Update app.py with transformer embeddings and prediction pipeline

app.py

@@ -74,7 +74,7 @@ def predict_with_gpflow(model, X):
     print (X_tensor.shape)
     # Get predictions
     predict_fn = model.predict_f_compiled
-    mean, variance = predict_fn(X_new=X_tensor)
+    mean, variance = predict_fn(Xnew=X_tensor)
 
     # Return mean and variance as numpy arrays
     return mean.numpy().flatten(), variance.numpy().flatten()