Update app.py

app.py

@@ -28,7 +28,7 @@ def get_df() -> pd.DataFrame:
     df_stats = datasets.load_dataset("hysts-bot-data/daily-papers-stats", split="train").to_pandas()
     
     # Merge datasets on 'arxiv_id'
-    df = pd.merge(left=df_papers, right=df_stats, on="arxiv_id")
+    df = pd.merge(left=df_papers, right=df_stats, on="arxiv_id", suffixes=('_papers', '_stats'))
     
     # Reverse the DataFrame to have the latest papers first
     df = df[::-1].reset_index(drop=True)
@@ -164,7 +164,7 @@ class PaperManager:
         Calculate the score of a paper based on upvotes and age.
         This mimics the "hotness" algorithm used by platforms like Hacker News.
         """
-        upvotes = row.get('👍', 0)
+        upvotes = row.get('upvotes', 0)  # Corrected from '👍' to 'upvotes'
         date_str = row.get('date', datetime.datetime.now(timezone.utc).strftime("%Y-%m-%d"))
         try:
             published_time = datetime.datetime.strptime(date_str, "%Y-%m-%d").replace(tzinfo=timezone.utc)
@@ -190,8 +190,12 @@ class PaperManager:
             df = df[df["title"].str.contains(self.current_search_query, case=False, na=False)]
 
         if self.sort_method == "hot":
-            df['score'] = df.apply(self.calculate_score, axis=1)
-            df_sorted = df.sort_values(by='score', ascending=False).drop(columns=['score'])
+            if not df.empty:
+                df = df.drop(columns=['score'], errors='ignore')  # Remove existing 'score' column if present
+                df['score'] = df.apply(self.calculate_score, axis=1)
+                df_sorted = df.sort_values(by='score', ascending=False).drop(columns=['score'])
+            else:
+                df_sorted = df
         elif self.sort_method == "new":
             df_sorted = df.sort_values(by='date', ascending=False)  # Sort by 'date'
         elif self.sort_method == "top":
@@ -677,13 +681,6 @@ with demo:
         outputs=[paper_list]
     )
 
-    # Footer
-    gr.Markdown("""
-    Related useful Spaces:
-    - [Semantic Scholar Paper Recommender](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) by [davanstrien](https://huggingface.co/davanstrien)
-    - [ArXiv CS RAG](https://huggingface.co/spaces/bishmoy/Arxiv-CS-RAG) by [bishmoy](https://huggingface.co/bishmoy)
-    - [Paper Q&A](https://huggingface.co/spaces/chansung/paper_qa) by [chansung](https://huggingface.co/chansung)
-    """)
 
 
 # --- Launch the App ---