def main():
# MetaMind makes it simple to create custom classifiers for both text and images
# Create and use a custom image classifier
# This classifier classifies an image as 'food' or 'animal'
image_classifier = create_image_classifier()
print 'Custom image classifier predictions:'
pprint.pprint(
image_classifier.predict([blueberry_pie_url, deer_url],
input_type='urls'))
# Create and use a custom text classifier
# This classifier classifies text as 'rural' or 'urban'
text_classifier = create_text_classifier()
print 'Custom text classifier predictions:'
pprint.pprint(
text_classifier.predict(
['We sheared the sheep yesterday.', 'The traffic is loud.'],
input_type='text'))
# Use builtin general image classifier
print 'MetaMind builtin general image classifier predictions:'
pprint.pprint(
general_image_classifier.predict([apple_pie_url, zebra_url],
input_type='urls'))
# Use builtin food image classifier
print 'MetaMind builtin food image classifier predictions:'
pprint.pprint(
food_image_classifier.predict([apple_pie_url, salad_url],
input_type='urls'))
# Use builtin twitter sentiment classifier
# This classifier finds tweets by a given key word, and classifies each tweet as
# 'positive', 'negative' or 'neutral'
print 'MetaMind builtin twitter sentiment classifier:'
pprint.pprint(twitter_text_classifier.query_and_predict('trump')[:3])
# You can create a representation of a given classifier by passing its id into the constructor.
# You can explore additional public classifiers here: https://www.metamind.io/vision/explore
# You can explore your private classifiers and data here: https://www.metamind.io/my_stuff
# You can find more details about the classifier used below here: https://www.metamind.io/classifiers/155
print 'Public sentiment classifier with id=155:'
pprint.pprint(
ClassificationModel(id=155).predict(
"This is such a great, wonderful sentiment", input_type="text"))
def main():
# MetaMind makes it simple to create custom classifiers for both text and images
# Create and use a custom image classifier
# This classifier classifies an image as 'food' or 'animal'
image_classifier = create_image_classifier()
print 'Custom image classifier predictions:'
pprint.pprint(image_classifier.predict([
blueberry_pie_url,
deer_url
], input_type='urls'))
# Create and use a custom text classifier
# This classifier classifies text as 'rural' or 'urban'
text_classifier = create_text_classifier()
print 'Custom text classifier predictions:'
pprint.pprint(text_classifier.predict([
'We sheared the sheep yesterday.',
'The traffic is loud.'
], input_type='text'))
# Use builtin general image classifier
print 'MetaMind builtin general image classifier predictions:'
pprint.pprint(general_image_classifier.predict([apple_pie_url, zebra_url], input_type='urls'))
# Use builtin food image classifier
print 'MetaMind builtin food image classifier predictions:'
pprint.pprint(food_image_classifier.predict([apple_pie_url, salad_url], input_type='urls'))
# Use builtin twitter sentiment classifier
# This classifier finds tweets by a given key word, and classifies each tweet as
# 'positive', 'negative' or 'neutral'
print 'MetaMind builtin twitter sentiment classifier:'
pprint.pprint(twitter_text_classifier.query_and_predict('trump')[:3])
# You can create a representation of a given classifier by passing its id into the constructor.
# You can explore additional public classifiers here: https://www.metamind.io/vision/explore
# You can explore your private classifiers and data here: https://www.metamind.io/my_stuff
# You can find more details about the classifier used below here: https://www.metamind.io/classifiers/155
print 'Public sentiment classifier with id=155:'
pprint.pprint(ClassificationModel(id=155).predict("This is such a great, wonderful sentiment", input_type="text"))
import sys
import os
from metamind.api import ClassificationData, ClassificationModel, set_api_key, general_image_classifier
warnings.filterwarnings("ignore")
url = sys.argv[1]
set_api_key("Xnxh4kNZgcykl9ePz4nfoRV7EVYW5BM9WDQGoMopzVObFJdvzR")
# training_data = ClassificationData(private=True, data_type='image', name='training images')
# training_data.add_samples([
# ('http://newsimg.bbc.co.uk/media/images/46310000/jpg/_46310103_r850082-andromeda_galaxy_(m31)-spl.jpg', 'galaxy'),
# ('https://encrypted-tbn1.gstatic.com/images?q=tbn:ANd9GcSNB1gxHaGwKNV8r-AvYgST0PiM4t9YXU7e8XRELdTHGx50dqtUMg', 'galaxy'),
# ('https://encrypted-tbn3.gstatic.com/images?q=tbn:ANd9GcT7KaboL9LLsjMTM346fphYW3fufnVN8zMxcG7FhvDahepCET0sXA', 'galaxy'),
# ('https://maleficusamore.files.wordpress.com/2012/06/sirius20crop1.jpg', 'star'),
# ('http://aetherforce.com/wp-content/uploads/2014/12/sun_stars_space_light_58237_1920x1180.jpg', 'star'),
# ('http://cdn.spacetelescope.org/archives/images/publicationjpg/heic1312a.jpg', 'planet'),
# ('http://orig00.deviantart.net/6290/f/2006/336/b/0/planet_stock_5_by_bareck.jpg', 'planet'),
# ('https://upload.wikimedia.org/wikipedia/commons/8/85/Venus_globe.jpg', 'planet'),
# ('https://stenila.files.wordpress.com/2014/08/purchased-elanon.jpg?w=569&h=367', 'star')],
# input_type='urls')
#
# classifier = ClassificationModel(private=True, name='my classifier')
# classifier.fit(training_data)
print general_image_classifier.predict(url, input_type='urls')
list = [general_image_classifier.predict(url, input_type='urls')]
firstList = list[0]
dict = firstList[0]
print dict['label']
from metamind.api import set_api_key, general_image_classifier
set_api_key('aXZxkB3eOMupDZSMNIZSdfD9hxv2zBDpen8qbMOOPLtzYwhx2X')
print general_image_classifier.predict(['https://scontent.xx.fbcdn.net/hphotos-xft1/t31.0-8/p180x540/1799935_10153379041592500_4858712342770219261_o.jpg', 'https://scontent.xx.fbcdn.net/hphotos-xfp1/v/t1.0-9/q83/p720x720/945268_10151610992317500_1182525900_n.jpg?oh=377f8509b6dd5353d6f47205405a7df8&oe=566DD49C'], input_type='urls')
import metamind
from metamind.api import set_api_key, general_image_classifier
import json
apiKey = 'd2TshfVAyBuTqRnCto5aDay1XsZOTd0CEhIOZNKEPuCMRlNde0'
if __name__ == "__main__":
set_api_key(apiKey)
res = json.loads(open('out.json', 'r').read())
urlRes = []
for date in res:
val = res[date]['data']
for item in val:
if len(item['tags']) > 0:
#print 'non-empty tags found: ' + str(item['tags'])
#print 'image: ' + item['images']['standard_resolution']['url']
urlRes.append(item['images']['standard_resolution']['url'])
imgTag = general_image_classifier.predict(urlRes, input_type='urls')
f = open('img.json', 'w')
f.write(json.dumps(imgTag))
f.close()
