def predict(offer): from engines import content_engine num_predictions = 5 offer_list=content_engine.predict(offer, num_predictions) print("Similar offers recommended for you:") for s in offer_list: print(*s)
def predict(): from engines import content_engine item = request.data.get('item') num_predictions = request.data.get('num', 10) if not item: return [] return content_engine.predict(str(item), num_predictions)
def predict(): from engines import content_engine item = request.get_data('item') num_predictions = 10 if not item: return [] return content_engine.predict(str(item), num_predictions, "id_desc_price.xlsx")
def predict(offer): from engines import content_engine num_predictions = 5 print("Similar offers recommended for you:") similar_offer_list = content_engine.predict(offer, num_predictions) # prints similar offers along with their similarity score for s in similar_offer_list: print(int(s[0]), s[1])
def predict(): from engines import content_engine item = request.data.get('item') realID = item # if item == -1, means prediction for the last row. if item == '-1': with open('backup.csv') as source: reader = csv.DictReader(source.read().splitlines()) realID = str(len(list(reader)) - 1) num_predictions = request.data.get('num', 10) data_url = request.data.get('data-url', None) if not realID: return [] # For now, only returns a nested list of the top num of post and their scores, need more detailed loggin info! return content_engine.predict(str(realID), int(num_predictions), data_url)