Update app.py

app.py

@@ -54,6 +54,7 @@ class Application:
         # Calculate stat columns
         data["n_flagged_percent"] = 100 * (data["n_stars_flagged"] / data["n_stars"])
 
+        # Rename and organize columns
         data.columns = ["repo", "month", "clustered", "low activity", "total stars", "flagged stars", "flagged %"]
         return data[["repo", "month", "clustered", "low activity", "flagged stars", "total stars", "flagged %"]]
 
@@ -100,6 +101,9 @@ class Application:
     def parse(self, repos):
         """
         Parses and cleans the input repos string.
+
+        Returns:
+            list of repos
         """
 
         outputs = []
@@ -143,17 +147,17 @@ _4.5 Million (Suspected) Fake Stars in GitHub: A Growing Spiral of Popularity Co
 
 _[Paper](https://arxiv.org/abs/2412.13459) | [GitHub Project](https://github.com/hehao98/StarScout)_
 
-Note the disclaimer from the paper's author's.
+Note the disclaimer from the paper's authors.
 
 **Disclaimer**. _As we discussed in Section 3.4 and 3.5 in our paper, the resulting dataset are only repositories and users with suspected
 fake stars. The individual repositories and users in our dataset may be false positives. The main purpose of our dataset is for statistical
 analyses (which tolerates noises reasonably well), not for publicly shaming individual repositories. If you intend to publish subsequent work
 based on our dataset, please be aware of this limitation and its ethical implications._
 
-To add to the author's disclaimer.
+To add to the authors disclaimer.
 
-_It's also worth noting that projects that trend on popular sites and the GitHub trending page tend to attract lots of automated behavior.
-This is just a data point that shouldn't be used in a vacuum._ 
+_It's also worth noting that projects that trend on popular sites such as the GitHub Trending Page can attract a lot of automated behavior outside
+of a project's control. This dataset is just a data point that shouldn't be used in a vacuum._ 
 """
     )