def _format_word(self, word):
"""
Lemmatize (https://en.wikipedia.org/wiki/Lemma_(morphology)),
capitalize, and remove punctuation (other than apostrophes).
"""
punctuation_stripped = re.sub(r"[^\w\d'\s]+", '', word)
lemmatized = Word(punctuation_stripped).lemmatize()
capitalized = lemmatized.capitalize()
return capitalized
def predict():
if request.method == 'POST':
message = request.form['message']
if (len(message) > 2):
text = message
pre_processed_reviews = []
data = gensim.utils.simple_preprocess(text, min_len=2)
review = ' '.join(WordNetLemmatizer().lemmatize(word)
for word in data if word not in stop_words)
pre_processed_reviews.append(review.strip())
tfidf_model = joblib.load(MODEL_tfidf)
vect = tfidf_model.transform(pre_processed_reviews)
lr_model = joblib.load(MODEL_lr)
my_prediction = lr_model.predict(vect)
else:
my_prediction = 3
return render_template('home.html', prediction=my_prediction)
blob = TextBlob(text)
nouns = list()
for word, tag in blob.tags:
if tag == 'NN':
nouns.append(word.lemmatize())
display = []
output = ""
for item in random.sample(nouns, len(nouns)):
word = Word(item)
if word not in display:
display.append(word.capitalize())
for i in display:
if len(i) > 2:
output = output + " " + i
else:
output = ""
return render_template('home.html',
prediction=my_prediction,
summary=output)
