Update app.py

app.py

@@ -19,15 +19,13 @@ model.eval()
 
 
 
-@spaces.GPU
+@spaces.GPU(duration=60, enable_queue=True)
 def predict(title, abstract):
     model_device = next(model.parameters()).device
     text = f'''Given a certain paper, Title: {title}\n Abstract: {abstract}. \n Predict its normalized academic impact (between 0 and 1):'''
-    inputs = tokenizer(text, return_tensors="pt").to(model_device)
-
+    inputs = tokenizer(text, return_tensors="pt")
     with torch.no_grad():
         outputs = model(**inputs)
-
     probability = torch.sigmoid(outputs.logits).item()
     # reason for +0.05: We observed that the predicted values in the web demo are generally around 0.05 lower than those in the local deployment (due to differences in software/hardware environments). Therefore, we applied the following compensation in the web demo. Please do not use this in the local deployment.
     if probability + 0.05 >=1.0: