def __set_options(self, cfg_path):
try:
cfg = file_folder_operations.read_json_file(cfg_path)
# model_options
self.predicted_image_action = cfg["predictor_options"]["model_options"]["predicted_image_action"]
self.confidence_threshold = cfg["predictor_options"]["model_options"]["confidence_threshold"]
self.keras_image_size = (cfg["predictor_options"]["model_options"]["image_w"], cfg["predictor_options"]["model_options"]["image_h"])
self.keras_grayscale = cfg["predictor_options"]["model_options"]["grayscale"]
self.keras_topN = cfg["predictor_options"]["model_options"]["return_topN"]
# graph for tf 1 session
self.tensorflow_version = cfg["predictor_options"]["model_options"]["tensorflow_version"]
if(self.tensorflow_version == 1):
self.tf_graph = tf.get_default_graph()
# model_paths
self.predictions_main_folder = cfg["predictor_options"]["model_paths"]["predictions_main_folder"]
self.not_confiedent_name = cfg["predictor_options"]["model_paths"]["not_confiedent_folder_name"]
self.keras_model_path = cfg["predictor_options"]["model_paths"]["model_path"]
self.keras_names_path = cfg["predictor_options"]["model_paths"]["names_path"]
return True
except:
self.logger.error("cfg file error", exc_info=True)
return False
def __set_options(self, cfg_path):
try:
cfg = file_folder_operations.read_json_file(cfg_path)
# model info
self.model_info = cfg["predictor_options"]["model_info"]
self.predictor_backend = cfg["predictor_options"]["model_info"][
"predictor_backend"]
self.method = cfg["predictor_options"]["model_info"]["method"]
self.model_id = cfg["predictor_options"]["model_info"]["model_id"]
return True
except:
self.logger.error("cfg file error", exc_info=True)
return False
def __set_options(self, cfg_path):
try:
cfg = file_folder_operations.read_json_file(cfg_path)
# model_options
self.predicted_image_action = cfg["predictor_options"]["model_options"]["predicted_image_action"]
self.confidence_threshold = cfg["predictor_options"]["model_options"]["confidence_threshold"]
# model_paths
self.predictions_main_folder = cfg["predictor_options"]["model_paths"]["predictions_main_folder"]
self.not_confiedent_name = cfg["predictor_options"]["model_paths"]["not_confiedent_folder_name"]
self.darknet_configPath = cfg["predictor_options"]["model_paths"]["darknet_configPath"]
self.darknet_weightPath = cfg["predictor_options"]["model_paths"]["darknet_weightPath"]
self.darknet_metaPath = cfg["predictor_options"]["model_paths"]["darknet_metaPath"]
return True
except:
self.logger.error("cfg file error", exc_info=True)
return False
def __set_options(self, cfg_path):
try:
cfg = file_folder_operations.read_json_file(cfg_path)
# model_options
self.simulate = cfg["predictor_options"]["model_options"][
"simulate"]
self.image_class = cfg["predictor_options"]["model_options"][
"image_class"]
self.sleep = cfg["predictor_options"]["model_options"]["sleep"]
self.predicted_image_action = cfg["predictor_options"][
"model_options"]["predicted_image_action"]
# model_paths
self.predictions_main_folder = cfg["predictor_options"][
"model_paths"]["predictions_main_folder"]
return True
except:
self.logger.error("cfg file error", exc_info=True)
return False
def __set_options(self, cfg_path):
try:
cfg = file_folder_operations.read_json_file(cfg_path)
# model_options
self.predicted_image_action = cfg["predictor_options"]["model_options"]["predicted_image_action"]
self.tf_yolo_image_size = cfg["predictor_options"]["model_options"]["input_size"]
self.iou_threshold = cfg["predictor_options"]["model_options"]["iou_threshold"]
self.score_threshold = cfg["predictor_options"]["model_options"]["score_threshold"]
self.max_output_size_per_class = cfg["predictor_options"]["model_options"]["max_output_size_per_class"]
self.max_total_size = cfg["predictor_options"]["model_options"]["max_total_size"]
# model_paths
self.predictions_main_folder = cfg["predictor_options"]["model_paths"]["predictions_main_folder"]
self.not_confiedent_name = cfg["predictor_options"]["model_paths"]["not_confiedent_folder_name"]
self.tf_yolo_model_path = cfg["predictor_options"]["model_paths"]["model_path"]
self.tf_yolo_names_path = cfg["predictor_options"]["model_paths"]["names_path"]
return True
except:
self.logger.error("cfg file error", exc_info=True)
return False
def create_app(deep_predictor_cfg_path):
# create logger
logger = logger_creator().flask_logger()
logger.info("creating flask app with cfg file on path:{0}".format(deep_predictor_cfg_path))
# set config options
cfg = file_folder_operations.read_json_file(deep_predictor_cfg_path)
# flask options
secret_key = cfg["deep_predictor_options"]["flask_options"]["secret_key"]
max_content_length = eval(cfg["deep_predictor_options"]["flask_options"]["max_content_length"])
debug = cfg["deep_predictor_options"]["flask_options"]["debug"]
# ---------- production options ----------
# recaptcha_options
recaptcha_sitekey = cfg["deep_predictor_options"]["production"]["recaptcha_options"]["recaptcha_sitekey"]
recaptcha_secret_key = cfg["deep_predictor_options"]["production"]["recaptcha_options"]["recaptcha_secret_key"]
# upload options
supported_extensions = cfg["deep_predictor_options"]["production"]["upload_options"]["supported_extensions"]
# api options
api_key = cfg["deep_predictor_options"]["production"]["api_options"]["api_key"]
prediction_id_length = cfg["deep_predictor_options"]["production"]["api_options"]["prediction_id_length"]
default_api_response = cfg["deep_predictor_options"]["production"]["api_options"]["default_api_response"]
get_prediction_endpoint = cfg["deep_predictor_options"]["production"]["api_options"]["get_prediction_endpoint"]
get_predictors_endpoint = cfg["deep_predictor_options"]["production"]["api_options"]["get_predictors_endpoint"]
# path options
temp_save_path = cfg["deep_predictor_options"]["production"]["path_options"]["temp_save_path"]
database_path = cfg["deep_predictor_options"]["production"]["path_options"]["database_path"]
# prediction options
default_predictor_name = cfg["deep_predictor_options"]["production"]["prediction_options"]["default_predictor_name"]
# create instace of db class
db = database_handler(database_path = database_path, check_connection = True, create_table = True)
# create predictors
predictors = {}
for predictor in cfg["deep_predictor_options"]["production"]["prediction_options"]["predictors"]:
predictors.update({
predictor : deep_predictor(cfg["deep_predictor_options"]["production"]["prediction_options"]["predictors"][predictor])
})
# --------------------------------------------------
# ---------- test options ----------
# upload options
TEST_supported_extensions = cfg["deep_predictor_options"]["test"]["upload_options"]["supported_extensions"]
# api options
TEST_key = cfg["deep_predictor_options"]["test"]["api_options"]["api_key"]
TEST_prediction_id_length = cfg["deep_predictor_options"]["test"]["api_options"]["prediction_id_length"]
TEST_default_api_response = cfg["deep_predictor_options"]["test"]["api_options"]["default_api_response"]
TEST_get_prediction_endpoint = cfg["deep_predictor_options"]["test"]["api_options"]["get_prediction_endpoint"]
TEST_get_predictors_endpoint = cfg["deep_predictor_options"]["test"]["api_options"]["get_predictors_endpoint"]
# path options
TEST_temp_save_path = cfg["deep_predictor_options"]["test"]["path_options"]["temp_save_path"]
TEST_database_path = cfg["deep_predictor_options"]["test"]["path_options"]["database_path"]
# create instace of db class
TEST_db = database_handler(database_path = TEST_database_path, check_connection = True, create_table = True)
# create predictors
TEST_predictors = {}
for predictor in cfg["deep_predictor_options"]["test"]["prediction_options"]["predictors"]:
TEST_predictors.update({
predictor : deep_predictor(cfg["deep_predictor_options"]["test"]["prediction_options"]["predictors"][predictor])
})
# --------------------------------------------------
# flask app
app = Flask(__name__)
app.config["DEBUG"] = debug
app.config['MAX_CONTENT_LENGTH'] = max_content_length
app.secret_key = secret_key
# ---------- regular routes ----------
@app.route('/', methods=['GET'])
def home():
logger.debug("function: {0} method: {1}".format("home", request.method))
return render_template("index.html"), 200
@app.route('/result', methods=['GET'])
def result():
logger.debug("function: {0} method: {1}".format("result", request.method))
return redirect(url_for("home")), 308
@app.route('/upload', methods=['POST', 'GET'])
def upload():
logger.debug("function: {0} method: {1}".format("upload", request.method))
if(request.method == 'POST'):
# ---------- check key errors from form ----------
try:
uploaded_file = request.files['image']
filename = secure_filename(uploaded_file.filename)
model_name = request.form['model_name']
prediction_id = request.form['prediction_id']
except KeyError:
logger.warning("KeyError", exc_info=True)
abort(400, description="Unknown key received from form")
recaptcha_response = request.form.get('g-recaptcha-response')
form_api_key = request.form.get('api_key')
# --------------------------------------------------
# ---------- check recaptcha or api_key ----------
if(form_api_key):
# check api key
if(api_key != form_api_key):
logger.warning("api key is incorrect")
abort(400, description="Api key is incorrect")
elif(recaptcha_response):
# check recaptcha
if(not validate_recaptcha(recaptcha_response, recaptcha_secret_key)):
logger.warning("recaptcha is not verified")
return render_template("upload.html", models=predictors.keys(), recaptcha_sitekey=recaptcha_sitekey), 400
else:
logger.warning("recaptcha or api key is not provided")
abort(400, description="Recaptcha or api key is not provided")
# --------------------------------------------------
# ---------- check form elements ----------
if(model_name == ""):
model_name = default_predictor_name
if(filename == "" or prediction_id == ""):
logger.warning("required form elements are empty")
return render_template("upload.html", models=predictors.keys(), recaptcha_sitekey=recaptcha_sitekey), 400
# prediction_id length
if(len(prediction_id) != prediction_id_length):
logger.warning("prediction_id length is wrong")
abort(400, description="Something went wrong please try again")
# model name
if(model_name not in predictors):
logger.warning("model name is not exists")
abort(400, description="This predictor is not exists")
# image extension
if(not image_operations.validate_extension(supported_extensions, filename, is_case_sensitive=False)):
logger.warning("image extension is not supported")
abort(400, description="Your image extension is not supported")
# --------------------------------------------------
# check for prediction_id collision
if(db.is_prediction_exists(prediction_id)):
logger.warning("prediction id exists")
abort(500, description="Something went wrong please try again")
# generate unique id
# unique_full_filename = file_folder_operations.create_unique_file_name(os.path.join(temp_save_path, filename))
unique_filename = str(uuid.uuid4())
_, file_extension = os.path.splitext(filename)
unique_full_filename = os.path.join(temp_save_path, unique_filename + file_extension)
uploaded_file.save(unique_full_filename)
# check image resizability
status, unique_full_filename = image_operations.validate_image(unique_full_filename, try_to_convert = True, delete_unresizable = True)
if(not status):
logger.warning("image is not supported")
abort(400, description="Your image is not supported")
# prepare preediction
model_info, model_id = predictors[model_name].get_model_info()
db.create_prediction(prediction_id, model_info, model_id)
# start prediction on thread
pred_thread = prediction_thread(
database_path,
unique_full_filename,
prediction_id,
predictor=predictors[model_name]
)
pred_thread.start()
return render_template("result.html", prediction_id=prediction_id), 201
else:
return render_template("upload.html", models=predictors.keys(), recaptcha_sitekey=recaptcha_sitekey), 200
@app.route('/api', methods=['GET'])
def api():
logger.debug("function: {0} method: {1}".format("api", request.method))
# get args
arguments = request.args
prediction_id = arguments.get(get_prediction_endpoint)
test_prediction_id = arguments.get(TEST_get_prediction_endpoint)
if(prediction_id):
prediction = db.get_prediction_json(prediction_id)
return prediction, 200
elif(get_predictors_endpoint in arguments):
return {get_predictors_endpoint : list(predictors.keys())}, 200
else:
logger.warning("prediction_id is not provided")
return default_api_response, 400
# --------------------------------------------------
# ---------- error routes ----------
@app.route('/', defaults={'path': ''})
@app.route('/<path:path>')
def catch_all(path):
# all to 404
# e.code
# e.name
abort(404)
@app.errorhandler(404)
@app.errorhandler(400)
@app.errorhandler(413)
@app.errorhandler(500)
def generic_error(e):
logger.debug("function: {0} method: {1}".format("error", request.method))
if(e.code == 404):
return render_template('error.html', description="The page you are looking for is not found", code=e.code), e.code
elif(e.code == 413):
return render_template('error.html', description="Your image is too large", code=e.code), e.code
else:
return render_template('error.html', description=e.description, code=e.code), e.code
# --------------------------------------------------
# ---------- test routes ----------
@app.route('/test-upload', methods=['POST', 'GET'])
def test_upload():
if(request.method == 'POST'):
logger.debug("function: {0} method: {1}".format("test_upload", request.method))
# ---------- check key errors from form ----------
try:
uploaded_file = request.files['image']
filename = secure_filename(uploaded_file.filename)
model_name = request.form['model_name']
prediction_id = request.form['prediction_id']
form_api_key = request.form['api_key']
except KeyError:
logger.warning("KeyError", exc_info=True)
abort(400, description="Unknown key received from form")
# --------------------------------------------------
# ---------- check test api key ----------
if(TEST_key != form_api_key):
logger.warning("test api key is incorrect")
abort(400, description="test api key is incorrect")
# --------------------------------------------------
# ---------- check form elements ----------
if(filename == "" or prediction_id == "" or model_name == ""):
logger.warning("required form elements are empty")
abort(400, description="Required form elements are empty")
# prediction_id length
if(len(prediction_id) != TEST_prediction_id_length):
logger.warning("prediction_id length is wrong")
abort(400, description="Something went wrong please try again")
# model name
if(model_name not in TEST_predictors):
logger.warning("model name is not exists")
abort(400, description="This predictor is not exists")
# image extension
if(not image_operations.validate_extension(TEST_supported_extensions, filename, is_case_sensitive=False)):
logger.warning("image extension is not supported")
abort(400, description="Your image extension is not supported")
# --------------------------------------------------
# check for prediction_id collision
if(TEST_db.is_prediction_exists(prediction_id)):
logger.warning("prediction id exists")
abort(500, description="Something went wrong please try again")
# generate unique id
# unique_full_filename = file_folder_operations.create_unique_file_name(os.path.join(temp_save_path, filename))
unique_filename = str(uuid.uuid4())
_, file_extension = os.path.splitext(filename)
unique_full_filename = os.path.join(TEST_temp_save_path, unique_filename + file_extension)
uploaded_file.save(unique_full_filename)
# check image resizability
status, unique_full_filename = image_operations.validate_image(unique_full_filename, try_to_convert = True, delete_unresizable = True)
if(not status):
logger.warning("image is not supported")
abort(400, description="Your image is not supported")
# prepare preediction
model_info, model_id = TEST_predictors[model_name].get_model_info()
TEST_db.create_prediction(prediction_id, model_info, model_id)
# start prediction on thread
pred_thread = prediction_thread(
TEST_database_path,
unique_full_filename,
prediction_id,
predictor=TEST_predictors[model_name]
)
pred_thread.start()
return "success", 201
else:
return redirect(url_for("home")), 308
@app.route('/test-api', methods=['GET'])
def test_api():
logger.debug("function: {0} method: {1}".format("test_api", request.method))
# get args
arguments = request.args
prediction_id = arguments.get(TEST_get_prediction_endpoint)
if(prediction_id):
prediction = TEST_db.get_prediction_json(prediction_id)
return prediction, 200
elif(TEST_get_predictors_endpoint in arguments):
return {TEST_get_predictors_endpoint : list(TEST_predictors.keys())}, 200
else:
logger.warning("prediction_id is not provided")
return TEST_default_api_response, 400
# --------------------------------------------------
@app.route('/admin', methods=['GET'])
def roll():
return redirect("https://www.youtube.com/watch?v=dQw4w9WgXcQ&ab_channel=RickAstleyVEVO")
# return flask app for serving
return app
def read_cfg_file(self):
self.cfg = file_folder_operations.read_json_file(self.cfg_file_path)
def __set_options(self):
"""reads cfg file and assigns cfg values to local variables"""
self.__logger.info("flask_app using cfg file on path:{0}".format(
self.__deep_predictor_cfg_path))
# set config options
cfg = file_folder_operations.read_json_file(
self.__deep_predictor_cfg_path)
# flask options
self.__secret_key = cfg["deep_predictor_options"]["flask_options"][
"secret_key"]
self.__max_content_length = eval(
cfg["deep_predictor_options"]["flask_options"]
["max_content_length"])
self.__debug = cfg["deep_predictor_options"]["flask_options"]["debug"]
# ---------- production options ----------
# recaptcha_options
self.__recaptcha_sitekey = cfg["deep_predictor_options"]["production"][
"recaptcha_options"]["recaptcha_sitekey"]
self.__recaptcha_secret_key = cfg["deep_predictor_options"][
"production"]["recaptcha_options"]["recaptcha_secret_key"]
# upload options
self.__supported_extensions = cfg["deep_predictor_options"][
"production"]["upload_options"]["supported_extensions"]
# api options
self.__api_key = cfg["deep_predictor_options"]["production"][
"api_options"]["api_key"]
self.__prediction_id_length = cfg["deep_predictor_options"][
"production"]["api_options"]["prediction_id_length"]
self.__default_api_response = cfg["deep_predictor_options"][
"production"]["api_options"]["default_api_response"]
self.__get_prediction_endpoint = cfg["deep_predictor_options"][
"production"]["api_options"]["get_prediction_endpoint"]
self.__get_predictors_endpoint = cfg["deep_predictor_options"][
"production"]["api_options"]["get_predictors_endpoint"]
# path options
self.__temp_save_path = cfg["deep_predictor_options"]["production"][
"path_options"]["temp_save_path"]
self.__database_path = cfg["deep_predictor_options"]["production"][
"path_options"]["database_path"]
# prediction options
self.__default_predictor_name = cfg["deep_predictor_options"][
"production"]["prediction_options"]["default_predictor_name"]
# predictor cfg paths
self.__predictor_cfgs = cfg["deep_predictor_options"]["production"][
"prediction_options"]["predictors"]
# --------------------------------------------------
# ---------- test options ----------
# upload options
self.__TEST_supported_extensions = cfg["deep_predictor_options"][
"test"]["upload_options"]["supported_extensions"]
# api options
self.__TEST_key = cfg["deep_predictor_options"]["test"]["api_options"][
"api_key"]
self.__TEST_prediction_id_length = cfg["deep_predictor_options"][
"test"]["api_options"]["prediction_id_length"]
self.__TEST_default_api_response = cfg["deep_predictor_options"][
"test"]["api_options"]["default_api_response"]
self.__TEST_get_prediction_endpoint = cfg["deep_predictor_options"][
"test"]["api_options"]["get_prediction_endpoint"]
self.__TEST_get_predictors_endpoint = cfg["deep_predictor_options"][
"test"]["api_options"]["get_predictors_endpoint"]
# path options
self.__TEST_temp_save_path = cfg["deep_predictor_options"]["test"][
"path_options"]["temp_save_path"]
self.__TEST_database_path = cfg["deep_predictor_options"]["test"][
"path_options"]["database_path"]
# predictor cfg paths
self.__TEST_predictor_cfgs = cfg["deep_predictor_options"]["test"][
"prediction_options"]["predictors"]
