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  File "/home/kamleshsahoo/mysite/flask_app.py", line 23, in sentiment_analyzer
    model = tf.keras.models.load_model('/home/kamleshsahoo/mysite/saved_models/model2')
  File "/usr/local/lib/python3.10/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/usr/local/lib/python3.10/site-packages/keras/saving/saved_model/load.py", line 532, in _revive_layer_or_model_from_config
    raise RuntimeError(
RuntimeError: Unable to restore object of class 'TextVectorization' likely due to name conflict with built-in Keras class '<class 'keras.layers.preprocessing.text_vectorization.TextVectorization'>'. To override the built-in Keras definition of the object, decorate your class with `@keras.utils.register_keras_serializable` and include that file in your program, or pass your class in a `keras.utils.CustomObjectScope` that wraps this load call.

  from flask import Flask, request, jsonify
  from flask_cors import CORS, cross_origin
  import tensorflow as tf
  import numpy as np

  app = Flask(__name__)
  cors = CORS(app)

  @app.route('/', methods=['POST'])
  @cross_origin()
  def sentiment_analyzer():
    numeric_to_sentiment = {0: 'negative', 1: 'neutral', 2: 'positive'}
    data = request.get_json()
    news = data.get('news')
    ## fails at next line
    model = tf.keras.models.load_model('/home/kamleshsahoo/mysite/saved_models/model2')
    pred = model.predict([news])
    senti = numeric_to_sentiment[np.argmax(pred)]

  return jsonify(sentiment=senti)