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Last active December 15, 2015 16:09
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Quick demo of some new NLP features in the Embedly API
import sys
import json
import urllib
import requests
def reddit(sub):
"""
Retrieve the json representation of the given subreddit
"""
url = 'http://reddit.com/r/%s.json' % sub
resp = requests.get(url)
return json.loads(resp.text)
def reddit_urls(js):
"""
Return all the URLs in the given json blob of reddit data
"""
return [x['data']['url'] for x in js['data']['children']]
def embedly_extract(url, key):
"""
Call the embedly API and return the json blob. Returns None if there
is an error processing the URL (e.g., HTTP 404)
"""
quoted = urllib.quote(url)
api = 'http://api.embed.ly/1/extract?url=%s&key=%s' % (quoted, key)
resp = requests.get(api)
if resp.status_code != 200:
sys.stderr.write('Failed to process URL %s :(\n' % url)
return None
return json.loads(resp.text)
def sum_counts(item_lists):
"""
Takes a list of lists of items and mashes them all together into a dict
mapping item_name -> item_score. The input items are either entity or
keyword lists, which look like {'name': 'Dennis', 'count': 10} or
{'name': 'sports', 'score': 33}, respectively.
"""
ret = {}
for item_list in item_lists:
for item in item_list:
# get either score or count so we can work with both
# keywords and entities
name, count = item['name'], item.get('count', item.get('score', 0))
ret[name] = ret.get(name, 0) + count
return ret
def top_n(item_dict, n):
by_count = sorted(item_dict.iteritems(), key=lambda x: x[1],
reverse=True)
return by_count[:n]
def main(subreddit, key):
urls = reddit_urls(reddit(subreddit))
embeds = [embedly_extract(url, key) for url in urls]
# change 'entities' to 'keywords' to check out the keyword extraction!
items = [x['entities'] for x in embeds if x]
all_counts = sum_counts(items)
top = top_n(all_counts, 10)
print '\n'.join('%s %s' % (x[0], x[1]) for x in top)
if __name__ == '__main__':
if len(sys.argv) < 3:
sys.stderr.write('Usage: demo.py <subreddit> <embedly-key>\n')
else:
main(sys.argv[1], sys.argv[2])
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