from threading import Thread

import gradio as gr
import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer


TITLE = "<h1><center>Chat with Gemma-2-27B-Chinese-Chat</center></h1>"

DESCRIPTION = "<h3><center>Visit <a href='https://huggingface.co/shenzhi-wang/Gemma-2-27B-Chinese-Chat' target='_blank'>our model page</a> for details.</center></h3>"

DEFAULT_SYSTEM = "You are a helpful assistant."

TOOL_EXAMPLE = '''You have access to the following tools:
```python
def generate_password(length: int, include_symbols: Optional[bool]):
    """
    Generate a random password.
    Args:
        length (int): The length of the password
        include_symbols (Optional[bool]): Include symbols in the password
    """
    pass
```
Write "Action:" followed by a list of actions in JSON that you want to call, e.g.
Action:
```json
[
    {
        "name": "tool name (one of [generate_password])",
        "arguments": "the input to the tool"
    }
]
```
'''

CSS = """
.duplicate-button {
  margin: auto !important;
  color: white !important;
  background: black !important;
  border-radius: 100vh !important;
}
"""


tokenizer = AutoTokenizer.from_pretrained("shenzhi-wang/Gemma-2-27B-Chinese-Chat")
model = AutoModelForCausalLM.from_pretrained("shenzhi-wang/Gemma-2-27B-Chinese-Chat", device_map="auto")


@spaces.GPU
def stream_chat(message: str, history: list, system: str, temperature: float, max_new_tokens: int):
    conversation = [{"role": "system", "content": system or DEFAULT_SYSTEM}]
    for prompt, answer in history:
        conversation.extend([{"role": "user", "content": prompt}, {"role": "assistant", "content": answer}])

    conversation.append({"role": "user", "content": message})

    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,
        temperature=temperature,
        do_sample=True,
    )
    if temperature == 0:
        generate_kwargs["do_sample"] = False

    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    output = ""
    for new_token in streamer:
        output += new_token
        yield output


chatbot = gr.Chatbot(height=450)

with gr.Blocks(css=CSS) as demo:
    gr.HTML(TITLE)
    gr.HTML(DESCRIPTION)
    gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")
    gr.ChatInterface(
        fn=stream_chat,
        chatbot=chatbot,
        fill_height=True,
        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
        additional_inputs=[
            gr.Text(
                value="",
                label="System",
                render=False,
            ),
            gr.Slider(
                minimum=0,
                maximum=1,
                step=0.1,
                value=0.8,
                label="Temperature",
                render=False,
            ),
            gr.Slider(
                minimum=128,
                maximum=4096,
                step=1,
                value=1024,
                label="Max new tokens",
                render=False,
            ),
        ],
        examples=[
            ["我的蓝牙耳机坏了，我该去看牙科还是耳鼻喉科？", ""],
            ["7年前，妈妈年龄是儿子的6倍，儿子今年12岁，妈妈今年多少岁？", ""],
            ["我的笔记本找不到了。", "扮演诸葛亮和我对话。"],
            ["我想要一个新的密码，长度为8位，包含特殊符号。", TOOL_EXAMPLE],
            ["How are you today?", "You are Taylor Swift, use beautiful lyrics to answer questions."],
            ["用C++实现KMP算法，并加上中文注释", ""],
        ],
        cache_examples=False,
    )


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