def test_similar(self):
ctx = app.test_request_context()
ctx.push()
from engines import content_engine
#content_engine.train('sample-data.csv')
content_engine.train('/home/ubuntu/teamHTTP/serverPHP/engine/tfidf-item/iroya_data.csv')
def test_similar(self):
ctx = app.test_request_context()
ctx.push()
from engines import content_engine
#content_engine.train('sample-data.csv')
content_engine.train('result.csv')
'''
def train():
from engines import content_engine
print "data:"
print request.args
print "end."
data_url = request.data.get('data-url', None)
print "a"
content_engine.train(data_url)
return {"message": "Success!", "success": 1}
def test_similar(self):
ctx = app.test_request_context()
ctx.push()
from engines import content_engine
content_engine.train('sample-data.csv')
data = {'item': 1, 'num': 10}
headers = [('Content-Type', 'application/json'), ('X-API-TOKEN', current_app.config['API_TOKEN'])]
json_data = json.dumps(data)
json_data_length = len(json_data)
headers.append(('Content-Length', str(json_data_length)))
response = app.test_client().post('/predict', headers=headers, data=json_data)
response = json.loads(response.data)
self.assertEqual(len(response), 10)
self.assertEqual(response[0][0], "19") # Boxers are like boxers.
"""
Created on Mon Jan 16 01:30:40 2017
@author: mayank singh
sample-data is collection of offer ids and their descriptions(Harvested from OfferMaster.csv)
on which TF-IDF is run and their cosine similarities calculated and stored in redis.
sample-data length has been reduced considerabaly in order to compensate for low RAM
"""
from engines import content_engine
content_engine.train('sample-data.csv')
def train():
from engines import content_engine
data_url = request.data.get("data-url", None)
content_engine.train(data_url)
return {"message": "Success!", "success": 1}
def train():
from engines import content_engine
data_url = request.data.get('data-url', None)
content_engine.train(data_url)
return {"message": "Success!", "success": 1}
def train():
from engines import content_engine
#data_url = request.get_data('data-url', None)
content_engine.train("id_desc_price.xlsx")
return "\n\nSuccess: Model trained\n\n"
def train():
from engines import content_engine
filename = request.data.get('filename', None)
content_engine.train(filename)
return {"message": "Success", "success": 1}