import os
import json
from datetime import datetime

import gradio as gr
from openai import OpenAI


def print_now(msg):
    now = datetime.now()
    formatted_time = now.strftime("%Y-%m-%d %H:%M:%S.%f")
    print(f"{msg}:{formatted_time}")
    return formatted_time

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    try:
        weekdays = ["周一", "周二", "周三", "周四", "周五", "周六", "周日"]
        now = datetime.now()
        weekday_num = now.weekday()
        weekday_chinese = weekdays[weekday_num]
        formatted_time = now.strftime("%Y-%m-%d %H:%M:%S") + " " + weekday_chinese
        default_system = f"你是一个由腾讯开发的有用的人工智能助手，你的名字是“腾讯元宝”，简称“元宝”，你的英文名是“Tencent Yuanbao”，你乐于帮助大家解答问题。\n现在的时间是{formatted_time}"

        messages = [{"Role": "system", "Content": default_system}]
        client = OpenAI(
            api_key=os.getenv('HUNYUAN_API_KEY'),
            base_url="https://api.hunyuan.cloud.tencent.com/v1",
        )
        for val in history:
            if val[0] and val[1]:
                messages.append({"Role": "user", "Content": val[0]})
                pure_response = val[1].split("**End thinking**")[-1].strip()
                messages.append({"Role": "assistant", "Content": pure_response})
                
        
        messages.append({"Role": "user", "Content": message})
        completion = client.chat.completions.create(
            model="hunyuan-t1-latest",
            messages=messages,
            stream=True,
            extra_body={
            "stream_moderation": True,
            "enable_enhancement": False,
            }
        )
        response = ""
        is_reasoning_start = True
        is_reasoning_end = True
        

        for event in completion:
            if message == "深圳哪里好玩？":
                print(111111)
                print(event.choices[0].delta.reasoning_content)
                print(event.choices[0].delta.content)
            if hasattr(event.choices[0].delta, 'reasoning_content') and event.choices[0].delta.reasoning_content:
                if is_reasoning_start:
                    response += '> **Start thinking**\n\n'
                    is_reasoning_start = False
                token = event.choices[0].delta.reasoning_content
                response += token
            else:
                if is_reasoning_end:
                    response += '> **End thinking**\n\n'
                    is_reasoning_end = False
                token = event.choices[0].delta.content
                response += token
            yield response
    except Exception as e:
        raise gr.Error(f"发生错误: {str(e)}")

example_prompts = [
    [
        "Write a short papragraph where the 1st letter of each sentence spells out the word 'CODE'. The message should appear natural and not obviously hide this pattern.",
        "System",
        256,
        0.8,
        0.85
    ],  
    [
        "Compose an engaging travel blog post about a recent trip to Hawaii, highlighting cultural experiences and must-see attractions.",
        "System",
        128,
        0.3,
        0.9
    ],
    [
        "Why has online learning been able to spread rapidly in recent years?",
        "System",
        64,    
        0.1,   
        0.95
    ],
    [
        "How many 'e' in Deeplearning?",
        "System",
        512,   
        1.2,   
        0.75
    ],
    [
        "Write a 3-line poem",
        "System",
        384,   
        0.6,  
        0.88
    ]
]

latex_delimiters = [
    {"left": "$$", "right": "$$", "display": True},
    {"left": "\\[", "right": "\\]", "display": True},{"left": "$", "right": "$", "display": False},
    {"left": "\\(", "right": "\\)", "display": False}
]


chatbot = gr.Chatbot(latex_delimiters=latex_delimiters, scale=9)


demo = gr.ChatInterface(respond,
    title="Hunyuan-T1",
    chatbot=chatbot,
    examples=[
        "Write a short papragraph where the 1st letter of each sentence spells out the word 'CODE'. The message should appear natural and not obviously hide this pattern.",
        "Compose an engaging travel blog post about a recent trip to Hawaii, highlighting cultural experiences and must-see attractions.",
        "Why has online learning been able to spread rapidly in recent years?",
        "How many 'e' in Deeplearning?",
        "Write a 3-line poem"
    ],
    description="当前体验demo为非联网Hunyuan-T1 最新推理模型，完整版联网/非联网能力即将在元宝上线，敬请期待!</br>The current  demo is the latest reasoning  model of Hunyuan-T1. The full version with browsing will be launched on Tencent Yuanbao soon. Stay tuned."
)

if __name__ == "__main__":
    demo.queue(default_concurrency_limit=100)
    demo.launch(max_threads=100)