First draft

app.py

@@ -2,16 +2,107 @@ import streamlit as st
 import pandas as pd
 from github import Github
 from datetime import datetime, timedelta
+import time
 
-def get_issues(state):
-    g = Github(st.secrets["ACCESS_TOKEN"])
-    repo = g.get_repo(st.secrets["REPO_NAME"])
-    issues = repo.get_issues(state=state)
-    return issues
+g = Github(st.secrets["ACCESS_TOKEN"])
+repo = g.get_repo(st.secrets["REPO_NAME"])
 
+def fetch_data():
 
-open_issues = get_issues(state="open")
-st.metric("Open Issues", open_issues.totalCount)
+    issues_data = []
+    
+    issues = repo.get_issues(state="all")
+    
+    for issue in issues:
+        issues_data.append(
+            {
+                'Number': issue.number,
+                'Title': issue.title, 
+                'State': issue.state, 
+                'Created at': issue.created_at, 
+                'Closed at': issue.closed_at,
+                'Labels': [label.name for label in issue.labels],
+                'Reactions': issue.reactions['total_count'],
+                'Comments': issue.comments,
+                'URL': issue.html_url
+            }
+        )
+    return pd.DataFrame(issues_data)
 
-closed_issues = get_issues(state="closed", type="issue")
-st.metric("Closed Issues", closed_issues.totalCount)
+def save_data(df):
+    df.to_json("issues.json", orient="records", indent=4, index=False)
+
+
+st.title(f"GitHub Issues Dashboard for {repo.name}")
+
+fetching_data = st.empty()
+
+df = pd.read_json("issues.json")
+
+status = st.status("Loading data...", status="running")
+
+# Section 1: Issue activity metrics
+st.header("Issue activity metrics")
+
+col1, col2 = st.columns(2)
+
+state_counts = df['State'].value_counts()
+open_issues = df.loc[df['State'] == 'open']
+
+with col1:
+    st.metric(label="Open Issues", value=state_counts['open'])
+
+with col2:
+    st.metric(label="Closed Issues", value=state_counts['closed'])
+   
+
+# # TODO Plot: number of open vs closed issues by date
+   
+# # TODO Dataframe: Unresolved conversations
+# ## Issues with new comments. Sorted by number of new comments (based on timeframe above) and/or date of last comment.
+
+# Section 2: Issue classification
+st.header("Issue classification")
+
+## Dataframe: Number of open issues by label.
+st.subheader("Top ten labels by number of open issues:")
+open_issues_exploded = open_issues.explode("Labels")
+label_counts = open_issues_exploded.value_counts("Labels")
+st.dataframe(label_counts.head(10))
+
+# ## Dataframe: Number of open bugs by severity level. Critical, major, minor
+# ## Cloud of words: Issue titles and description
+
+# # Community engagement
+st.header("Community engagement")
+# ## Dataframe: Latest issues open by the community
+# ## Dataframe: issues sorted by number of comments
+engagement_df = df[["Number","Title","Reactions","Comments","URL"]].sort_values(by=["Reactions", "Comments"], ascending=False).head(10)
+st.dataframe(
+    engagement_df,
+    hide_index=True,
+    use_container_width=True,
+    column_config={
+        "Number": st.column_config.NumberColumn("No."),
+        "Title": st.column_config.TextColumn("Title"),
+        "Reactions": st.column_config.NumberColumn("Reactions", format="%d 👍"),
+        "Comments": st.column_config.NumberColumn("Comments", format="%d 💬"),
+        "URL": st.column_config.LinkColumn("🔗", display_text="🔗")
+    }
+)
+
+# ## Cloud of words: Comments??
+# ## Dataframe: issues sorted by number of reactions.
+# ## Dataframe: Contributor leaderboard.
+
+# # Issue dependencies
+# st.header("Issue dependencies")
+# ## Map: dependencies between issues. Network of issue mentions.x
+
+status.update(label="Checking for updated data...", status="running")
+updated_data = fetch_data()
+if not df.equals(updated_data):
+    save_data(updated_data)
+    status.update(label="Refresh for updated data!", status="complete")
+else:
+    status.update(label="Data is up to date!", status="complete")