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"))
def classify(in_img, ingredients):
train_data()
specific_descript = food_image_classifier.predict(in_img, input_type='files')
print(specific_descript)
general_descript = fruit.predict(in_img, input_type='files')
entry = date(2015, 10, 11)
if(general_descript[0]['label'] == 'meat'):
expiary = date(entry.year, entry.month, entry.day + 5)
elif(general_descript[0]['label'] == 'vegetables'):
expiary = date(entry.year, entry.month, entry.day + 14)
elif(general_descript[0]['label'] == 'fruits'):
expiary = date(entry.year, entry.month, entry.day + 7)
elif(general_descript[0]['label'] == 'dairy'):
expiary = date(entry.year, entry.month, entry.day + 14)
else:
expiary = date(entry.year, entry.month, entry.day + 7)
ingredients.append({'name': specific_descript[0]['label'], 'class': general_descript[0]['label'], 'entry_date': entry, 'exp_date': expiary})
print(ingredients)
f = open('./inventory.txt', 'r+b')
for item in ingredients:
json.dump(item,f)
f.close()
def labelMetamind(request):
if 'q' in request.GET and request.GET['q']:
url = request.GET['q']
set_api_key('q7dR3chgj4SLJHnnbpIzvDVXLrvyQ9ncJ6ZX2Gqt9ZyyTPr7oH')
print food_image_classifier.predict([url], input_type='urls')
else:
return HttpResponse('Please submit a image url')
calories = 0
carbohydrates = 0
protein = 0
fiber = 0
sugar = 0
calcium = 0
iron = 0
magnesium = 0
phosphorus = 0
pottasium = 0
sodium = 0
zinc = 0
vitaminc = 0
thiamin = 0
riboflavin = 0
niacin = 0
vitaminb6 = 0
folate = 0
vitamina = 0
vitamind = 0
saturatedfat = 0
monosaturatedfat = 0
polyunsaturatedfat = 0
transfat = 0
totalfat = 0
fatcalories = 0
cholestrol = 0
serving = ""
context = {
"serving": serving,
"calories": calories,
"carbohydrates": carbohydrates,
"protein": protein,
"fiber": fiber,
"sugar": sugar,
"calcium": calcium,
"iron": iron,
"magnesium": magnesium,
"phosphorus": phosphorus,
"pottasium": pottasium,
"sodium": sodium,
"zinc": zinc,
"vitaminc": vitaminc,
"thiamin": thiamin,
"riboflavin": riboflavin,
"niacin": niacin,
"vitaminb6": vitaminb6,
"folate": folate,
"vitamina": vitamina,
"vitamind": vitamind,
"saturatedfat": saturatedfat,
"monosaturatedfat": monosaturatedfat,
"polyunsaturatedfat": polyunsaturatedfat,
"transfat": transfat,
"totalfat": totalfat,
"fatcalories": fatcalories,
"cholestrol": cholestrol,
}
return render(request, 'label.html', context)
def labelMetamind(request):
if 'q' in request.GET and request.GET['q']:
url = request.GET['q']
set_api_key('q7dR3chgj4SLJHnnbpIzvDVXLrvyQ9ncJ6ZX2Gqt9ZyyTPr7oH')
print food_image_classifier.predict([url], input_type='urls')
else:
return HttpResponse('Please submit a image url')
calories = 0
carbohydrates = 0
protein = 0
fiber = 0
sugar = 0
calcium = 0
iron = 0
magnesium = 0
phosphorus = 0
pottasium = 0
sodium = 0
zinc = 0
vitaminc = 0
thiamin = 0
riboflavin = 0
niacin = 0
vitaminb6 = 0
folate = 0
vitamina = 0
vitamind = 0
saturatedfat = 0
monosaturatedfat = 0
polyunsaturatedfat = 0
transfat = 0
totalfat = 0
fatcalories = 0
cholestrol = 0
serving = ""
context = {
"serving": serving,
"calories": calories,
"carbohydrates": carbohydrates,
"protein": protein,
"fiber": fiber,
"sugar": sugar,
"calcium": calcium,
"iron": iron,
"magnesium": magnesium,
"phosphorus": phosphorus,
"pottasium": pottasium,
"sodium": sodium,
"zinc": zinc,
"vitaminc": vitaminc,
"thiamin": thiamin,
"riboflavin": riboflavin,
"niacin": niacin,
"vitaminb6": vitaminb6,
"folate": folate,
"vitamina": vitamina,
"vitamind": vitamind,
"saturatedfat": saturatedfat,
"monosaturatedfat": monosaturatedfat,
"polyunsaturatedfat": polyunsaturatedfat,
"transfat": transfat,
"totalfat": totalfat,
"fatcalories": fatcalories,
"cholestrol": cholestrol,
}
return render(request, 'label.html', context)
