Update app.py with transformer embeddings and prediction pipeline

app.py

@@ -75,6 +75,7 @@ def predict_with_gpflow(model, X):
     # Get predictions
     #predict_fn = model.predict_f_compiled
     predict_fn = model.signatures["serving_default"]
+    result = predict_fn(Xnew=X_tensor)  # Pass Xnew explicitly
     #mean, variance = predict_fn(Xnew=X_tensor)
     mean = result["output_0"].numpy()  # Adjust output key names if needed
     variance = result["output_1"].numpy()