import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from bitsandbytes.nn import Int8Params, Int8Linear
from transformers.utils.quantization_config import BitsAndBytesConfig

# モデルとトークナイザの読み込み
model_name = "Qwen/Qwen2.5-7B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)

# 4bit量子化設定
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4"
)

# モデルの読み込みと量子化
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype=torch.bfloat16,
    quantization_config=quantization_config
)

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    # メッセージの準備
    messages = [{"role": "system", "content": system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

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

    # メッセージをトークナイザに通す
    input_ids = tokenizer([message], return_tensors="pt").input_ids.to(model.device)

    # モデルの推論
    output_ids = model.generate(
        input_ids,
        max_length=max_tokens,
        temperature=temperature,
        top_p=top_p,
        do_sample=True,
    )

    response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
    response = response[len(message):]  # 入力メッセージを削除
    return response

# インターフェース
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="ユーザーの応答と依頼に答えてください。ポジティブに", label="システムメッセージ"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="新規トークン最大"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="温度"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (核 sampling)",
        ),
    ],
    concurrency_limit=30  # 例: 同時に4つのリクエストを処理
)

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