def _predict():
print(' requests incoming..')
sentence = []
try:
input_string = word_tokenize(request.json["text"].lower())
sentence.append([VOCAB[w]
for w in input_string] + [VOCAB['EOS']])
dummy_label = LongVar([0])
sentence = LongVar(sentence)
input_ = [0], (sentence, ), (0, )
output, attn = model(input_)
#print(LABELS[output.max(1)[1]], attn)
nwords = len(input_string)
return jsonify({
"result": {
'sentence':
input_string,
'attn': [
'{:0.4f}'.format(i) for i in
attn.squeeze().data.cpu().numpy().tolist()[:-1]
],
'probs': [
'{:0.4f}'.format(i) for i in
output.exp().squeeze().data.cpu().numpy().tolist()
],
'label':
LABELS[output.max(1)[1].squeeze().data.cpu().numpy()]
}
})
except Exception as e:
print(e)
return jsonify({"result": "model failed"})
)))
VOCAB = Vocab(vocabulary, VOCAB, freq_threshold=100)
pprint(VOCAB.word2index)
if 'train' in sys.argv:
labelled_samples = [d for d in dataset if len(d.a) > 0] #[:100]
pivot = int( Config().split_ratio * len(labelled_samples) )
random.shuffle(labelled_samples)
train_set, test_set = labelled_samples[:pivot], labelled_samples[pivot:]
train_set = sorted(train_set, key=lambda x: -len(x.a + x.story))
test_set = sorted(test_set, key=lambda x: -len(x.a + x.story))
exp_image = experiment(VOCAB, dataset, datapoints=[train_set, test_set])
if 'predict' in sys.argv:
model = BiLSTMDecoderModel(Config(), len(VOCAB), len(LABELS))
if Config().cuda: model = model.cuda()
model.load_state_dict(torch.load('{}.{}'.format(SELF_NAME, '.pth')))
start_time = time.time()
strings = sys.argv[2]
s = [WORD2INDEX[i] for i in word_tokenize(strings)] + [WORD2INDEX['PAD']]
e1, e2 = [WORD2INDEX['ENTITY1']], [WORD2INDEX['ENTITY2']]
output = model(s, e1, e2)
output = output.data.max(dim=-1)[1].cpu().numpy()
label = LABELS[output[0]]
print(label)
duration = time.time() - start_time
print(duration)
exp_image = experiment(
config,
ROOT_DIR,
model,
VOCAB,
LABELS,
datapoints=[train_set, train_set + test_set, train_set + test_set])
if 'predict' in sys.argv:
print('=========== PREDICTION ==============')
model.eval()
count = 0
while True:
count += 1
sentence = []
input_string = word_tokenize(input('?').lower())
sentence.append([VOCAB[w] for w in input_string] + [VOCAB['EOS']])
dummy_label = LongVar([0])
sentence = LongVar(sentence)
input_ = [0], (sentence, ), (0, )
output, attn = model(input_)
print(LABELS[output.max(1)[1]])
if 'show_plot' in sys.argv or 'save_plot' in sys.argv:
nwords = len(input_string)
from matplotlib import pyplot as plt
plt.figure(figsize=(20, 10))
plt.bar(range(nwords + 1), attn.squeeze().data.cpu().numpy())
plt.title('{}\n{}'.format(output.exp().tolist(),