diff --git a/nlp/app/forms.py b/nlp/app/forms.py
index 5395597..cd45592 100644
--- a/nlp/app/forms.py
+++ b/nlp/app/forms.py
@@ -6,7 +6,7 @@ class MyForm(FlaskForm):
 	class Meta:  # Ignoring CSRF security feature.
 		csrf = False
 
-	input_field = StringField(label='input headline:', id='input_field',
+	input_field = StringField(label='input comment:', id='input_field',
 							  validators=[DataRequired()], 
 							  render_kw={'style': 'width:50%'})
 	submit = SubmitField('Submit')
\ No newline at end of file
diff --git a/nlp/app/routes.py b/nlp/app/routes.py
index ec4db3f..f62628c 100644
--- a/nlp/app/routes.py
+++ b/nlp/app/routes.py
@@ -6,10 +6,11 @@
 import pickle
 import sys
 
-clf, vec = pickle.load(open(clf_path, 'rb'))
-print('read clf %s' % str(clf))
-print('read vec %s' % str(vec))
-labels = ['liberal', 'conservative']
+bnb, vec_1, process_text = pickle.load(open(clf_path, 'rb'))
+print('read bnb %s' % str(bnb))
+print('read vec %s' % str(vec_1))
+print('read process_text %s' % str(process_text))
+labels = ['loss', 'win']
 
 @app.route('/', methods=['GET', 'POST'])
 @app.route('/index', methods=['GET', 'POST'])
@@ -18,9 +19,10 @@ def index():
 	result = None
 	if form.validate_on_submit():
 		input_field = form.input_field.data
+        updated_field = process_text([input_field])
 		X = vec.transform([input_field])
-		pred = clf.predict(X)[0]
-		proba = clf.predict_proba(X)[0].max()
+		pred = bnb.predict(X)[0]
+		proba = bnb.predict_proba(X)[0].max()
 		# flash(input_field)
 		return render_template('myform.html', title='', form=form, 
 								prediction=labels[pred], confidence='%.2f' % proba)
diff --git a/nlp/cli.py b/nlp/cli.py
index 501a27c..825dfe0 100644
--- a/nlp/cli.py
+++ b/nlp/cli.py
@@ -16,7 +16,7 @@
 from sklearn.linear_model import LogisticRegression
 from nltk.corpus import stopwords
 from nltk.stem import PorterStemmer
-from . import config, config_path
+from . import bnb_path, lr_path, config, config_path
 
 @click.group()
 def main(args=None):
@@ -113,6 +113,7 @@ def process_text(document):
     y_pred = bnb.predict(X_val)
     f1 = f1_score(y_val, y_pred)
     print("F1 Score:", round(f1, 3))
+    pickle.dump((bnb, vec_1, process_text), open(bnb_path, 'wb'))
 
 @main.command('train_lr')
 def train_lr():