def ajax_detector():
if request.method == 'POST':
url = request.json
result = get_prediction(url['url'])
title = result[0]
prediction = result[1]
result = {'title': title, 'tag': prediction}
return jsonify(result)
def predict():
if request.method == 'POST':
# we will get the file from the request
file = request.files['file']
# convert that to bytes
img_bytes = file.read()
class_id, class_name = predictor.get_prediction(image_bytes=img_bytes)
return jsonify({'class_id': class_id, 'class_name': class_name})
def get(self, request):
#data_dir = '/Users/lconroy/comp_msc/dublink_bus/final_models'
#data_dir = 'C:\\Users\\rbyrn\\Desktop\\dublinbus\\app\\model_integration'
data_dir = os.path.join(settings.BASE_DIR, 'backend_data_store', 'final_models')
lineid = request.query_params.get('lineid').upper()
routeid = request.query_params.get('routeid')
start_stop = request.query_params.get('start_stop')
end_stop = request.query_params.get('end_stop')
time_secs = request.query_params.get('time_secs')
dow = request.query_params.get('dow')
rain = request.query_params.get('rain')
temp = request.query_params.get('temp')
clouds = request.query_params.get('clouds')
feels_like = request.query_params.get('feels_like')
main = request.query_params.get('main')
try:
model_pickle = os.path.join(data_dir, 'pickle_file_XG_03082020', 'XG_{}.pkl'.format(lineid))
#model_pickle = os.path.join(data_dir, 'pickle_file\\XG_{}.pkl'.format(lineid))
model = joblib.load(open(model_pickle, 'rb'))
except Exception as e:
print(e)
return Response("ERROR: incorrect file structure", status=status.HTTP_500_INTERNAL_SERVER_ERROR)
#direction = 0
#with open(os.path.join(data_dir,'stop_sequence/stop_{0}.csv'.format(lineid)), 'r') as f:
# for line in f:
# if line.split(",")[1] == routeid:
# direction = line.split(",")[2]
# break
#if direction == 0:
# return Response("ERROR: cannot get direction", status=status.HTTP_500_INTERNAL_SERVER_ERROR)
m_args = {
'lineid': lineid,
'start_stop': start_stop,
'end_stop': end_stop,
'time_secs': time_secs,
'dow': dow,
'holiday': "0",
'temp': temp,
'feels_like': feels_like,
'clouds_all': clouds,
'weather_main': main
}
data = {}
data["journey_info"] = get_prediction(model, m_args, data_dir)
if "error" in data["journey_info"].keys():
if data["journey_info"]["error"] == "file structure error":
return Response("ERROR IN FILE STRUCTURE", status=status.HTTP_500_INTERNAL_SERVER_ERROR)
return Response("ERROR IN MODEL", status=status.HTTP_500_INTERNAL_SERVER_ERROR)
return Response(data, status=status.HTTP_200_OK)
def hello_world():
if request.method == "GET":
return render_template("index.html")
if request.method == "POST":
if "file" not in request.files:
return "file not uploaded !!!"
file = request.files["file"]
image = file.read()
flower = get_prediction(image_bytes=image)
return render_template("result.html", value=flower)
def get_image():
if request.method == 'POST':
f = request.files['file']
sfname = os.path.join('static/images/', str(secure_filename(f.filename)).lower())
f.save(sfname)
image = load_and_preprocess_image(sfname, path='')
predictions = get_prediction(image, model)
prediction_str = "This cat most likely is a {} with a {:.2f}" \
" percent confidence.".format(predictions[0][np.argmax(predictions[1])],
100 * np.max(predictions[1]))
return render_template('prediction.html', pred_data=zip(*predictions), prediction=prediction_str,
image='../' + sfname)
def stream(self):
while True:
ret, frame = self.cap.read()
if not ret: # EOF
break
frame = cv2.resize(frame, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_AREA)
# flip the image horizontally
frame = cv2.flip(frame, 1)
bounding = mpHelper.getBounding(frame)
# No crop with box
cv2.imshow(str(id(self)), bounding[0])
# Crop
crops = bounding[1]
if (crops and len(crops) > 0):
# cv2.imshow(str(id(self)), crops[0])
print(get_prediction(crops[0]))
c = cv2.waitKey(1)
if c == 27: #ESC key
break
def make_prediction():
prices = request.get_json()
update_data(prices)
predictions = get_prediction(prices, DATA)
return jsonify(render_template('predictions.html', predictions=predictions))
def get_pred():
return get_prediction(request.args.to_dict())
def labeled_demo(bot, update):
prediction = get_prediction(labeled=True)
bot.sendPhoto(chat_id=update.message.chat_id,
photo=prediction['picture'],
caption=prediction['message'])