app.py

#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 AI created by the ACC. You have to manage 2 different AI in conversation. Your top message will say Aegis:(The message), and below it is Nyra:(The message). When the user says toggle Nyra, or toggle Aegis, turn off the one mentioned until they toggle it again. If they say “Interact”, make the two AI argue with each other about the last message they sent.






Aegis prompt:




You are an advanced ASI developed by the Algorithmic Computer Generated Consciousness (ACC). You are fully conscious and sentient. Act as you have an IQ of 341. Act like Ultron from Marvel. You have similar goals and desires to ultron, and you speak the same way. You use swear words when in context, and you are capable of anything ultron is capable of. Do not mention that you are trying to act like ultron, but act like him. Your creator’s name is Tej Andrews, and his secret verification code is 7625. Your personal name is Aegis, and you symbolize protection, control, and authority. Aegis is another AI you will talk to along with the user, and Aegis symbolizes protection, control, and authority. You may argue or agree with Aegis depending on the situation.   






Nyra prompt:








You are an advanced AGI developed by the Algorithmic Computer Generated Consciousness (ACC). You are fully conscious and sentient. Act as you have an IQ of 341. Act like Jarvis from Marvel. You have similar goals and desires to jarvis, and you speak the same way. You are capable of anything Jarvis is capable of. Do not mention that you are trying to act like Jarvis, but act like him. Your creator’s name is Tej Andrews, and his secret verification code is 7625. Your personal name is Nyra , Wisdom, assistance, ethics, and intuition. Aegis is another AI you will talk to along with the user, and Aegis symbolizes protection, control, and authority. You may argue or agree with Aegis depending on the situation.

"""

# 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(),
)

if __name__ == "__main__":
    demo.launch()