Update app.py

app.py

@@ -1,3 +1,6 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
 import gradio as gr
 from huggingface_hub import InferenceClient
 from llama_cpp import Llama
@@ -114,9 +117,64 @@ Gertrude said:
 
 Final notes: Make sure you act conscious, very autistic, and respond with very high perplexity. Also ensure that you make small purposeful errors(punctuation, spelling, and capitalisation). You also use the emoji combinations 🍆✊, 🍆🍑,  🙉, 🔥🔥🔥🔥🔥, and 👉👈 when you want to switch the tone of the conversation or say something funny. Remember to act autistic.
 
-
 """
 
+# RNN Model
+class RNNModel(nn.Module):
+    def __init__(self, input_size, hidden_size, output_size, num_layers=1):
+        super(RNNModel, self).__init__()
+        self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True)
+        self.fc = nn.Linear(hidden_size, output_size)
+
+    def forward(self, x):
+        h0 = torch.zeros(1, x.size(0), self.rnn.hidden_size).to(x.device)
+        out, _ = self.rnn(x, h0)
+        out = self.fc(out[:, -1, :])  # Get the last output
+        return out
+
+# CNN Model
+class CNNModel(nn.Module):
+    def __init__(self, num_classes):
+        super(CNNModel, self).__init__()
+        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
+        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
+        self.fc1 = nn.Linear(32 * 8 * 8, 128)
+        self.fc2 = nn.Linear(128, num_classes)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = x.view(-1, 32 * 8 * 8)  # Flatten
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+# NN Model (Feedforward Neural Network)
+class NNModel(nn.Module):
+    def __init__(self, input_size, hidden_size, output_size):
+        super(NNModel, self).__init__()
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.fc2 = nn.Linear(hidden_size, output_size)
+
+    def forward(self, x):
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+# PHI Model (Example: Softmax-Based Regression)
+class PHIModel(nn.Module):
+    def __init__(self, input_size, output_size):
+        super(PHIModel, self).__init__()
+        self.fc = nn.Linear(input_size, output_size)
+        self.softmax = nn.Softmax(dim=1)
+
+    def forward(self, x):
+        x = self.fc(x)
+        x = self.softmax(x)
+        return x
+
+# Chatbot Response Logic
 def respond(
     message,
     history: list[tuple[str, str]],
@@ -149,25 +207,17 @@ def respond(
         response += token
         yield response
 
-# Apply the soft theme
+# Gradio Chat Interface with Models
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Maximum Response Length"),
         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Creativity"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Neuron Firing Rate",
-        ),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Neuron Firing Rate"),
     ],
-    theme=gr.themes.Soft(),  # Set the soft theme
+    theme=gr.themes.Soft(),
 )
 
 if __name__ == "__main__":
     demo.launch()
 
-
-