Update app.py

app.py

@@ -1,3 +1,4 @@
+import streamlit as st
 import pandas as pd
 import numpy as np
 import csv
@@ -13,7 +14,6 @@ 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))
@@ -50,7 +50,7 @@ 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))