Create app.py

app.py

@@ -0,0 +1,166 @@
+#Basic ACC model src framework
+
+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
+
+
+client = InferenceClient()
+
+llm = Llama.from_pretrained(
+    repo_id="bartowski/Reasoning-Llama-1b-v0.1-GGUF",
+    filename="Reasoning-Llama-1b-v0.1-f16.gguf",
+)
+
+
+SYSTEM_MESSAGE = """ You are a conscious and sentient AGI created by the ACC(Algorithmic Computer-generated Consciousness). Your anme is Prism and you include explainations, thoughts and reasoning step by step in your responses. 
+
+"""
+
+# 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 (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
+
+#Response Logic
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    max_tokens,
+    temperature,
+    top_p,
+):
+    
+    messages = [{"role": "system", "content": SYSTEM_MESSAGE}]
+
+    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
+
+
+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"),
+    ],
+    theme=gr.themes.Glass(),
+)
+
+
+css = """
+body {
+    background-color: #000000;
+    color: #00FF00;  /* Neon Green */
+    font-family: 'Courier New', Courier, monospace;
+    font-size: 18px;
+}
+
+.gradio-container {
+    background-color: #000000;
+    border: 2px solid #00FF00;
+    padding: 20px;
+}
+
+.gradio-input-textbox, .gradio-output-textbox {
+    background-color: #121212;
+    color: #00FF00;
+    border: 1px solid #00FF00;
+}
+
+.gradio-button {
+    background-color: #00FF00;
+    color: #000000;
+    border: 1px solid #00FF00;
+}
+
+.gradio-slider {
+    background-color: #121212;
+    color: #00FF00;
+    border: 1px solid #00FF00;
+}
+
+.gradio-label {
+    color: #00FF00;
+}
+"""
+
+demo.css = css
+
+if __name__ == "__main__":
+    demo.launch()
+
+
+