def run(self, args):
result = float(0)
for ka, kv in enumerate(args):
if (is_number(kv) == False):
kv = 0
result = result + float(kv)
return stringify(result)
def run(self, args):
if len(args) > 1:
return "#-1 TOO MANY ARGS"
arg = args[0]
if isinstance(arg, basestring):
arg = float(arg)
return stringify(fabs(arg))
def uploadfile():
if request.method == 'POST':
files = request.files['file']
print(files.filename, flush=True)
# check validity of file
if files and allowed_file(files.filename):
filename = secure_filename(files.filename)
print(filename, flush=True)
updir = os.path.join(basedir, 'upload/')
image_path = os.path.join(updir, filename)
files.save(image_path)
file_size = os.path.getsize(os.path.join(updir, filename))
else:
app.logger.info('ext name error')
return jsonify(error='ext name error')
image = decode_image(image_path) # read in the image
predictions = inference_instance.get_class_probabilities(image)
return stringify(predictions)
def radio_selection():
if request.method == 'POST':
selection = json.loads(request.get_data(
as_text=True))['radio_sel'] # converts json byte string to dict
print(selection)
image_path = None
if selection == '1': #(cat)
image_path = os.path.join(os.getcwd(), 'static', 'images',
'n02121808_1421_domestic_cat.jpg')
elif selection == '2': # (whale)
image_path = os.path.join(os.getcwd(), 'static', 'images',
'grey_whale.jpeg')
elif selection == '3': # (dog)
image_path = os.path.join(os.getcwd(), 'static', 'images',
'n02084071_1365_dog.jpg')
image = decode_image(image_path) # read the image
predictions = inference_instance.get_class_probabilities(image)
return stringify(predictions)
def test_inference():
img_path = "grey_whale.jpeg"
img = decode_image(img_path)
prediction = inference_instance.get_class_probabilities(img, top_k=5)
output = stringify(prediction)
print(output)
