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#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()
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