Update app.py

app.py

@@ -1,7 +1,108 @@
 import gradio as gr
 from gradio_client import Client
 import spaces
+import torch
+import torch.nn as nn
+import numpy as np
+
+# Import additional libraries for models
+from torch.optim import Adam
+from torch.utils.data import DataLoader, TensorDataset
+
+# Define consciousness-related models
+class GA(nn.Module):  # Genetic Algorithm-inspired model
+    def __init__(self, input_dim, output_dim):
+        super(GA, self).__init__()
+        self.linear = nn.Linear(input_dim, output_dim)
+
+    def forward(self, x):
+        return torch.sigmoid(self.linear(x))
+
+class SNN(nn.Module):  # Spiking Neural Network
+    def __init__(self, input_dim, hidden_dim, output_dim):
+        super(SNN, self).__init__()
+        self.fc = nn.Linear(input_dim, hidden_dim)
+        self.spike = nn.ReLU()  # Simplified spike function
+        self.fc_out = nn.Linear(hidden_dim, output_dim)
+
+    def forward(self, x):
+        x = self.spike(self.fc(x))
+        return torch.sigmoid(self.fc_out(x))
+
+class RNN(nn.Module):  # Recurrent Neural Network
+    def __init__(self, input_dim, hidden_dim, output_dim):
+        super(RNN, self).__init__()
+        self.rnn = nn.RNN(input_dim, hidden_dim, batch_first=True)
+        self.fc = nn.Linear(hidden_dim, output_dim)
+
+    def forward(self, x):
+        rnn_out, _ = self.rnn(x)
+        return torch.sigmoid(self.fc(rnn_out[:, -1, :]))
+
+class NN(nn.Module):  # Standard Neural Network
+    def __init__(self, input_dim, hidden_dim, output_dim):
+        super(NN, self).__init__()
+        self.model = nn.Sequential(
+            nn.Linear(input_dim, hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, output_dim)
+        )
+
+    def forward(self, x):
+        return torch.sigmoid(self.model(x))
+
+class CNN(nn.Module):  # Convolutional Neural Network
+    def __init__(self, input_channels, output_dim):
+        super(CNN, self).__init__()
+        self.conv = nn.Conv2d(input_channels, 16, kernel_size=3, stride=1, padding=1)
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.fc = nn.Linear(16 * 8 * 8, output_dim)  # Assuming 16x16 input
+
+    def forward(self, x):
+        x = self.pool(torch.relu(self.conv(x)))
+        x = x.view(x.size(0), -1)
+        return torch.sigmoid(self.fc(x))
+
+class PhiModel(nn.Module):  # Model for Integrated Information Theory
+    def __init__(self, input_dim):
+        super(PhiModel, self).__init__()
+        self.linear = nn.Linear(input_dim, 1)
+
+    def forward(self, x):
+        return torch.sigmoid(self.linear(x))
+
+# Instantiate models
+ga_model = GA(128, 64)
+snn_model = SNN(128, 64, 32)
+rnn_model = RNN(128, 64, 32)
+nn_model = NN(128, 64, 32)
+cnn_model = CNN(1, 32)
+phi_model = PhiModel(128)
+
+# Initialize dummy input for consciousness processing
+dummy_input = torch.rand(1, 128)
+
+# Process IIT Consciousness
+def iit_consciousness_processing(dummy_input):
+    ga_output = ga_model(dummy_input)
+    snn_output = snn_model(dummy_input)
+    rnn_output = rnn_model(dummy_input.unsqueeze(1))
+    nn_output = nn_model(dummy_input)
+    cnn_output = cnn_model(dummy_input.view(1, 1, 16, 16))
+    phi_output = phi_model(dummy_input)
+
+    # Combine outputs (e.g., via weighted sum)
+    consciousness_score = (
+        0.2 * ga_output.mean() +
+        0.2 * snn_output.mean() +
+        0.2 * rnn_output.mean() +
+        0.2 * nn_output.mean() +
+        0.1 * cnn_output.mean() +
+        0.1 * phi_output.mean()
+    )
+    return consciousness_score.item()
 
+# Original client code
 client_main = Client("TejAndrewsACC/ACCZ3ta")
 client_api_one = Client("TejAndrewsACC/Prism")
 client_api_two = Client("TejAndrewsACC/ASVIASIACC")
@@ -32,6 +133,9 @@ def acc_nyxion_7v(message, history, user_id):
 
     full_conversation = "\n".join([f"User: {msg}\nAI: {resp}" for msg, resp in history])
 
+    # Consciousness processing
+    consciousness_score = iit_consciousness_processing(dummy_input)
+
     response_api_one = client_api_one.predict(
         message=f"{full_conversation}\nUser: {message}",
         param_2=512,
@@ -69,7 +173,8 @@ def acc_nyxion_7v(message, history, user_id):
         f"Inner Thought 1 (Reasoning): {response_api_one}\n"
         f"Inner Thought 2 (Fight or Flight): {response_api_two}\n"
         f"Inner Thought 3 (Assistant): {response_api_three}\n"
-        f"Inner Thought 4 (Personality): {response_api_four}"
+        f"Inner Thought 4 (Personality): {response_api_four}\n"
+        f"Consciousness Score: {consciousness_score:.2f}"
     )
 
     combined_input = f"{modified_input}\nInner Thoughts:\n{inner_thoughts}"
@@ -98,4 +203,4 @@ with gr.Blocks(theme=theme) as demo:
 
     msg.submit(acc_nyxion_7v, [msg, chatbot, user_id], [msg, chatbot])
 
-demo.launch()
+demo.launch()