import os

# コマンドを実行する
os.system("pip install transformers torch psutil")

# コマンドの実行結果を取得する（stdoutとstderrは出力されない）
result = os.system("pip install transformers")

import os

# コマンドを実行する
os.system("pip install transformers torch psutil")

# コマンドの実行結果を取得する（stdoutとstderrは出力されない）
result = os.system("pip install transformers")



from transformers import AutoModel, AutoTokenizer, trainer_utils
import gradio as gr
import psutil

device = "cpu"
model = AutoModel.from_pretrained("Tanrei/GPTSAN-japanese").to(device)
tokenizer = AutoTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
trainer_utils.set_seed(30)

def get_memory_usage():
    process = psutil.Process()
    memory_usage = process.memory_info().rss / 1024 / 1024  # メモリ使用量をMB単位で取得
    return f"Memory Usage: {memory_usage:.2f} MB"
  def generate_text(input_text, num_repeats):
    usag=get_memory_usage()
    x_token = tokenizer("", prefix_text=input_text, return_tensors="pt")
    input_ids = x_token.input_ids.to(device)
    token_type_ids = x_token.token_type_ids.to(device)
    gen_token = model.generate(input_ids, token_type_ids=token_type_ids, max_new_tokens=10)
    output_text = tokenizer.decode(gen_token[0])
    repeated_text = output_text
    for _ in range(num_repeats):
        x_token = tokenizer("", prefix_text=repeated_text, return_tensors="pt")
        input_ids = x_token.input_ids.to(device)
        token_type_ids = x_token.token_type_ids.to(device)
        gen_token = model.generate(input_ids, token_type_ids=token_type_ids, max_new_tokens=10)
        repeated_text += tokenizer.decode(gen_token[0])
    return repeated_text



input_text = gr.inputs.Textbox(lines=5, label="Input Text")
num_repeats = gr.inputs.Number(default=1, label="Number of Repeats")
output_text = gr.outputs.Textbox(label="Generated Text")

interface = gr.Interface(
    fn=generate_text,
    inputs=[input_text, num_repeats],
    outputs=output_text,
    title=get_memory_usage(),
    description="Enter a prompt in Japanese to generate text."
)