import gradio as gr
import requests
import os
import json
from collections import deque

# 환경 변수에서 API 토큰 가져오기
TOKEN = os.getenv("HUGGINGFACE_API_TOKEN")

# API 토큰이 설정되어 있는지 확인
if not TOKEN:
    raise ValueError("API token is not set. Please set the HUGGINGFACE_API_TOKEN environment variable.")

# 대화 기록을 관리하는 큐 (최대 10개의 대화 기록을 유지)
memory = deque(maxlen=10)

def respond(
    message,
    history: list[tuple[str, str]],
    system_message="너의 이름은 홍길동이다",
    max_tokens=512,
    temperature=0.7,
    top_p=0.95,
):
    # 현재 대화 내용을 메모리에 추가
    memory.append((message, None))

    messages = [{"role": "system", "content": system_message}]

    # 메모리에서 대화 기록을 가져와 메시지 목록에 추가
    for val in memory:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    headers = {
        "Authorization": f"Bearer {TOKEN}",
        "Content-Type": "application/json"
    }

    payload = {
        "model": "meta-llama/Meta-Llama-3.1-405B-Instruct",
        "max_tokens": max_tokens,
        "temperature": temperature,
        "top_p": top_p,
        "messages": messages
    }

    response = requests.post("https://api-inference.huggingface.co/v1/chat/completions", headers=headers, json=payload, stream=True)
    
    response_text = ""
    for chunk in response.iter_content(chunk_size=None):
        if chunk:
            chunk_data = chunk.decode('utf-8')
            response_json = json.loads(chunk_data)
            # content 영역만 출력
            if "choices" in response_json:
                content = response_json["choices"][0]["message"]["content"]
                response_text = content
                # 마지막 대화에 모델의 응답을 추가하여 메모리에 저장
                memory[-1] = (message, response_text)
                yield content

theme="Nymbo/Nymbo_Theme"

with gr.Blocks(css=None, theme=theme) as demo:
    with gr.Tab("Playground"):
        gr.Markdown("## Playground")
        with gr.Row():
            input_text = gr.Textbox(label="Enter your text")
            submit_button = gr.Button("Submit")
            output_text = gr.Textbox(label="Processed Text")
            submit_button.click(
                fn=respond,
                inputs=[input_text, output_text],
                outputs=output_text
            )
        additional_inputs = [
            gr.Textbox(value="너의 이름은 홍길동이다", label="System message"),
            gr.Slider(minimum=1, maximum=2048, value=512, 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.Tab("Guide"):
        gr.Markdown("## Guide")
        gr.Markdown("""
        ### How to use:
        - Use the Playground tab to interact with the chatbot.
        - Adjust the parameters to see how the model's responses change.
        - Explore different system messages to guide the conversation.
        """)

if __name__ == "__main__":
    demo.queue(concurrency_count=20).launch()