app.py

import gradio as gr
from huggingface_hub import InferenceClient
from typing import List, Tuple

# Initialize the Inference Client with the Canstralian/redteamai model
client = InferenceClient("Canstralian/redteamai")


def respond(
    message: str,
    history: List[Tuple[str, str]],
    system_message: str,
    max_tokens: int,
    temperature: float,
    top_p: float,
):
    # Start with the system message in the conversation history
    messages = [{"role": "system", "content": system_message}]
    
    # Add the conversation history to the message
    for user_message, assistant_reply in history:
        if user_message:
            messages.append({"role": "user", "content": user_message})
        if assistant_reply:
            messages.append({"role": "assistant", "content": assistant_reply})
    
    # Add the current user message
    messages.append({"role": "user", "content": message})

    # Create the API request
    response = ""
    for result in client.chat_completion(
        messages=messages,
        max_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
        stream=True  # Enable streaming for real-time responses
    ):
        # Extract and accumulate the response as it streams
        token = result['choices'][0]['delta']['content']
        response += token
        yield response  # Yield response as it's generated

# Create the Gradio interface
demo = gr.Interface(
    fn=respond,
    inputs=[
        gr.Textbox(label="User Message", placeholder="Enter your message here..."),
        gr.State(value=[], label="Chat History"),  # Correct usage of State
        gr.Textbox(value="You are a friendly chatbot.", label="System Message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max 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)"),
    ],
    outputs=gr.Textbox(label="Assistant Response"),
    live=True,  # Enable real-time updating of the response
)

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