update app with model in .h5

app.py

@@ -4,12 +4,25 @@ import tensorflow as tf
 from tensorflow.keras import layers
 
 # retrieve text vectorization layer 
-tv_spec = pkl.load(Open('saved_model/tv_layer.pkl', 'rb'))
+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'])
 
-# load model
-model = tf.keras.models.load_model('saved_model/dnn_model')
+# 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?')

saved_model/dnn_model/.DS_Store → model/dnn_model.h5

@@ -1,3 +1,3 @@
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+oid sha256:2b8797eed0f2ed83f8a468a9ffc57d6aa47fea50581a0382ec6bb7abed0541ca
+size 14824

saved_model/dnn_model/keras_metadata.pb

@@ -1,3 +0,0 @@
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saved_model/dnn_model/saved_model.pb

@@ -1,3 +0,0 @@
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saved_model/dnn_model/variables/variables.data-00000-of-00001

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-size 7946

saved_model/dnn_model/variables/variables.index

@@ -1,3 +0,0 @@
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