predict.py

def predict(text):
  max_length = 612
  encoded = tokenizer(text, truncation=True, padding=True, max_length=max_length, return_tensors='tf')

  pred = model.predict(dict(encoded), verbose=0)

  prob = tf.nn.softmax(pred.logits)[0]
  predictions = np.argmax(prob)

  predicted_label = label_encoder.inverse_transform([predictions])[0]
  return prob, predicted_label