import gradio as gr
from huggingface_hub import InferenceClient
import os

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/en/guides/inference
"""

# Retrieve the Hugging Face token
hf_token = os.environ.get("HF_TOKEN")
if not hf_token:
    raise ValueError("Please set the HF_TOKEN environment variable with your Hugging Face API token.")

# Initialize the InferenceClient with a correct model
client = InferenceClient("models/meta-llama/Llama-3.2-1B", token=hf_token)

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": system_message}]

    for user_input, assistant_response in history:
        if user_input:
            messages.append({"role": "user", "content": user_input})
        if assistant_response:
            messages.append({"role": "assistant", "content": assistant_response})

    messages.append({"role": "user", "content": message})

    response = ""

    # Start the chat completion
    try:
        for msg in client.chat_completion(
            messages=messages,
            max_new_tokens=max_tokens,
            stream=True,
            temperature=temperature,
            top_p=top_p,
        ):
            token = msg.delta.get("content", "")
            response += token
            yield response
    except Exception as e:
        yield f"Error during inference: {e}"

"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    fn=respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=1024, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.01, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.01,
            label="Top-p (nucleus sampling)",
        ),
    ],
    title="Chat with Llama 2",
    description="A chat interface using Llama 2 model via Hugging Face Inference API.",
)

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