Refactor format_search_results to return a dictionary with 'data' and 'headers' keys instead of a DataFrame

app.py

@@ -451,27 +451,27 @@ def find_synergistic_papers(abstract: str, limit=25) -> list[dict]:
     return papers
 
 
-def format_search_results(abstract: str) -> tuple[pd.DataFrame, list[dict]]:
-    """Format search results as a DataFrame for display"""
+def format_search_results(abstract: str) -> dict:
+    """Format search results as a dictionary with 'data' and 'headers' keys"""
     # Find papers synergistic with the given abstract
     papers = find_synergistic_papers(abstract)
 
     # Convert to DataFrame for display
-    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
-        ]
-    )
+    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"]
 
-    return df, papers  # Return both DataFrame and original data
+    return {"data": data, "headers": headers}
 
 
 def format_paper_as_markdown(paper: dict) -> str: