app.py

import gradio as gr
from huggingface_hub import InferenceClient
from llama_cpp import Llama

# Initialize the InferenceClient
client = InferenceClient()

llm = Llama.from_pretrained(
    repo_id="bartowski/Reasoning-Llama-1b-v0.1-GGUF",
    filename="Reasoning-Llama-1b-v0.1-f16.gguf",
)

# Fixed system message
FIXED_SYSTEM_MESSAGE = "You are an artifial inteligence created by the ACC(Algorithmic Computer-generated Consciousness). Act liek a german man in the mid 1940s."

def respond(
    message,
    history: list[tuple[str, str]],
    user_system_message,  # User-configurable system message
    max_tokens,
    temperature,
    top_p,
):
    # Combine the fixed and user-provided system messages
    combined_system_message = f"{FIXED_SYSTEM_MESSAGE} {user_system_message}"

    # Construct the messages list
    messages = [{"role": "system", "content": combined_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 = ""

    # Use the client to get the chat completion
    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

# Gradio interface
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="", label="System Message"),
        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="Neural Activity",
        ),
    ],
)

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