Update test.py

test.py

@@ -30,15 +30,12 @@ def predict_hoax(title, content):
     label = 'HOAX' if pred == 1 else 'NON-HOAX'
     return label
 
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
-
 @st.cache_resource
 # LIME prediction function
 def predict_proba_for_lime(texts):
     results = []
     for text in texts:
-        inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=256).to(device)
+        inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=256)
         with torch.no_grad():
             outputs = model(**inputs)
         probs = softmax(outputs.logits, dim=1).detach().cpu().numpy()