made an arangment of the device

app.py

@@ -5,6 +5,9 @@ import torch
 tokenizer = AutoTokenizer.from_pretrained("nebiyu29/fintunned-v2-roberta_GA")
 model = AutoModelForSequenceClassification.from_pretrained("nebiyu29/fintunned-v2-roberta_GA")
 
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+
 # Define a function to split a text into segments of 512 tokens
 def split_text(text):
     # Tokenize the text
@@ -47,10 +50,6 @@ def classify_text(text):
     # Initialize empty list for predictions
     predictions = []
 
-    # Move device to GPU if available
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model = model.to(device)
-
     # Loop through segments, process, and store predictions
     for segment in segments:
         inputs = tokenizer([segment], padding=True, return_tensors="pt")