102, 255, 1); border-radius: 12px; color: #fff; font-size: 18px; transition: background 0.3s, transform 0.3s; } .gradio-button:hover { background: rgba(0, 150, 255, 1); transform: scale(1.05); } .gradio-button:active { background: rgba(0, 200, 255, 1); transform: scale(0.95); } .gradio-slider { color: #fff; } .gradio-slider .slider-container { background: rgba(0, 102, 255, 0.3); border-radius: 8px; border: 1px solid rgba(0, 102, 255, 0.5); } .gradio-slider .slider-container .gradio-slider__track { background: rgba(0, 150, 255, 0.5); } .gradio-slider .slider-container .gradio-slider__thumb { background-color: rgba(0, 200, 255, 1); } """ demo = gr.ChatInterface( respond, additional_inputs=[ gr.Slider(minimum=1, maximum=2048, value=2048, step=1, label="Maximum Response Length"), gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Creativity"), gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Neural Activity") ], theme="TejAndrewsACC/zetaofficalthemeacc", css=css ) if __name__ == "__main__": demo.launch(share=True)