fine-tuned cloud

app.py

@@ -3,6 +3,7 @@ import pandas as pd
 from github import Github
 from wordcloud import WordCloud
 import matplotlib.pyplot as plt
+import re
 
 g = Github(st.secrets["ACCESS_TOKEN"])
 repo = g.get_repo(st.secrets["REPO_NAME"])
@@ -53,14 +54,14 @@ closed_issues = df.loc[df['State'] == 'closed']
 closed_issues['Time to Close'] = closed_issues['Closed at'] - closed_issues['Created at']
 
 with col1:
-    st.metric(label="Open Issues", value=state_counts['open'])
+    st.metric(label="Open issues", value=state_counts['open'])
 
 with col2:
-    st.metric(label="Closed Issues", value=state_counts['closed'])
+    st.metric(label="Closed issues", value=state_counts['closed'])
 
 with col3:
     average_time_to_close = closed_issues['Time to Close'].mean().days
-    st.metric(label="Avg. Days to Close", value=average_time_to_close)
+    st.metric(label="Avg. days to close", value=average_time_to_close)
 
 
 # TODO Plot: number of open vs closed issues by date
@@ -120,13 +121,13 @@ with col1:
             "Link": st.column_config.LinkColumn("Link", display_text="🔗")
         }
     )
-
-## Dataframe: Number of open bugs ordered by date
     
 ## Cloud of words: Issue titles
 with col2:
-    st.subheader("Word cloud of issue titles")
-    wordcloud = WordCloud(width=800, height=400, background_color="white").generate(" ".join(open_issues["Issue"]))
+    st.subheader("Cloud of words ☁️")
+    titles = " ".join(open_issues["Issue"])
+    titles = re.sub(r'\[.*?\]', '', titles)
+    wordcloud = WordCloud(width=800, height=400, background_color="white").generate(titles)
     plt.figure(figsize=(10, 5))
     plt.imshow(wordcloud, interpolation="bilinear")
     plt.axis("off")