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)