BentoML: TypeError: 'TfKerasModelArtifact' object is not iterable

Hi there. Here’s my packaging code:

%%writefile text_classification_service.py
import pandas as pd
from tensorflow import keras
from sklearn.preprocessing import LabelEncoder
from sklearn.feature_extraction.text import CountVectorizer
from string import digits
from bentoml import api, env, BentoService, artifacts
from bentoml.artifact import TfKerasModelArtifact
from bentoml.handlers import JsonHandler

@artifacts([TfKerasModelArtifact('model')])
@env(conda_dependencies=['tensorflow', 'pandas', 'scikit-learn'])
class TextClassificationService(BentoService):
    
    def vectorizer():
        vectorizer = CountVectorizer(stop_words=None, lowercase=True,
                             ngram_range=(1, 1), min_df=2, binary=True)
        
        train = pd.read_csv('https://raw.githubusercontent.com/Nilabhra/kolkata_nlp_workshop_2019/master/data/train.csv')
        vectorizer.fit_transform(train['text'])
        return vectorizer
    
    def remove_digits(s):
        remove_digits = str.maketrans('', '', digits)
        res = s.translate(remove_digits)
        return res
    
    @api(JsonHandler)
    def predict(self, parsed_json):
        text = parsed_json['text']
        text = remove_digits(text)
        vectorizer = vectorizer()
        text = vectorizer.transform(text)
        prediction =  self.artifacts.model.predict_classes(text)
        response = {'Sentiment': prediction}
        return response

And when I am building the archive using -

from text_classification_service import TextClassificationService

svc = TextClassificationService.pack(model=model)
saved_path = svc.save('/tmp/bento')
print(saved_path)

It gives me the following error (with trace):

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-28-39d1075ec116> in <module>
      1 from text_classification_service import TextClassificationService
      2 
----> 3 svc = TextClassificationService.pack(model=model)
      4 saved_path = svc.save('/tmp/bento')
      5 print(saved_path)

/miniconda3/lib/python3.7/site-packages/bentoml/service.py in pack(cls, *args, **kwargs)
    320         artifacts = ArtifactCollection()
    321 
--> 322         for artifact_spec in cls._artifacts_spec:
    323             if artifact_spec.name in kwargs:
    324                 artifact_instance = artifact_spec.pack(kwargs[artifact_spec.name])

TypeError: 'TfKerasModelArtifact' object is not iterable

Here is the code for model building, compilation and fitting:

model = keras.Sequential()

model.add(Dropout(rate=0.2, input_shape=features.shape[1:]))
for _ in range(2):
        model.add(Dense(units=64, activation='relu'))
        model.add(Dropout(rate=0.2))
model.add(Dense(units=1, activation='sigmoid'))

model.compile(optimizer='adam',
              loss='binary_crossentropy',
              metrics=['acc'])

es_cb = keras.callbacks.EarlyStopping(monitor='val_loss', patience=5)

model.fit(features,
                    labels,
                    epochs=15,
                    batch_size=512,
                    validation_data=(test_features, test_labels),
                    callbacks=[es_cb],
                    verbose=1)

Is there any ArtiFact I am missing here?