app.py

import gradio as gr
from huggingface_hub import InferenceClient
from collections import defaultdict

# Initialize model client
client = InferenceClient("Futuresony/future_ai_12_10_2024.gguf")

# Memory storage
session_histories = defaultdict(list)  # Stores full chat history per session

def format_chat_history(history):
    """Formats history in a structured way for LLaMA models."""
    chat_str = ""  
    for user_msg, bot_response in history:
        chat_str += f"User: {user_msg}\nAI: {bot_response}\n"
    return chat_str.strip()  # Remove unnecessary spaces

def respond(message, history, system_message, max_tokens, temperature, top_p):
    session_id = id(history)  # Unique session ID
    session_history = session_histories[session_id]  # Retrieve stored history

    # Add user message to history
    formatted_history = format_chat_history(session_history)
    full_input = f"{system_message}\n\n{formatted_history}\nUser: {message}\nAI:"

    # Generate response
    response = client.text_generation(
        full_input,
        max_new_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
    )

    # Extract & clean response
    cleaned_response = response.strip()

    # Update chat history
    session_history.append((message, cleaned_response))

    return cleaned_response

# Gradio Chat Interface
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are an AI assistant that remembers previous conversations.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=250, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
    ],
)

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