Spread inputs

app.py

@@ -40,7 +40,7 @@ def predict(text) -> Tuple[Dict, float]:
   model.eval()
   with torch.inference_mode():
     # Pass tokenized text through the model and turn the prediction logits into probaiblities
-    pred_probs = torch.softmax(model(X).logits, dim=1)
+    pred_probs = torch.softmax(model(**X).logits, dim=1)
 
   # Create a prediction label and prediction probability dictionary
   pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}