import os
import gradio as gr
from cerebras.cloud.sdk import Cerebras

client = Cerebras(
    api_key=os.environ.get("CEREBRAS_API_KEY"),
)

TTILE = """
<h1 align="center">🚀 Try the world's fastest inference by Cerebras ⚡</h1>
"""
NOTICE = """
Current only support Llama3.1 8B and Llama3.1 70B.
"""

def respond(
    message,
    history: list[tuple[str, str]],
    model_id,
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    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 = ""

    stream = client.chat.completions.create(
        messages=messages,
        model=model_id,
        max_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
        stream=True,
    )

    for chunk in stream:
        token = chunk.choices[0].delta.content or ""
        response += token
        yield response

chatbot = gr.ChatInterface(
    respond,
    chatbot=gr.Chatbot(height=500),
    additional_inputs=[
        gr.Dropdown(
            ["llama3.1-8b", "llama3.1-70b"],
            value="llama3.1-70b",
            label="Models"
        ),
        gr.Textbox(value="You are a friendly assistant.", label="System message"),
        gr.Slider(minimum=1, maximum=8192, value=4096, 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)",
        ),
    ],
)

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.HTML(TTILE)
    gr.HTML(NOTICE)
    chatbot.render()

if __name__ == "__main__":
    demo.launch(debug=True, show_error=True)