app.py

import os
from threading import Thread
from typing import Iterator

import gradio as gr
import spaces
import torch
from transformers import BitsAndBytesConfig, AutoModelForCausalLM, GemmaTokenizerFast, TextIteratorStreamer

huggingface_token = os.getenv('read_access')

MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

model_id = "google/gemma-2-9b-it"
tokenizer = GemmaTokenizerFast.from_pretrained(model_id, token = huggingface_token)

quantization = BitsAndBytesConfig(load_in_4bit= True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
    quantization_config=quantization,
    token = huggingface_token
)
model.config.sliding_window = 4096
model.eval()


@spaces.GPU(duration=90)
def generate(
    message: str,
    chat_history: list[tuple[str, str]],
    max_new_tokens: int = 1024,
    temperature: float = 0.6,
    top_p: float = 0.9,
    top_k: int = 50,
    repetition_penalty: float = 1.2,
) -> Iterator[str]:
    conversation = []
    for user, assistant in chat_history:
        conversation.extend(
            [
                {"role": "user", "content": user},
                {"role": "assistant", "content": assistant},
            ]
        )
    conversation.append({"role": "user", "content": message})

    input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt")
    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
    input_ids = input_ids.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,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        num_beams=1,
        repetition_penalty=repetition_penalty,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

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


chat_interface = gr.ChatInterface(
    fn=generate,
    chatbot=gr.Chatbot(height=500, label = "日本語アシスタント", show_label=True),
    textbox=gr.Textbox(placeholder="メッセージを入力してください", container=False, scale=7),
    additional_inputs=[
        gr.Slider(
            label="テキスト作成時の最大単語数",
            minimum=1,
            maximum=MAX_MAX_NEW_TOKENS,
            step=1,
            value=DEFAULT_MAX_NEW_TOKENS,
        ),
        gr.Slider(
            label="創造",
            minimum=0.1,
            maximum=4.0,
            step=0.1,
            value=0.2,
        ),
        gr.Slider(
            label="最も確率の高い単語のグループ",
            minimum=0.05,
            maximum=1.0,
            step=0.05,
            value=0.9,
        ),
        gr.Slider(
            label="上位の単語の確率が最も高い(top-k)",
            minimum=1,
            maximum=1000,
            step=1,
            value=50,
        ),
        gr.Slider(
            label="懲罰を繰り返す",
            minimum=1.0,
            maximum=2.0,
            step=0.05,
            value=1.1,
        ),
    ],
    theme="soft",
    stop_btn=None,
    examples = [
    ["寿司の作り方"],
    ["美しい着物ドレスの選び方"],
    ["地震が起きたらどうするか"],
    ["どうすれば幸せに生きられるか"],
    ["魚を食べることの利点"],
    ["グループで効果的に作業する方法"]
    ],

    cache_examples=False,
    title = "日本語アシスタント",
    clear_btn="🗑️ 消す",
    undo_btn="↩️ 元に戻す",
    submit_btn="🚀 送信",
    retry_btn="🔄 リトライ",
    additional_inputs_accordion="高度なカスタマイズ",
)


if __name__ == "__main__":
    chat_interface.queue(max_size=20).launch()