model = tf.keras.models.load_model( 'models/model.h5' )
embedding_matrix = model.layers[0].get_weights()[0] # --- ( 1 )
print( 'Embedding Shape ~> {}'.format( embedding_matrix.shape ) )

# ------------ ( 2 ) ---------------------

word_index : dict = pickle.load( open( 'glove_embedding/tokenizer.pkl' , 'rb' ) ).word_index
word_index_2 = dict()
for word , index in word_index.items():
	word_index_2[ index ] = word
word_index = word_index_2
embedding_dict = dict()

# --------------- ( 3 ) ------------------

for i in range( len( embedding_matrix ) - 1 ):
	embedding_dict[ word_index[ i + 1 ] ] = embedding_matrix[ i + 1 ].tolist()

# ----------------( 4 ) -------------------

with open( 'android/embedding.json' , 'w' ) as file:
	json.dump( embedding_dict , file )