Update test.py

test.py

@@ -17,6 +17,7 @@ st.write('''### Group: Jason Wu''')
 url = "https://www.kaggle.com/datasets/rahulgoel1106/xenophobia-on-twitter-during-covid19"
 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)
 
@@ -41,6 +42,7 @@ 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("Expert Visualization: (%s)" % url)
 # Load sentiment scores and cleaned text data
 data = pd.read_csv('sentiment_scores.csv')
 df = pd.read_csv('Xenophobia.csv', encoding='latin1', nrows=5000)