import os
import torch
import gradio as gr
from transformers import GemmaTokenizer, AutoModelForCausalLM
from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer, TextIteratorStreamer
from threading import Thread
import subprocess

subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
# Set an environment variable
token = os.getenv('HUGGINGFACE_TOKEN')

model_path= "CubeAI/Zhuji-Internet-Literature-Intelligent-Writing-Model-V1.0"
tokenizer = AutoTokenizer.from_pretrained(model_path, encode_special_tokens=True, token=token)
model= AutoModelForCausalLM.from_pretrained(
                                            model_path,
                                            torch_dtype= torch.bfloat16,
                                            low_cpu_mem_usage= True, 
                                            token=token,
                                            attn_implementation="flash_attention_2",
                                            device_map= "auto"
     )


model = torch.compile(model)
#model.to("cuda")
model = model.eval()


DESCRIPTION = '''
<div>
    <h1 style="text-align: center;">网文智能辅助写作 - 珠玑系列模型</h1>
    <p>我们自主研发的珠玑系列智能写作模型，专为网文创作与理解而生。基于丰富的网文场景数据，包括续写、扩写、取名等创作任务和章纲抽取等理解任务，我们训练了一系列模型参数，覆盖1B至14B不等的模型族，包括生成模型和embedding模型。</p>
    <p>📚 <strong>基础版模型：</strong>适合初次尝试智能写作的用户，提供长篇小说创作的基础功能，助您轻松迈入智能写作的新纪元。</p>
    <p>🚀 <strong>高级版模型：</strong>为追求更高层次创作体验的用户设计，配备更先进的文本生成技术和更精细的理解能力，让您的创作更具深度和创新。</p>
    <p>珠玑系列模型（Zhuji-Internet-Literature-Intelligent-Writing-Model-V1.0）现已发布，包括1B、7B、14B规模的模型，基于Qwen1.5架构，旨在为您提供卓越的网文智能写作体验。</p>
</div>

'''

LICENSE = """
<p/>
---
Built with NovelGen
"""

PLACEHOLDER = """
<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
   <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">ai助力写作</h1>
   <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">ai辅助写作</p>
</div>
"""


css = """
h1 {
  text-align: center;
  display: block;
}
#duplicate-button {
  margin: auto;
  color: white;
  background: #1565c0;
  border-radius: 100vh;
}
"""
tokenizer.chat_template = """{% for message in messages %}
    {% if message['role'] == 'user' %}
        {{'<|user|>'+ message['content'].strip() + '<|observation|>'+ '<|assistant|>'}}
    {% elif message['role'] == 'system' %}
        {{ '<|system|>' + message['content'].strip() + '<|observation|>'}}
    {% elif message['role'] == 'assistant' %}
        {{  message['content'] + '<|observation|>'}}
    {% endif %}
    {% endfor %}""".replace("\n", "").replace("  ", "")

def chat_zhuji(
              message: str, 
              history: list, 
              temperature: float, 
              max_new_tokens: int
             ) -> str:
    """
    Generate a streaming response using the llama3-8b model.
    Args:
        message (str): The input message.
        history (list): The conversation history used by ChatInterface.
        temperature (float): The temperature for generating the response.
        max_new_tokens (int): The maximum number of new tokens to generate.
    Returns:
        str: The generated response.
    """
    conversation = []
    for user, assistant in history:
        conversation.extend([{"role": "system","content": "",},{"role": "user", "content": user}, {"role": "<|assistant|>", "content": assistant}])
    conversation.append({"role": "user", "content": message})

    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").to(model.device)
    
    streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)

    generate_kwargs = dict(
        input_ids= input_ids,
        streamer=streamer,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        penalty_alpha= 0.65, 
        top_p= 0.90, 
        top_k= 35, 
        use_cache= True, 
        eos_token_id= tokenizer.encode("<|observation|>",add_special_tokens= False),
        temperature=temperature,
    )
    # This will enforce greedy generation (do_sample=False) when the temperature is passed 0, avoiding the crash.             
    if temperature == 0:
        generate_kwargs['do_sample'] = False
        
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    #outputs = []
    #for text in streamer:
    #    outputs.append(text)
    #    yield "".join(outputs)

    partial_message = ""
    for new_token in streamer:
        if new_token != '<|observation|>':
            partial_message += new_token
            yield partial_message

# Gradio block
chatbot=gr.Chatbot(height=450, placeholder=PLACEHOLDER, label='Gradio ChatInterface')
text_box= gr.Textbox(show_copy_button= True)
with gr.Blocks(fill_height=True, css=css) as demo: 
    gr.Markdown(DESCRIPTION)
    gr.ChatInterface(
        fn=chat_zhuji,
        chatbot=chatbot,
        textbox= text_box,
        fill_height=True,
        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
        additional_inputs=[
            gr.Slider(minimum=0,
                      maximum=1, 
                      step=0.1,
                      value=0.95, 
                      label="Temperature", 
                      render=False),
            gr.Slider(minimum=128, 
                      maximum=8192*2,
                      step=1,
                      value=8192, 
                      label="Max new tokens", 
                      render=False ),
            ],
        examples=[
            ['请给一个古代美女的外貌来一段描写'],
            ['请生成4个东方神功的招式名称'],
            ['生成一段官军和倭寇打斗的场面描写'],
            ['生成一个都市大女主的角色档案'],
            ],
        cache_examples=False,
                     )
    
    gr.Markdown(LICENSE)
    
if __name__ == "__main__":
    demo.launch( 
               #server_name='0.0.0.0',
               #server_port=config.webui_config.port, 
               #inbrowser=True, 
               #share=True
            )