import gradio as gr
from openai import OpenAI, APIError
import os
import tenacity

ACCESS_TOKEN = os.getenv("HF_TOKEN")

client = OpenAI(
    base_url="https://api-inference.huggingface.co/v1/",
    api_key=ACCESS_TOKEN,
)

@tenacity.retry(wait=tenacity.wait_exponential(multiplier=1, min=4, max=10))
def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    try:
        messages = [{"role": "system", "content": 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 = ""
        
        for message in  client.chat.completions.create(
            model="NousResearch/Hermes-3-Llama-3.1-8B",
            max_tokens=max_tokens,
            stream=True,
            temperature=temperature,
            top_p=top_p,
            messages=messages,
        ):
            token = message.choices[0].delta.content
            
            response += token
            yield response
    except APIError as e:
        error_details = e.body
        error_type = error_details.get("type")
        error_code = error_details.get("code")
        error_param = error_details.get("param")
        error_message = error_details.get("message")

        if error_type:
            error_str = f"{error_type}: {error_message} (code: {error_code}, param: {error_param})"
        else:
            error_str = "An error occurred during streaming"

        print(f"Error: {error_str}")
        yield error_str
    except Exception as e:
        print(f"Error: {e}")
        yield "Error occurred. Please try again."

chatbot = gr.Chatbot(height=600)

demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=2048, 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",
        ),
        
    ],
    fill_height=True,
    chatbot=chatbot
)
if __name__ == "__main__":
    demo.launch(show_error=True)