class Bot:
def __init__(self):
self.keyword_fetcher = Keywords()
self.spot_client = Spot()
def fetch_spot(self, sentence):
result = self.keyword_fetcher.extract_from_sentence(sentence)
message = {}
message_body = ''
spot = self.spot_client.recommend_spot(list(result[1])[0], result[0])
if spot:
message_body += spot['name']
message_body += 'はどうでしょうか？'
message_body += 'オススメポイントは'
message_body += spot['reason']
message_body += ' です'
message['body'] = message_body
message['image'] = spot['image']
else:
message_body = '申し訳ありません、候補が見つかりませんでした'
message['body'] = message_body
return message
class Spot:
def __init__(self):
self.FOURSQUARE_API_URL = 'https://api.foursquare.com/v2/venues/search'
self._foursquare_client = foursquare.Foursquare(
client_id=SECRETS['FOURSQUARE_CLIENT_ID'],
client_secret=SECRETS['FOURSQUARE_CLIENT_SECRET'],
lang='ja'
)
categories = self._foursquare_client.venues.categories()
self._categories = self._flatten_categories(categories)
self._keyword = Keywords()
def _flatten_categories(self, nested_categories):
result = []
for categories in nested_categories['categories']:
result.append((categories['name'], categories['id']))
for sub_category in self._flatten_categories(categories):
result.append(sub_category)
return result
def _match_category_ids(self, keywords):
matched_category_ids = [(category[0], category[1]) for category in self._categories if category[0] in keywords]
return matched_category_ids
def recommend_spot(self, location, keywords):
target_categories = self._match_category_ids(keywords)
target_category_names = set(category[0] for category in target_categories)
target_category_ids = set(category[1] for category in target_categories)
params={
'near':location,
'categoryId':reduce(lambda i, s:i+','+s, target_category_ids),
'intent': 'browse',
'limit':50,
}
try :
response = self._foursquare_client.venues.search(params=params)
except:
return None
## Reccomend comment
candidates = []
for venue in response['venues']:
venue_id = venue['id']
candidate = {}
if venue['stats']['tipCount'] >= 2 and venue['stats']['checkinsCount'] >= 1500:
venue_detail = self._foursquare_client.venues(venue_id)['venue']
if len(venue_detail['tips']['groups']) > 1:
tips = venue_detail['tips']['groups'][1]['items']
else:
tips = venue_detail['tips']['groups'][0]['items']
word_counter = Counter()
for tip in tips:
if 'lang' in tip and tip['lang'] != 'ja':
continue
keywords, _ = self._keyword.extract_from_sentence(tip['text'])
for keyword in keywords:
like_count = tip['likes']['count']
word_counter[keyword] += 1 * (1 if like_count == 0 else like_count)
print(venue['id'], venue['name'], word_counter)
tags = []
for key, count in word_counter.most_common(10):
if count > 1:
tags.append(key)
if len(tags) > 0:
candidate['name'] = venue['name']
candidate['tags'] = tags
if 'photos' in venue_detail and venue_detail['photos']['groups'] and venue_detail['photos']['groups'][0]['items']:
photo = venue_detail['photos']['groups'][0]['items'][0]
candidate['image'] = photo['prefix'] + '128x128' +photo['suffix']
candidates.append(candidate)
if len(candidates) > 0:
break
if len(candidates) == 0:
return None
# Instantly return most popular candidate
print(candidates)
top_candidate = candidates[0]
ret = {
'name': top_candidate['name'],
'reason': reduce(lambda i, s: i+' '+s, top_candidate['tags']), # Add recommend reason
'image': top_candidate['image']
}
return ret
class Spot:
def __init__(self):
self.FOURSQUARE_API_URL = 'https://api.foursquare.com/v2/venues/search'
self._foursquare_client = foursquare.Foursquare(
client_id=os.environ['FOURSQUARE_CLIENT_ID'],
client_secret=os.environ['FOURSQUARE_CLIENT_SECRET'],
lang='ja')
categories = self._foursquare_client.venues.categories()
self._categories = self._flatten_categories(categories)
self._keyword = Keywords()
def _flatten_categories(self, nested_categories):
result = []
for categories in nested_categories['categories']:
result.append((categories['name'], categories['id']))
for sub_category in self._flatten_categories(categories):
result.append(sub_category)
return result
def _match_category_ids(self, keywords):
matched_category_ids = [(category[0], category[1])
for category in self._categories
if category[0] in keywords]
return matched_category_ids
def recommend_spot(self, location, keywords):
print(keywords)
target_categories = self._match_category_ids(keywords)
target_category_names = set(category[0]
for category in target_categories)
target_category_ids = set(category[1]
for category in target_categories)
params = {
'near': location,
'categoryId': reduce(lambda i, s: i + ',' + s,
target_category_ids),
'limit': 50,
}
try:
response = self._foursquare_client.venues.search(params=params)
except:
return None
## Reccomend comment
candidates = []
for venue in response['venues']:
venue_id = venue['id']
candidate = {}
if venue['stats']['tipCount'] >= 2 and venue['stats'][
'checkinsCount'] >= 1500:
venue_detail = self._foursquare_client.venues(
venue_id)['venue']
if len(venue_detail['tips']['groups']) > 1:
tips = venue_detail['tips']['groups'][1]['items']
else:
tips = venue_detail['tips']['groups'][0]['items']
word_counter = Counter()
for tip in tips:
if 'lang' in tip and tip['lang'] != 'ja':
continue
keywords, _ = self._keyword.extract_from_sentence(
tip['text'])
for keyword in keywords:
like_count = tip['likes']['count']
word_counter[keyword] += 1 * (1 if like_count == 0 else
like_count)
tags = []
for key, count in word_counter.most_common(10):
if count > 1:
tags.append(key)
if len(tags) > 0:
candidate['name'] = venue['name']
candidate['tags'] = tags
if 'photos' in venue_detail and venue_detail['photos'][
'groups'] and venue_detail['photos']['groups'][0][
'items']:
photo = venue_detail['photos']['groups'][0]['items'][0]
candidate['image'] = photo[
'prefix'] + '128x128' + photo['suffix']
candidates.append(candidate)
if len(candidates) > 0:
break
if len(candidates) == 0:
return None
# Instantly return most popular candidate
print(candidates)
top_candidate = candidates[0]
ret = {
'name': top_candidate['name'],
'reason': reduce(lambda i, s: i + ' ' + s,
top_candidate['tags']), # Add recommend reason
'image': top_candidate['image']
}
return ret
