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)