Hi folks,
I have trained a model (via Keras framework), exported it with model.save('model.hdf5') and now I want to integrate it with the awesome Streamlit.
Obviously, I do not want to load the model every time the end-user insert a new input, but to load it once and for all.
so my code looks something like that:

@st.cache
def load_my_model():
    model = load_model('model.hdf5')
    model.summary()
    return model
if __name__ == '__main__':
    st.title('My first app')
    sentence = st.text_input('Input your sentence here:')
    model = load_my_model()
    if sentence:
        y_hat = model.predict(sentence)
In that way I got:
“streamlit.errors.UnhashableType: <exception str() failed>”
exception.

I tried to use @st.cache(allow_output_mutation=True) and when I run a query at the streamlit page. I got:
“TypeError: Cannot interpret feed_dict key as Tensor: Tensor Tensor(“input_1:0”, shape=(?, 80), dtype=int32) is not an element of this graph.”
(Of-course without any cache decorators the model is loaded and works fine)
HOW should I properly load and cache a Keras trained model?
Python ver: 2.7 (unfortunately)

Keras ver: 2.1.3

Tensorflow ver: 1.3.0

Streamlit ver: 0.55.2
Many thanks!
              
Hi @tc1,

Unfortunately the src that I use is not trevial to be migrated to python 3.6 (and probably therefore cannot upgrade my streamlit version).

Any suggestions with my current versions?
Thank you.
    model = load_model(MODEL_PATH)
    model._make_predict_function()
    model.summary()  # included to make it visible when model is reloaded
    session = K.get_session()
    return model, session
if __name__ == '__main__':
    st.title('My first app')
    sentence = st.text_input('Input your sentence here:')
    model, session = load_model()
    if sentence:
        K.set_session(session)
        y_hat = model.predict(sentence)
              
Thanks for the solution. We should use this method instead, in Tensorflow 2 (due to the removal of session on tf2) :
import tensorflow.keras.backend as K
@st.cache(allow_output_mutation=True)
def load_model():
    model = load_model(MODEL_PATH)
    model._make_predict_function()
    model.summary()  # included to make it visible when model is reloaded
    return model
if __name__ == '__main__':
    st.title('My first app')
    sentence = st.text_input('Input your sentence here:')
    model= load_model()
    if sentence:
        y_hat = model.predict(sentence)
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