Update app.py

app.py

@@ -11,6 +11,9 @@ from datetime import date, timedelta
 from transformers import pipeline
 import torch
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from contextlib import redirect_stdout
+from io import StringIO
+
 
 
 def scrapper (start_date):
@@ -124,7 +127,12 @@ if tickerSymbol:
 
     
     Begindatestring = start_date
-    headlines = scrapper(start_date)
+    # Create a dummy stream to suppress output
+    dummy_stream = StringIO()
+
+    with redirect_stdout(dummy_stream):
+        headlines = scrapper(start_date)
+    
     index = [idx for idx, s in enumerate(headlines) if s=='Most Read' ][0]
     del headlines[index:]
     news = pd.DataFrame({"News": headlines})
@@ -139,6 +147,7 @@ if tickerSymbol:
     tokenizer = AutoTokenizer.from_pretrained("nickmuchi/sec-bert-finetuned-finance-classification")
     model = AutoModelForSequenceClassification.from_pretrained("nickmuchi/sec-bert-finetuned-finance-classification")
 
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
     nlp = pipeline("text-classification", model=model, tokenizer=tokenizer, device=device)