Add answer source of app.py

app.py

@@ -0,0 +1,28 @@
+import numpy as np
+import pandas as pd
+from gensim.corpora import Dictionary, MmCorpus
+from gensim.models import LdaModel, Word2Vec
+import matplotlib.pyplot as plt
+import streamlit as st
+from pyLDAvis import prepared_data_to_html
+import pyLDAvis.gensim_models as gensimvis
+
+# 生データ・コーパス・辞書・モデルのロード
+df = pd.read_csv("./raw_corpus.csv")
+corpus = MmCorpus('./corpus.mm')
+dict = Dictionary.load(f'./livedoor_demo.dict')
+lda = LdaModel.load('./lda_demo.model')
+
+st.caption("生データ一覧")
+st.dataframe(df.iloc[:100])
+
+st.caption("記事のカテゴリ")
+fig, ax = plt.subplots()
+count = df[["CATEGORY", "DOCUMENT"]].groupby("CATEGORY").count()
+count.plot.pie(y="DOCUMENT", ax=ax, ylabel="", legend=False)
+st.pyplot(fig)
+
+# pyLDAvisによるトピックの可視化
+vis = gensimvis.prepare(lda, corpus, dict)
+html_string = prepared_data_to_html(vis)
+st.components.v1.html(html_string, width=1300, height=800)