Update app.py

app.py

@@ -23,6 +23,9 @@ from langchain_core.runnables import RunnableParallel, RunnablePassthrough
 from langchain_core.documents import Document
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import cosine_similarity
+from openpyxl import load_workbook
+from openpyxl.utils.dataframe import dataframe_to_rows
+
 
 
 huggingface_token = os.environ.get("HUGGINGFACE_TOKEN")
@@ -461,18 +464,30 @@ def export_news_to_excel():
     if 'relevance_score' not in df.columns:
         df['relevance_score'] = 0.0
     else:
-        df['relevance_score'] = df['relevance_score'].astype(float)
+        df['relevance_score'] = pd.to_numeric(df['relevance_score'], errors='coerce').fillna(0.0)
     
     # Reorder columns to put relevance_score after summary
     columns = ['published_date', 'title', 'url', 'content', 'summary', 'relevance_score']
     df = df[columns]
     
-    # Format relevance_score to display 2 decimal places
-    df['relevance_score'] = df['relevance_score'].apply(lambda x: f"{x:.2f}")
-    
     with NamedTemporaryFile(delete=False, suffix='.xlsx') as tmp:
         excel_path = tmp.name
-        df.to_excel(excel_path, index=False)
+        
+        # First, save the DataFrame to Excel
+        df.to_excel(excel_path, index=False, engine='openpyxl')
+        
+        # Then, load the workbook and modify the relevance_score column
+        wb = load_workbook(excel_path)
+        ws = wb.active
+        
+        for row in dataframe_to_rows(df, index=False, header=True):
+            ws.append(row)
+        
+        # Format the relevance_score column as numbers
+        for cell in ws['F'][1:]:  # Assuming relevance_score is in column F
+            cell.number_format = '0.00'
+        
+        wb.save(excel_path)
     
     return excel_path