Revert 0529f34412eba1ce68c492559e72c506823c3094

app.py

@@ -451,27 +451,27 @@ def find_synergistic_papers(abstract: str, limit=25) -> list[dict]:
     return papers
 
 
-def format_search_results(abstract: str) -> dict:
-    """Format search results as a dictionary with 'data' and 'headers' keys"""
+def format_search_results(abstract: str) -> tuple[pd.DataFrame, list[dict]]:
+    """Format search results as a DataFrame for display"""
     # Find papers synergistic with the given abstract
     papers = find_synergistic_papers(abstract)
 
     # Convert to DataFrame for display
-    data = [
-        {
-            "Title": p["title"],
-            "Authors": p["authors"][:50] + "..." if len(p["authors"]) > 50 else p["authors"],
-            "Categories": p["categories"],
-            "Date": p["update_date"],
-            "Match Score": f"{int(p['synergy_score'] * 100)}%",
-            "ID": p["id"],  # Hidden column for reference
-        }
-        for p in papers
-    ]
-
-    headers = ["Title", "Authors", "Categories", "Date", "Match Score", "ID"]
+    df = pd.DataFrame(
+        [
+            {
+                "Title": p["title"],
+                "Authors": p["authors"][:50] + "..." if len(p["authors"]) > 50 else p["authors"],
+                "Categories": p["categories"],
+                "Date": p["update_date"],
+                "Match Score": f"{int(p['synergy_score'] * 100)}%",
+                "ID": p["id"],  # Hidden column for reference
+            }
+            for p in papers
+        ]
+    )
 
-    return {"data": data, "headers": headers}, papers
+    return df, papers  # Return both DataFrame and original data
 
 
 def format_paper_as_markdown(paper: dict) -> str: