import gradio as gr
from huggingface_hub import InferenceClient
import os

# Fetch the API key from environment variables
api_key = os.getenv('HF_API_KEY')

# Configure the Inference API client
client = InferenceClient("meta-llama-3-120b-instruct-zoa", token=api_key)

def respond(message, history, system_message, max_tokens, temperature, top_p):
    messages = [{"role": "system", "content": system_message}]
    
    for user_msg, assistant_msg in history:
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
        if assistant_msg:
            messages.append({"role": "assistant", "content": assistant_msg})
        
    messages.append({"role": "user", "content": message})

    try:
        responses = client.chat_completion(
            messages,
            max_tokens=max_tokens,
            temperature=temperature,
            top_p=top_p,
        )
    except Exception as e:
        yield f"Error: {str(e)}"
        return

    response = ""
    for res in responses:
        response += res.choices[0].delta.content
        yield response

demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly assistant.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, label="Top-p (nucleus sampling)"),
    ],
    title="Meta-Llama Chat",
    description="A chat interface powered by Meta Llama 3-120B model."
)

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