gradio

import gradio as gr

def EmotionPrediction(emotion):
  input_text_tokenized = bert_tokenizer.encode(emotion,
                                               truncation=True,
                                               padding='max_length',
                                               return_tensors='tf')
  bert_predict = bert_load_model(input_text_tokenized)
  bert_output = tf.nn.softmax(bert_predict[0], axis=-1)

  Emotions = ['joy','anger','surprise', 'neutral', 'disgust', 'sadness', 'fear', 'shame']

  label = tf.argmax(bert_output, axis=1)
  label = label.numpy()

  return Emotions[label[0]]

iface = gr.Interface(fn=EmotionPrediction, inputs='text', outputs='text')
iface.launch()