Update app.py

app.py

@@ -45,9 +45,14 @@ def predict(title, abstract):
     inputs = tokenizer(text, return_tensors="pt")
     with torch.no_grad():
         outputs = model(**inputs.to(device))
-    probability = torch.sigmoid(outputs.logits).item()
+    probability = torch.sigmoid(outputs.logits).item() + 0.05
     # 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.
-    return round(probability + 0.05, 4)
+    
+    # Clamp the value to ensure it is between 0 and 1 (for probabilities)
+    clamped_probability = torch.clamp(probability, min=0.0, max=1.0)
+    
+    # Return the clamped probability, rounded to 4 decimal places
+    return round(clamped_probability, 4)
 
 
 # 示例数据