pei223

import ky from "ky";

const baseApi = ky
  .create({
    prefixUrl: "http://localhost:8080",
    headers: { "Content-Type": "application/json" },
  });

type TodoList = {
  items: Todo[];

pei223

import json
import time
import uuid
from azure.storage.blob import BlobServiceClient, ContainerClient, __version__

# ストレージアカウントの接続文字列
connect_str = ""
# データストレージのコンテナ名
container_name = ""

pei223

import gensim

words_list = [["単語", "英語"], ["日本語", "野球"], ]  # 単語リストのリスト
max_document_frequency, min_document_frequency = 1.0, 0.0  # 単語の出現頻度の上限下限
words_dictionary = gensim.corpora.Dictionary(words_list)  # 単語辞書を生成
words_dictionary.filter_extremes(no_above=max_document_frequency, no_below=min_document_frequency)
corpus = [words_dictionary.doc2bow(words) for words in words_list]  # 単語辞書を使って単語リストをBoW表現にする
lda_model = gensim.models.ldamodel.LdaModel(
    corpus=corpus,
    num_topics=num_topics,

pei223

from typing import List, Dict
from wordcloud import WordCloud
import matplotlib.pylab as plt
import math


class WordCloudGenerator:
    def __init__(self):
        self.word_cloud = None

pei223

from typing import Dict, List, Tuple
import numpy as np
from scipy import sparse as sp

from word_analyze import tokenize


def is_key_exist(d: Dict, key: any):
    return d.get(key) is not None