import os
import gradio as gr
import spaces
from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
from threading import Thread


# Set an environment variable
HF_TOKEN = os.environ.get("HF_TOKEN", None)

model_icon = "🐼"
model_name = "Gemma 2 Baku 2B Instruct"
model_url = "https://huggingface.co/rinna/gemma-2-baku-2b-it"
model_id = "rinna/gemma-2-baku-2b-it"
base_model_url = "https://huggingface.co/google/gemma-2-2b"
base_model_name = "Gemma 2 2B"
press_url = "https://rinna.co.jp/news/2024/07/20240725.html"
logo_url = "https://huggingface.co/rinna/gemma-2-baku-2b/resolve/main/rinna.png"

LICENSE = """
---
<div>
<p>License: <a href="https://ai.google.dev/gemma/terms">Gemma Terms of Use </a><p>
<p>This space is implemented based on <a href="https://huggingface.co/spaces/ysharma/Chat_with_Meta_llama3_8b">ysharma/Chat_with_Meta_llama3_8b</a>.</p>
</div>
"""


DESCRIPTION = f"""
<div>
<p>{model_icon} <a href="{model_url}"><b>{model_name}</b> ({model_id})</a>は、<a href="https://rinna.co.jp">rinna株式会社</a>が<a href="{base_model_url}">{base_model_name}</a>に日本語継続事前学習およびインストラクションチューニングを行った大規模言語モデルです．{base_model_name}の優れたパフォーマンスを日本語に引き継いでおり、日本語のチャットにおいて高い性能を示しています。</p>
<p>🤖 このデモでは、{model_name}とチャットを行うことが可能です。</p>
<p>📄 モデルの詳細については、<a href="{press_url}">プレスリリース</a>、および、<a href="https://rinnakk.github.io/research/benchmarks/lm/index.html">ベンチマーク</a>をご覧ください。お問い合わせは<a href="https://rinna.co.jp/inquiry/">こちら</a>までどうぞ。</p>
</div>
"""

PLACEHOLDER = f"""
<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
   <img src="{logo_url}" style="width: 80%; max-width: 550px; height: auto; opacity: 0.55;  "> 
   <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">{model_name}</h1>
</div>
"""

css = """
h1 {
  text-align: center;
  display: block;
}
#duplicate-button {
  margin: auto;
  color: white;
  background: #1565c0;
  border-radius: 100vh;
}
"""


# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")


@spaces.GPU(duration=120)
def chat_llama3_8b(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": "user", "content": user}, {"role": "assistant", "content": assistant}])
    conversation.append({"role": "user", "content": message})

    # Need to set add_generation_prompt=True to ensure the model generates the response.
    input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt").to(model.device)
    
    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, 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,
        temperature=temperature,
        repetition_penalty=1.1,
    )
    # 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)
        

# Gradio block
chatbot=gr.Chatbot(height=450, placeholder=PLACEHOLDER, label='Gradio ChatInterface')

with gr.Blocks(fill_height=True, css=css) as demo:
    
    gr.Markdown(DESCRIPTION)
    gr.ChatInterface(
        fn=chat_llama3_8b,
        chatbot=chatbot,
        fill_height=True,
        additional_inputs_accordion=gr.Accordion(label="⚙️ パラメータ", open=False, render=False),
        additional_inputs=[
            gr.Slider(minimum=0,
                      maximum=1, 
                      step=0.05,
                      value=0.9, 
                      label="生成時におけるサンプリングの温度（ランダム性）", 
                      render=False),
            gr.Slider(minimum=128, 
                      maximum=4096,
                      step=1,
                      value=512, 
                      label="生成したい最大のトークン数", 
                      render=False),
            ],
        examples=[
            ["日本で有名なものと言えば"],
            ["ネコ: 「お腹が減ったニャ」\nから始まる物語を書いて"],
            ["C言語で素数を判定するコードを書いて"],
            ["人工知能とは何ですか"],
        ],
        cache_examples=False,
                     )
    
    gr.Markdown(LICENSE)
    

if __name__ == "__main__":
    demo.launch()