app.py

from typing import List, Tuple

import gradio as gr
import numpy as np
import torch
from transformers import AutoModelForCausalLM, T5Tokenizer

device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
tokenizer = T5Tokenizer.from_pretrained("rinna/japanese-gpt2-medium")
tokenizer.do_lower_case = True

model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt2-medium")
model.to(device)


def calculate_surprisals(
    input_text: str, normalize_surprisals: bool = True
) -> Tuple[float, List[Tuple[str, float]]]:
    input_tokens = [
        token.replace("▁", "")
        for token in tokenizer.tokenize(input_text)
        if token != "▁"
    ]
    input_ids = tokenizer.encode(
        "<s>" + input_text, add_special_tokens=False, return_tensors="pt"
    ).to(device)

    logits = model(input_ids)["logits"].squeeze(0)

    surprisals = []
    for i in range(logits.shape[0] - 1):
        if input_ids[0][i + 1] == 9:
            continue
        logit = logits[i]
        prob = torch.softmax(logit, dim=0)
        neg_logprob = -torch.log(prob)
        surprisals.append(neg_logprob[input_ids[0][i + 1]].item())
    mean_surprisal = np.mean(surprisals)

    if normalize_surprisals:
        min_surprisal = np.min(surprisals)
        max_surprisal = np.max(surprisals)
        surprisals = [
            (surprisal - min_surprisal) / (max_surprisal - min_surprisal)
            for surprisal in surprisals
        ]
        assert min(surprisals) >= 0
        assert max(surprisals) <= 1

    tokens2surprisal: List[Tuple[str, float]] = []
    for token, surprisal in zip(input_tokens, surprisals):
        tokens2surprisal.append((token, surprisal))

    return mean_surprisal, tokens2surprisal


def highlight_token(token: str, score: float):
    html_color = "#%02X%02X%02X" % (255, int(255 * (1 - score)), int(255 * (1 - score)))
    return '<span style="background-color: {}; color: black">{}</span>'.format(
        html_color, token
    )


def create_highlighted_text(tokens2scores: List[Tuple[str, float]]):
    highlighted_text: str = ""
    for token, score in tokens2scores:
        highlighted_text += highlight_token(token, score)
    highlighted_text += "<br><br>"
    return highlighted_text


def main(input_text: str) -> Tuple[float, str]:
    mean_surprisal, tokens2surprisal = calculate_surprisals(
        input_text, normalize_surprisals=True
    )
    highlighted_text = create_highlighted_text(tokens2surprisal)
    return round(mean_surprisal, 2), highlighted_text


if __name__ == "__main__":
    demo = gr.Interface(
        fn=main,
        title="読みにくい箇所を検出するAI（デモ）",
        description="テキストを入力すると、読みにくさに応じてハイライトされて出力されます。",
        inputs=gr.inputs.Textbox(
            lines=5, label="テキスト", placeholder="ここにテキストを入力してください。"
        ),
        outputs=[
            gr.Number(label="文全体の読みにくさ（サプライザル）"),
            gr.outputs.HTML(label="トークン毎サプライザル"),
        ],
    )
    demo.launch()