Update app.py

app.py

@@ -11,6 +11,9 @@ from sklearn import linear_model
 
 df = pd.read_csv('emily_election.csv')
 
+loaded_model = pickle.load(open(filename, 'rb'))
+
+"""
 
 df['runtime'] = df['cumulative_ad_runtime'].apply(lambda s: int(s.split('days')[0]))
 df['impressions'] = df['cumulative_impressions_by_region'].apply(lambda d: ast.literal_eval(d))
@@ -46,10 +49,12 @@ x = np.asanyarray(new_train[['log_runtime', 'log_spend']])
 y = np.asanyarray(new_train[['log_impressions']])
 regr.fit (x, y)
 y_pred= regr.predict(new_train[['log_runtime', 'log_spend']])
+
+"""
 # # The coefficients
-print(regr.coef_)
-print('R-squared score: %.2f' % regr.score(x, y))
-print('Standard Deviation: %.2f' % np.sqrt(sum((y - y_pred)**2) / (len(y) - 2)))
+#print(regr.coef_)
+#print('R-squared score: %.2f' % regr.score(x, y))
+#print('Standard Deviation: %.2f' % np.sqrt(sum((y - y_pred)**2) / (len(y) - 2)))