File size: 862 Bytes
6e4b95f 9716ffb 6e4b95f 9716ffb 6e4b95f 9716ffb 6e4b95f 9716ffb 6e4b95f 9716ffb 6e4b95f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 |
import streamlit as st
from matplotlib import pyplot as plt
from wordcloud import WordCloud, STOPWORDS
import numpy as np
from app import AnalysisData
df = AnalysisData.ds.to_pandas(batched=False)
disaster_types = df['disaster_type'].unique()
text_data = {
disaster: ' '.join(df[df['disaster_type'] == disaster]['tweet_text'])
for disaster in disaster_types
}
for disaster in disaster_types:
st.subheader(disaster + ' ' + 'Word Cloud')
wordcloud = WordCloud(width=800, height=400).generate(text_data[disaster])
fig, ax = plt.subplots(figsize=(10, 5))
ax.imshow(wordcloud, interpolation='bilinear')
ax.axis('off')
st.pyplot(fig)
# DataSet links
st.subheader("DataSet links")
st.markdown("- [Humaid Dataset](https://crisisnlp.qcri.org/humaid_dataset?fbclid=IwAR2rpSdcVhcXvQagxAG5VA2dvwAUOJOCVwTKxqtDiz7soIhVMUtp_N0BfSo)") |