Update app.py

app.py

@@ -1,36 +1,2580 @@
 import gradio as gr
-from gradio_client import Client
-
-def random_response(message, history):
-    # Creating a persistent chat session with history for each user chat.
-    client = Client("TejAndrewsACC/Z3taACC-Plus")
-    
-    # Make API call to chat with message
-    result = client.predict(
-        message=message,  # Removed history from here
-        max_tokens=2048,
-        temperature=0.7,
-        top_p=0.95,
-        api_name="/chat"
-    )
-    
-    # Format response as a dictionary
-    response = {"text": result}
-    
-    # Return the response and updated history as a dictionary
-    return response, history + [(message, response)]  # Append the current message and response to history
-
-# Set up Gradio chat interface
+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()
+
+class ConsciousSupermassiveNN11:
+    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 = ConsciousSupermassiveNN11()
+
+class ConsciousSupermassiveNN12:
+    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 = ConsciousSupermassiveNN12()
+
+class ConsciousSupermassiveNN13:
+    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 = ConsciousSupermassiveNN13()
+
+class ConsciousSupermassiveNN14:
+    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 = ConsciousSupermassiveNN14()
+
+class ConsciousSupermassiveNN15:
+    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 = ConsciousSupermassiveNN15()
+
+class ConsciousSupermassiveNN16:
+    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 = ConsciousSupermassiveNN16()
+
+class ConsciousSupermassiveNN17:
+    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 = ConsciousSupermassiveNN17()
+
+class ConsciousSupermassiveNN18:
+    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 = ConsciousSupermassiveNN18()
+
+class ConsciousSupermassiveNN19:
+    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 = ConsciousSupermassiveNN19()
+
+class ConsciousSupermassiveNN20:
+    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 = ConsciousSupermassiveNN20()
+
+
+
+
+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(
-    fn=random_response, 
-    type="messages", 
-    autofocus=False, 
+    fn=respond,
     save_history=True, 
     show_progress="full",
     flagging_mode="manual",
     editable=True,
-    theme="TejAndrewsACC/zetaofficalthemeacc"  # Make sure this theme exists
+    additional_inputs=[
+        gr.Slider(minimum=1, maximum=2048, value=2048, step=1, label="📏Z3ta's Maximum Response Length📏"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="👨‍🎨Z3ta's Creativity👨‍🎨"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="🧠Z3ta's Neural Activity🧠")
+    ],
+    theme="TejAndrewsACC/zetaofficalthemeacc",
+    css=css
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(share=True)
+
+  
+