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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: カテゴリごとに10個を表示")
# ここに処理を書いてください。
for k,v in (df.groupby("CATEGORY").groups).items():
st.dataframe(df.iloc[v[:10]], height=200)
st.caption("記事のカテゴリ")
# ここに処理を書いてください。
count = df[["CATEGORY", "DOCUMENT"]].groupby("CATEGORY").count()
col1, col2 = st.columns(2)
with col1:
st.dataframe(count)
with col2:
fig, ax = plt.subplots()
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)
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