class MyApp:
def __init__(self, name=''):
self._name = name
print('the app is initialized')
# self.say_name()
self.index = Index()
self.df_amazon = pd.read_csv('Datafiniti_Amazon_Consumer_Reviews_of_Amazon_Products.csv', \
error_bad_lines=False,encoding='utf-8-sig')
num_files = self.index.index_product(self.df_amazon)
print("indexed %d files" % num_files)
self.index.index_review(self.df_amazon)
# init predict
filename1 = "xgboost.pkl"
filename2 = "randomForest.pkl"
filename3 = "word_vectorize.pkl"
filename4 = "char_vectorize.pkl"
with open(filename1, "rb") as f1:
self.xgboostModel = pickle.load(f1)
with open(filename3, "rb") as f2:
self.word_vectorizer = pickle.load(f2)
with open(filename4, "rb") as f3:
self.char_vectorizer = pickle.load(f3)
def say_name(self):
print('my name is {0}'.format(self._name))
def get_name(self):
return self._name
def search_product(self, query):
# dataList = []
# for i in range(10):
# dataList.append({'id': i+1, 'name': '{0}-{1}'.format(arg, i+1)})
return self.index.search_product(query)
def get_product_info(self, id):
info = dict()
# reviews = [{'id': i, 'text': '{0} is good. [{1}]'.format(id, i)} for i in range(5)]
labels = self.index.top_frequency_words(id)
# info['topReviews'] = reviews
info['labels'] = labels
review_txt = default_reviews(id, self.df_amazon)
info['topReviews'] = [{'text': txt} for txt in review_txt]
# print(info['topReviews'])
return info
def search_review(self, id, term):
# dummy = '{0} of {1} is good'.format(term, id)
# reviews = []
# for i in range(10):
# reviews.append({'id': i, 'text': '{0}-[{1}]'.format(dummy, i)})
review_text = self.index.search_review(id, term)
print('# of reviews={0}'.format(len(review_text)))
reviews = [{'text': txt} for txt in review_text]
return reviews
def predict_score(self, id, text):
test1 = text
test1 = [test1]
test1_word = self.word_vectorizer.transform(test1)
test1_char = self.char_vectorizer.transform(test1)
test1_features = hstack([test1_char, test1_word])
test1_features = test1_features.tocsr()
result = self.xgboostModel.predict(test1_features)
return {'score': float(result)}
