import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_ID = "rinna/bilingual-gpt-neox-4b-instruction-ppo"

# モデルをロード（8ビット量子化を使用せず）
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    device_map="auto"  # 自動でCPU/GPUを選択
)

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=False)

def generate_response(user_question,
                      chat_history,
                      temperature=0.3,
                      top_p=0.85,
                      max_new_tokens=2048,
                      repetition_penalty=1.05):

    user_prompt_template = "ユーザー: こんにちは、あなたは日本語を学ぶ手助けをしてくれるアシスタントやねん。質問するから、簡潔に答えてな。"
    system_prompt_template = "システム: うん、簡潔に答えるで。何を教えてほしいん？"


    user_sample = "ユーザー: 富士山の標高ってどれくらいなん？"
    system_sample = "システム: 富士山の標高は3776メートルやで。"

    user_prefix = "ユーザー: "
    system_prefix = "システム: "

    prompt = user_prompt_template + "\n" + system_prompt_template + "\n"

    if len(chat_history) < 1:
        prompt += user_sample + "\n" + system_sample + "\n"
    else:
        u = chat_history[-1][0]
        s = chat_history[-1][1]
        prompt += user_prefix + u + "\n" + system_prefix + s + "\n"

    prompt += user_prefix + user_question + "\n" + system_prefix

    inputs = tokenizer(prompt, add_special_tokens=False, return_tensors="pt")
    inputs = inputs.to(model.device)
    with torch.no_grad():
        tokens = model.generate(
            **inputs,
            temperature=temperature,
            top_p=top_p,
            max_new_tokens=max_new_tokens,
            repetition_penalty=repetition_penalty,
            do_sample=True,
            pad_token_id=tokenizer.pad_token_id,
            bos_token_id=tokenizer.bos_token_id,
            eos_token_id=tokenizer.eos_token_id
        )
    output = tokenizer.decode(tokens[0], skip_special_tokens=True)
    return output[len(prompt):]

import gradio as gr

with gr.Blocks() as demo:
    chat_history = gr.Chatbot()
    user_message = gr.Textbox(label="Question:", placeholder="人工知能とは何ですか？")
    clear = gr.ClearButton([user_message, chat_history])

    def response(user_message, chat_history):
        system_message = generate_response(user_message, chat_history)
        chat_history.append((user_message, system_message))
        return "", chat_history

    user_message.submit(response, inputs=[user_message, chat_history], outputs=[user_message, chat_history])

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