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| import streamlit as st | |
| import pickle as pkl | |
| import tensorflow as tf | |
| from tensorflow import keras | |
| from tensorflow.keras import layers | |
| # retrieve text vectorization layer | |
| tv_spec = pkl.load(open('model/tv_layer.pkl', 'rb')) | |
| text_vectorizer = layers.TextVectorization.from_config(tv_spec['config']) | |
| text_vectorizer.set_weights(tv_spec['weights']) | |
| # function to create model Simple DNN | |
| def get_model(hidden_dim = 8): | |
| inputs = keras.Input(shape=(35,), dtype = "int64") | |
| x = layers.Dense(hidden_dim, activation = "relu")(inputs) | |
| outputs = layers.Dense(1, activation = 'sigmoid')(x) | |
| model = keras.Model(inputs, outputs) | |
| model.compile( | |
| loss = "binary_crossentropy", | |
| optimizer = keras.optimizers.Adam(learning_rate = 0.01), | |
| metrics = ['accuracy'] | |
| ) | |
| return model | |
| model = get_model() | |
| model.load_weights('model/dnn_model.h5') | |
| # get input | |
| text = st.text_input('check if you\'re the \U0001F437', 'Hi Disky, how is your business doing?') | |
| if text: | |
| text = [text] | |
| text_vector = text_vectorizer(text) | |
| out = model.predict(text_vector)[0][0] | |
| st.write('Your \U0001F437 score is', out) | |
| if out > 0.5: | |
| st.write('I smell \U0001F953') | |
| else: st.write('Well, think twice, think twice.') |