Update test.py

test.py

@@ -18,8 +18,7 @@ url = "https://www.kaggle.com/datasets/rahulgoel1106/xenophobia-on-twitter-durin
 url2 = "https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest"
 url3 = "https://vega.github.io/vega/docs/transforms/wordcloud/"
 url4 = "https://huggingface.co/spaces/jwu249/is445_final"
-st.write("Dataset Link to Download -> [Kaggle Covid-19 Xenophobic Datatset](%s)" % url)
-st.write("Trained Sentiment Analyzer -> [Huggicardiffnlp / twitter-roberta-base-sentiment-latest](%s)" % url2)
+
 
 multi = '''This visualization aims to help the public understand the xenophobia on Twitter during Covid-19,
 the data used is from the Kaggle dataset linked above. The data in this visualization has been ran through a trained data model set on Twitter for sentiment.
@@ -42,7 +41,10 @@ For the next part and the visual you're seeing now, it is called a word cloud wh
 the bigger the word becomes in the visual. To help you understand the word cloud better, I added different colors for each sentiments when you select them. Also I add small texts for the interactions to help you understand what they mean. Additionally, included are different sources for inspiration for this visual. So feel free to
 check them out if you want to learn more about word clouds. '''
 st.markdown(multi)
+st.write("Dataset Link to Download -> [Kaggle Covid-19 Xenophobic Datatset](%s)" % url)
+st.write("Trained Sentiment Analyzer -> [Huggicardiffnlp / twitter-roberta-base-sentiment-latest](%s)" % url2)
 st.write("Step 2 Expert Visualization: %s" % url4)
+st.header('''Sentiment Word Cloud''')
 # Load sentiment scores and cleaned text data
 data = pd.read_csv('sentiment_scores.csv')
 df = pd.read_csv('Xenophobia.csv', encoding='latin1', nrows=5000)