def get_output():
print('System: Initializing...')
from Models import abcnn_model
from Helpers.deployment_utils import extract_answer_from_sentences
sents = [
'john went to the bathroom',
'mary went to the kitchen',
'john moved to the hallway',
'kim journeyed to the garden',
'sandra is in the bedroom',
]
print('\nSystem: The context is:')
for sentence in sents:
print(sentence)
query = 'where is john'
print('System: The question is:')
print(query)
# Select Ans Sents - ABCNN
print('\nSentence Selection Module: Initializing...')
abcnn = abcnn_model()
ans_sents = abcnn.ans_select(query, sents)
print('\nSystem: Sentences scored by Sentence Selection Module')
for sentence,score in ans_sents:
print('{0:50}\t{1}'.format(sentence, score[0]))
print('')
best_ans, score, answers = extract_answer_from_sentences(
ans_sents,
query,
verbose=True,
)
ans_list = []
for x in answers:
ans_list.append({'word':x[0], 'score': x[1]})
print('\nSystem: Candidate answers scored by Answer Extraction Module')
for answer in ans_list:
print('{0:10}\t{1}'.format(answer['word'], answer['score']))
def __init__(self):
self.file = ''
self.context = []
self.query = ''
self.abcnn = abcnn_model(verbose=False)
# answers.append((word, w_score * a_score))
# answers = sorted(answers, key=operator.itemgetter(1))
# proc = subprocess.Popen(['python','test.py',query],shell=False,stdout=subprocess.PIPE)
ans_list = []
for x in answers:
ans_list.append({'word': x[0], 'score': x[1]})
ans_dict = {'answers': ans_list}
return ans_dict
app = Flask(__name__)
server = EdXServer()
abcnn = abcnn_model()
ans_select = AnsSelect()
dt_rnn = DT_RNN()
app.config['UPLOAD_FOLDER'] = os.path.join('./data/uploads')
@app.route('/filed', methods=['POST'])
def filer():
# data = request.get_json(force=True)
# filename = data['filename']
# file = data['file']
f = request.files['file']
f.save(
def get_query(self, query):
self.query = query
print(self.query)
# Filter top 5 paras using Info Retrieval
self.update({'val': 'Ranking Paragraphs using Information Retrieval.'})
para_select = infoRX.retrieve_info(self.context, self.query)
para_sents = []
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
print(type(para_select[0]), para_select[0])
self.update({'val': 'Tokenizing top ranked paragraphs'})
for para in para_select:
para_sents.extend(tokenizer.tokenize(para[0]))
print('Sentences selected by IR Module:')
print(para_sents)
val_list = []
for sent in para_sents:
val_list.append({'word': sent, 'score': '\b\b'})
self.update({
'val': 'Sentences selected by IR Module',
'answers': val_list
})
try:
# Select Ans Sents - ABCNN
self.update({'val': 'Ranking Candidate Answer Sentences.'})
abcnn = abcnn_model()
ans_sents = abcnn.ans_select(query, para_sents)
val_list = []
for sentence, score in ans_sents:
val_list.append({'word': sentence, 'score': score[0]})
self.update(
{
'val': 'Sentences scored by Sentence Selection Module',
'answers': val_list,
}, )
print('\nSystem: Sentences scored by Sentence Selection Module')
for sentence, score in ans_sents:
print('{0:50}\t{1}'.format(sentence, score[0]))
print('')
self.update({'val': 'Generating VDT and extracting Answer.'})
best_ans, score, answers = deploy.extract_answer_from_sentences(
ans_sents,
query,
verbose=True,
)
except Exception as e:
return {'answers': [{'word': 'ERROR', 'score': str(e)}]}
# Ignore: Phase 2-3: Input Module and Answer Module
# answers = []
# for ans, a_score in ans_sents.iteritems():
# words = deploy.extract_answer_from_sentence(ans, self.query)
# words = sorted(words, key=operator.itemgetter(1))
# for word, w_score in words.iteritems()[:5]:
# answers.append((word, w_score * a_score))
# answers = sorted(answers, key=operator.itemgetter(1))
# proc = subprocess.Popen(['python','test.py',query],shell=False,stdout=subprocess.PIPE)
ans_list = []
print('\nSystem: Candidate answers scored by Answer Extraction Module')
for x in answers[:5]:
print('{0:10}\t{1}'.format(x[0], float(x[1][0])))
ans_list.append({'word': x[0], 'score': float(x[1][0])})
ans_dict = {
'val': 'Candidate answers scored by Answer Extraction Module',
'answers': ans_list
}
return ans_dict