def fune():
pic = request.form['picture']
model = c_app.models.get('food-items-v1.0')
image = CImage(pic)
result = model.predict([image])
print(result['outputs'][0]['data']['concepts'])
healthy = 1
# Transfer money if food is healthy
if healthy:
url = 'http://api.reimaginebanking.com/accounts/{}/transfers?key={}'.format(
parent, apiKey)
payload = {
"medium": "balance",
"payee_id": child,
"amount": 0.01,
"transaction_date": "2017-04-09",
"description": "Money transferred for healthy food"
}
response = requests.post(
url,
data=json.dumps(payload),
headers={'content-type': 'application/json'},
)
if response.status_code == 201:
print('money transferred')
return "yay"
def process_image():
# i = '11111111111111111111111111' + '11111100111111111100111111' + '11110001111100111110001111' + '11000001111000011110000011' +'10000000111000011100000001' +'10000000000000000000000001' +'00000000000000000000000000' +'00000000000000000000000000' +'10000000000000000000000001' +'10000110001000010001100001' +'11001111111100111111110011' +'11100111111100111111100111' + '11111111111111111111111111'
# im = Image.open(io.BytesIO(i))
# im.show()
image = request.get_data()
with open('image.png', 'wb') as f:
f.write(base64.b64decode(image))
# image = CImage(url='http://media.npr.org/assets/img/2012/02/04/dietcoke_custom-0473c1516f8a6fd514e84622d0e092ae0172a66c-s300-c85.jpg')
image = CImage(file_obj=open('./image.png', 'rb'))
res = model.predict([image])
# print(res)
prediction = res['outputs'][0]['data']['concepts'][0]['name']
item_code = ncr.getItem(prediction)['itemCode']
price = ncr.getItemPrice(item_code)
# print(prediction)
# print(price)
return json.dumps({"price": price})
def image_request():
name = request.headers.get('name')
image = request.get_json(force=True).split(';')[-1].split(',')[-1]
name_split = name.split('.')
file_name = name_split[0]
extension = name_split[1]
url = 'https://www.googleapis.com/upload/storage/v1/b/recipeanut-images/o?uploadType=media&name={}'.format(file_name)
body = image.decode('base64')
headers={
"Content-Type": "image/{}".format(extension)
}
http = httplib2.Http()
resp_headers, resp_body = http.request(url, method='POST', headers=headers, body=body)
serving_url = 'https://storage.googleapis.com/recipeanut-images/{}'.format(file_name)
model = clar_app.models.get('food-items-v1.0')
image = CImage(url=serving_url)
resp = model.predict([image])
if resp['status']['description'] == 'Ok':
items = {}
index = 0
for item in resp['outputs'][0]['data']['concepts']:
food = extract_image_data(item)
if food != '':
items[index] = food
index += 1
return json.dumps(items)
return 'Image could not be read.'
def pic_colors(**kwargs):
logger.critical("Extracting key colors")
model = app.models.get('color')
image = CImage(**kwargs)
output = model.predict([image])
colors = output['outputs'][0]['data']['colors']
for c in colors:
c['hsv'] = hsv_color(c['raw_hex'])
return sorted(colors, key=lambda c: -c['value'])
def getData(model, venues):
venueColorDict = {}
for x in range(len(venues)):
image = CImage(url=venues[x][1])
imageData = model.predict([image])
colors = imageData['outputs'][0]['data'][
'colors'] #current color palette
colorPalette = []
for y in range(len(colors)):
colorPalette.append(colors[y]['raw_hex'])
venueColorDict[venues[x][0]] = colorPalette
return venueColorDict
def post(imagePath):
f = open("./Keys/key_clarifai.txt","r")
key = f.readline()
f.close()
# - `CLARIFAI_API_KEY`
app = ClarifaiApp(api_key=key)
model = app.models.get('food-items-v1.0')
image = CImage(file_obj=open(imagePath, 'rb'))
#output this to JSON
with open ('./output_data/results.json', 'w') as f:
json.dump(model.predict([image]),f)
def uploaded_file(filename):
model = app1.models.get('graffiti')
image = CImage(
file_obj=open('/home/karan/flaskapp/uploads/' + str(filename), 'rb'))
z = model.predict([image])
values = []
print(z, file=sys.stderr)
results = z['outputs'][0]['data']['concepts']
for tag in results:
print(tag['name'], file=sys.stderr)
print(tag['value'], file=sys.stderr)
values.append(tag['name'])
values.append(tag['name'])
#print (len(results), file=sys.stderr)
#print i['name']
print(str(z['outputs'][0]['data']['concepts'][0]), file=sys.stderr)
return app.send_static_file('finalview.html')
def post_view(request):
user = check_validation(request)
if user:
if request.method == 'POST':
form = PostForm(request.POST, request.FILES)
#checking if form is valid or not
if form.is_valid():
image = form.cleaned_data.get('image')
caption = form.cleaned_data.get('caption')
post = PostModel(user=user, image=image, caption=caption)
#saving posts
post.save()
apikey = 'lBzto9IhYQnI8Z6kd4dFap0gGbFexBgRBknxuISGFK4'
request_url = (
'https://apis.paralleldots.com/sentiment?sentence1=%s&apikey=%s'
) % (caption, apikey)
print 'POST request url : %s' % (request_url)
sentiment = requests.get(request_url, verify=False).json()
sentiment_value = sentiment['sentiment']
path = str(BASE_DIR + '\\' + post.image.url)
#imgur api key
client = ImgurClient(
"315c0833408f9c0",
"ab94bfdc68d430ac6f7aa5f16260b1f5d6e27b5e")
post.image_url = client.upload_from_path(path,
anon=True)['link']
print post.image_url
post.save()
#keywords that will be helpful in accessing dirty area post
keywords = [
'garbage', 'waste', 'trash', 'dirt', 'pollution', 'dust'
]
value_list = []
app = ClarifaiApp(api_key='ecc5aea7265040b4b320b3446f96152c')
model = app.models.get('general-v1.3')
image = CImage(url=post.image_url)
prediction = model.predict([image])
for i in range(
0, len(prediction['outputs'][0]['data']['concepts'])):
if prediction['outputs'][0]['data']['concepts'][i][
'name'] in keywords:
value = prediction['outputs'][0]['data']['concepts'][
i]['value']
value_list.append(value)
#
# checking condition
if (sentiment_value < 0.6 and max(value_list) > 0.8):
print 'dirty image'
send_mail(post.image_url)
return redirect('/feed/')
else:
form = PostForm()
#return post html page
return render(request, 'post.html', {'form': form})
else:
return redirect('/login/')
from clarifai import rest
from clarifai.rest import ClarifaiApp
app1 = ClarifaiApp("fXY39wieIcnVwRxNlu4d2HbKGupLzHXrvF1rYPMZ",
"y6-0qljij7SOOVC1FuRylVhKh7lgeLDu-TIbfkCW")
from clarifai.rest import Image as CImage
import csv
filename = './final_graffiti.csv'
print("# for row in csv_reader:")
count = 0
with open(filename, 'r') as count_file:
csv_reader = csv.reader(count_file)
for row in csv_reader:
count += 1
if count == 100:
break
print count
model = app1.models.get('nsfw-v1.0')
image = CImage(url=str(row[0]))
print(model.predict([image]))
def get_ingredients(image):
model = app.models.get('food-items-v1.0')
image = CImage(url=image)
return model.predict([image])