def recommendation():
'''
Return the use case 2 (house recommendation) page
It will get the user input from webpage and then process and pass
the input to datahandle package
'''
if request.method == 'POST':
zipcode = request.form.get('zipcode')
nights = request.form.get('nights')
price = request.form.get('price')
accommodates = request.form.get('accommodates')
room_type = request.form.get('room_type')
score = request.form.get('score')
is_verified_host = request.form.get('verified_host')
is_need_license = request.form.get('need_license')
results_limit = request.form.get('results_limit')
nights = None if nights == "" else int(nights)
price = None if price == "" else "$" + price
accommodates = None if accommodates == "" else int(accommodates)
room_type = None if room_type == "" else room_type
score = None if score == "" else int(score)
is_verified_host = True if is_verified_host == "True" else None
is_need_license = True if is_need_license == "True" else None
results_limit = None if results_limit == "" else int(results_limit)
df_table = dh.primary_recommend_search(zipcode, accommodates, price, score, None,\
is_verified_host, room_type, None, None, None, None, nights, None, None, \
is_need_license, results_limit)
if df_table.empty:
return render_template('not_found.html')
points_list = get_marker_points(df_table)
return render_template('recommendation.html', points_list = points_list,\
house_list = df_table)
return render_template('index.html')
def test_primary_search_super_host(self):
df = dh.primary_recommend_search("98105", None, None, \
None, True, None, None, None, None, None, None, None, None, \
None, None, 10)
for index, row in df.iterrows():
self.assertTrue(row['host_is_superhost'])
def test_primary_search_rating(self):
df = dh.primary_recommend_search("98105", None, None, \
80, None, None, None, None, None, None, None, None, None, \
None, None, 10)
for index, row in df.iterrows():
self.assertGreaterEqual(row['review_scores_rating'], 80)
def test_primary_search_price(self):
df = dh.primary_recommend_search("98105", None, "$100", \
None, None, None, None, None, None, None, None, None, None, \
None, None, 10)
for index, row in df.iterrows():
self.assertLessEqual(row['price'], "$100")
def test_primary_search_accommodates(self):
df = dh.primary_recommend_search("98105", 2, None, \
None, None, None, None, None, None, None, None, None, None, \
None, None, 10)
for index, row in df.iterrows():
self.assertGreaterEqual(row['accommodates'], 2)
def test_primary_search_zipcode(self):
df = dh.primary_recommend_search("98105", None, None, \
None, None, None, None, None, None, None, None, None, None, \
None, None, 10)
for index, row in df.iterrows():
self.assertEqual(row['zipcode'], "98105")
def test_primary_search_limit(self):
self.assertEqual(len(dh.primary_recommend_search("98105", None, None, \
None, None, None, None, None, None, None, None, None, None, \
None, None, 10)), 10)