demo.py

from keras.models import load_model
import numpy as np
from emo_utils import *


def sentences_to_indices(X, word_to_index, max_len):
    m = X.shape[0]
    X_indices = np.zeros((m, max_len))
    for i in range(m):
        sentence_words = X[i].lower().split()
        j = 0
        for w in sentence_words:
            X_indices[i, j] = word_to_index[w]
            j = j + 1
    return X_indices


model = load_model('checkpoints/my_model.h5')
word_to_index, index_to_word, word_to_vec_map = read_glove_vecs('data/glove.6B.50d.txt')
maxLen = 10

x_test = np.array(['not feeling happy','I love you so much','let us play ball'])
X_test_indices = sentences_to_indices(x_test, word_to_index, maxLen)
pred = model.predict(X_test_indices)
for i in range(len(x_test)):
    x = X_test_indices
    num = np.argmax(pred[i])
    print(' prediction: ' + x_test[i] + label_to_emoji(num).strip())