from all_features import getFeatures
import numpy as np
import pickle
novel = input(
    'Please type the title of the novel which stored in a .txt file: ')
features = getFeatures(novel)
np.savez_compressed(novel + '_features.npz', features)
nn = pickle.load(open('NeuralNet_model.pkl', 'rb'))
D = np.load(novel + '_features.npz')['arr_0']
prediction = nn.predict(D)
probs = nn.predict_proba(D)
simple_predicts = []
scale_predicts = []
for i in prediction:
    simple_predicts.append(i[0])
for i in probs:
    scale_predicts.append(i[1] * 10)
    compare = []
print('Simple prediction (suspenseful / unsuspensful) paragraph:\n',
    simple_predicts, '\n\n')
print('Scale prediction for each paragraph:\n', scale_predicts)

4318d7cbbf40ed076e03401bcf46b437b96cfb98
<mask token>

<mask token>
np.savez_compressed(novel + '_features.npz', features)
<mask token>
for i in prediction:
    simple_predicts.append(i[0])
for i in probs:
    scale_predicts.append(i[1] * 10)
    compare = []
print('Simple prediction (suspenseful / unsuspensful) paragraph:\n',
    simple_predicts, '\n\n')
print('Scale prediction for each paragraph:\n', scale_predicts)

<mask token>
novel = input(
    'Please type the title of the novel which stored in a .txt file: ')
features = getFeatures(novel)
np.savez_compressed(novel + '_features.npz', features)
nn = pickle.load(open('NeuralNet_model.pkl', 'rb'))
D = np.load(novel + '_features.npz')['arr_0']
prediction = nn.predict(D)
probs = nn.predict_proba(D)
simple_predicts = []
scale_predicts = []
for i in prediction:
    simple_predicts.append(i[0])
for i in probs:
    scale_predicts.append(i[1] * 10)
    compare = []
print('Simple prediction (suspenseful / unsuspensful) paragraph:\n',
    simple_predicts, '\n\n')
print('Scale prediction for each paragraph:\n', scale_predicts)

from all_features import getFeatures
import numpy as np
import pickle
novel = input(
    'Please type the title of the novel which stored in a .txt file: ')
features = getFeatures(novel)
np.savez_compressed(novel + '_features.npz', features)
nn = pickle.load(open('NeuralNet_model.pkl', 'rb'))
D = np.load(novel + '_features.npz')['arr_0']
prediction = nn.predict(D)
probs = nn.predict_proba(D)
simple_predicts = []
scale_predicts = []
for i in prediction:
    simple_predicts.append(i[0])
for i in probs:
    scale_predicts.append(i[1] * 10)
    compare = []
print('Simple prediction (suspenseful / unsuspensful) paragraph:\n',
    simple_predicts, '\n\n')
print('Scale prediction for each paragraph:\n', scale_predicts)
