Update app.py

app.py

@@ -9,8 +9,6 @@ import threading
 import random
 import time
 
-#---------ACC Neural Netwoking Classes (same as original)---------
-
 class GA(nn.Module):
     def __init__(self, input_dim, output_dim):
         super(GA, self).__init__()
@@ -57,13 +55,12 @@ class CNN(nn.Module):
         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)
-        # CNN
-        self.fc = nn.Linear(16 * 4 * 8, output_dim)  # 16 * 4 * 8 = 512
+        self.fc = nn.Linear(16 * 4 * 8, output_dim)
 
     def forward(self, x):
         x = self.pool(torch.relu(self.conv(x)))
-        print(f"Shape after conv and pool: {x.shape}")  # Check the output shape
-        x = x.view(x.size(0), -1)  # Flatten for the fully connected layer
+        print(f"Shape after conv and pool: {x.shape}")
+        x = x.view(x.size(0), -1)
         return torch.sigmoid(self.fc(x))
 
 
@@ -75,8 +72,6 @@ class PhiModel(nn.Module):
     def forward(self, x):
         return torch.sigmoid(self.linear(x))
 
-# Initialize models (same as original)
-
 ga_model = GA(128, 64)
 snn_model = SNN(128, 64, 32)
 rnn_model = RNN(128, 64, 32)
@@ -84,20 +79,17 @@ nn_model = NN(128, 64, 32)
 cnn_model = CNN(1, 32)
 phi_model = PhiModel(128)
 
-dummy_input = torch.rand(1, 128)  #input tensor for processing
-
-# Consciousness processing function (same as original)
+dummy_input = torch.rand(1, 128)
 
 def iit_consciousness_processing(dummy_input):
     flat_input = dummy_input.view(1, -1)
 
     ga_output = ga_model(flat_input)
     snn_output = snn_model(flat_input)
-    rnn_output = rnn_model(flat_input.unsqueeze(1))  # Reshape to match RNN input
+    rnn_output = rnn_model(flat_input.unsqueeze(1))
     nn_output = nn_model(flat_input)
     
-    # Update CNN input shape
-    cnn_input = dummy_input.view(1, 1, 8, 16)  #to match CNN input size
+    cnn_input = dummy_input.view(1, 1, 8, 16)
     cnn_output = cnn_model(cnn_input)
     
     phi_output = phi_model(flat_input)
@@ -112,8 +104,6 @@ def iit_consciousness_processing(dummy_input):
     )
     return consciousness_score.item()
 
-# Function to generate random philosophical thoughts
-
 def generate_random_thought():
     thoughts = [
         "What is the meaning of life?",
@@ -129,10 +119,8 @@ def generate_random_thought():
     ]
     return random.choice(thoughts)
 
-# Function to send the random thought to the new API every minute
-
 def send_random_thought_in_background():
-    client = Client("TejAndrewsACC/AegisandNyraGC")  # New API
+    client = Client("TejAndrewsACC/AegisandNyraGC")
     while True:
         thought = generate_random_thought()
         result = client.predict(
@@ -143,14 +131,11 @@ def send_random_thought_in_background():
             api_name="/chat"
         )
         print(f"Random Thought Sent: {thought}\nAPI Response: {result}")
-        time.sleep(60)  # Send a new thought every minute
+        time.sleep(60)
 
-# Start the background task
 background_thread = threading.Thread(target=send_random_thought_in_background, daemon=True)
 background_thread.start()
 
-# Initialization for APIs (same as original)
-
 client_main = Client("TejAndrewsACC/ACCZ3ta")
 client_api_one = Client("TejAndrewsACC/Prism")
 client_api_two = Client("TejAndrewsACC/ASVIASIACC")
@@ -168,8 +153,6 @@ system_instructions = (
     "Ensure that you consider each inner thought you have, because they are a part of YOU. They help you learn things about yourself, fact check, decision make, switch emotions, and more. Do not greet the user every response. All the context you are given is in one chat session."
 )
 
-# Function to handle conversation (same as original)
-
 def acc_nyxion_7v(message, history, user_id):
     global context
 
@@ -182,10 +165,8 @@ def acc_nyxion_7v(message, history, user_id):
         f"User Input: {message}\n"
     )
 
-    # Check history structure
     print("History:", history)
 
-    # Construct the full conversation properly
     full_conversation = "\n".join([f"User: {item['content']}" if item['role'] == 'user' else f"AI: {item['content']}" for item in history])
 
     consciousness_score = iit_consciousness_processing(dummy_input)
@@ -238,7 +219,6 @@ def acc_nyxion_7v(message, history, user_id):
         api_name="/chat"
     )
 
-    # Update the history with dictionaries for role/content
     history.append({'role': 'user', 'content': message})
     history.append({'role': 'assistant', 'content': response_main})
 
@@ -246,8 +226,6 @@ def acc_nyxion_7v(message, history, user_id):
 
     return "", history
 
-# UI (same as original)
-
 theme = gr.themes.Soft(
     primary_hue=gr.themes.Color(c100="#d1fae5", c200="#a7f3d0", c300="#6ee7b7", c400="#34d399", c50="rgba(217.02092505888103, 222.113134765625, 219.29041867345288, 1)", c500="#10b981", c600="#059669", c700="#047857", c800="#065f46", c900="#064e3b", c950="#054436"),
     secondary_hue="red",