Update app.py with transformer embeddings and prediction pipeline

app.py

@@ -73,7 +73,8 @@ def predict_with_gpflow(model, X):
     X_tensor = tf.convert_to_tensor(X, dtype=tf.float64)
     print (X_tensor.shape)
     # Get predictions
-    predict_fn = model.predict_f_compiled
+    #predict_fn = model.predict_f_compiled
+    predict_fn = model.signatures["serving_default"]
     mean, variance = predict_fn(Xnew=X_tensor)
 
     # Return mean and variance as numpy arrays