|
|
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) |
|
|
|
|
|
|
|
|
|
|
|
st.subheader("DataSet links") |
|
|
st.markdown("- [Humaid Dataset](https://crisisnlp.qcri.org/humaid_dataset?fbclid=IwAR2rpSdcVhcXvQagxAG5VA2dvwAUOJOCVwTKxqtDiz7soIhVMUtp_N0BfSo)") |
