def fit_suggestions(token, document):
send_json({
'action': 'fit',
'token': token,
'document': document
}, settings.NETWORK['recommendation']['host'],
settings.NETWORK['recommendation']['port'])
def build(path):
dic = {'action': 'build', 'path': path}
success = {
send_json(dic, settings.NETWORK['models']['host'],
settings.NETWORK['models']['port'], True)['success'],
send_json(dic, settings.NETWORK['recommendation']['host'],
settings.NETWORK['recommendation']['port'], True)['success'],
send_json(dic, settings.NETWORK['summaries']['host'],
settings.NETWORK['summaries']['port'], True)['success']
}
return False not in success
def _build_index(self, freq, terms):
index = [{'key': term, 'value': {'documents': []}} for term in terms]
freq = np.array(freq.todense())
it = np.nditer(freq, flags=['multi_index'])
while not it.finished:
f = int(it[0])
i, j = it.multi_index
if f:
index[i]['value']['documents'].append({'document': self.doc_names[j], 'freq': f})
it.iternext()
utils.send_json({
'action': 'create',
'data': index
}, NETWORK['indices']['host'], NETWORK['indices']['port'])
def _analyze(text, is_query):
response = utils.send_json({
'action': 'process',
'data': text,
'is_query': is_query
}, NETWORK['text']['host'], NETWORK['text']['port'], True)
return response['terms']
def update_search(token, document, positive, count):
return send_json(
{
'action': 'update_query',
'token': token,
'document': document,
'positive': positive,
'count': count
}, settings.NETWORK['models']['host'],
settings.NETWORK['models']['port'], True)
def search(token, query, continuation, count):
return send_json(
{
'action': 'query',
'token': token,
'query': query,
'continuation': continuation,
'count': count
}, settings.NETWORK['models']['host'],
settings.NETWORK['models']['port'], True)
def _load_index(self):
while True:
index = utils.send_json({
'action': 'load',
'path': self.path
}, NETWORK['indices']['host'], NETWORK['indices']['port'], True)
if index:
break
time.sleep(random.randint(10, 60)) # Give some time so indices module can make it
return index
def build(self, path):
self.path = path
self.doc_names = [
'.'.join(doc.name.split('.')[:-1])
for doc in Path(self.path).iterdir() if doc.name.endswith('.txt')
]
self.doc_names.sort()
index = utils.send_json({
'action': 'load',
'path': path
}, NETWORK['indices']['host'], NETWORK['indices']['port'], True)
if not index:
freq, terms = self._calculate_freq()
self._build_index(freq, terms)
else:
freq, terms = utils.load_freq(index, self.doc_names)
self.freq = freq
self.w = TfidfTransformer().fit_transform(freq)
self.terms = terms
self.term_count = self.w.shape[0]
self.doc_count = self.w.shape[1]
def set_model(model):
send_json({
'action': 'set_model',
'model': model
}, settings.NETWORK['models']['host'], settings.NETWORK['models']['port'])
def get_summary():
return send_json({'action': 'get'}, settings.NETWORK['summaries']['host'],
settings.NETWORK['summaries']['port'], True)
def get_suggestions(token):
return send_json({
'action': 'suggest',
'token': token
}, settings.NETWORK['recommendation']['host'],
settings.NETWORK['recommendation']['port'], True)
def get_model():
return send_json({'action': 'get_model'},
settings.NETWORK['models']['host'],
settings.NETWORK['models']['port'], True)