def classify_flight_delays_realtime():
"""POST API for classifying flight delays"""
# Define the form fields to process
api_field_type_map = \
{
"DepDelay": float,
"Carrier": str,
"FlightDate": str,
"Dest": str,
"FlightNum": str,
"Origin": str
}
# Fetch the values for each field from the form object
api_form_values = {}
for api_field_name, api_field_type in api_field_type_map.items():
api_form_values[api_field_name] = request.form.get(api_field_name, type=api_field_type)
# Set the direct values, which excludes Date
prediction_features = {}
for key, value in api_form_values.items():
prediction_features[key] = value
# Set the derived values
prediction_features['Distance'] = predict_utils.get_flight_distance(
client, api_form_values['Origin'],
api_form_values['Dest']
)
# Turn the date into DayOfYear, DayOfMonth, DayOfWeek
date_features_dict = predict_utils.get_regression_date_args(
api_form_values['FlightDate']
)
for api_field_name, api_field_value in date_features_dict.items():
prediction_features[api_field_name] = api_field_value
# Add a timestamp
prediction_features['Timestamp'] = predict_utils.get_current_timestamp()
# Create a unique ID for this message
unique_id = str(uuid.uuid4())
prediction_features['UUID'] = unique_id
message_bytes = json.dumps(prediction_features).encode()
producer.send(PREDICTION_TOPIC, message_bytes)
response = {"status": "OK", "id": unique_id}
return json_util.dumps(response)
def classify_flight_delays_realtime():
"""POST API for classifying flight delays"""
# Define the form fields to process
api_field_type_map = \
{
"DepDelay": float,
"Carrier": str,
"FlightDate": str,
"Dest": str,
"FlightNum": str,
"Origin": str
}
# Fetch the values for each field from the form object
api_form_values = {}
for api_field_name, api_field_type in api_field_type_map.items():
api_form_values[api_field_name] = request.form.get(api_field_name, type=api_field_type)
# Set the direct values, which excludes Date
prediction_features = {}
for key, value in api_form_values.items():
prediction_features[key] = value
# Set the derived values
prediction_features['Distance'] = predict_utils.get_flight_distance(
client, api_form_values['Origin'],
api_form_values['Dest']
)
# Turn the date into DayOfYear, DayOfMonth, DayOfWeek
date_features_dict = predict_utils.get_regression_date_args(
api_form_values['FlightDate']
)
for api_field_name, api_field_value in date_features_dict.items():
prediction_features[api_field_name] = api_field_value
# Add a timestamp
prediction_features['Timestamp'] = predict_utils.get_current_timestamp()
# Create a unique ID for this message
unique_id = str(uuid.uuid4())
prediction_features['UUID'] = unique_id
message_bytes = json.dumps(prediction_features).encode()
producer.send(PREDICTION_TOPIC, message_bytes)
response = {"status": "OK", "id": unique_id}
return json_util.dumps(response)
def classify_flight_delays():
"""POST API for classifying flight delays"""
api_field_type_map = \
{
"DepDelay": float,
"Carrier": str,
"FlightDate": str,
"Dest": str,
"FlightNum": str,
"Origin": str
}
api_form_values = {}
for api_field_name, api_field_type in api_field_type_map.items():
api_form_values[api_field_name] = request.form.get(api_field_name, type=api_field_type)
# Set the direct values, which excludes Date
prediction_features = {}
for key, value in api_form_values.items():
prediction_features[key] = value
# Set the derived values
prediction_features['Distance'] = predict_utils.get_flight_distance(
client, api_form_values['Origin'],
api_form_values['Dest']
)
# Turn the date into DayOfYear, DayOfMonth, DayOfWeek
date_features_dict = predict_utils.get_regression_date_args(
api_form_values['FlightDate']
)
for api_field_name, api_field_value in date_features_dict.items():
prediction_features[api_field_name] = api_field_value
# Add a timestamp
prediction_features['Timestamp'] = predict_utils.get_current_timestamp()
client.agile_data_science.prediction_tasks.insert_one(
prediction_features
)
return json_util.dumps(prediction_features)
def classify_flight_delays():
"""POST API for classifying flight delays"""
api_field_type_map = \
{
"DepDelay": float,
"Carrier": str,
"FlightDate": str,
"Dest": str,
"FlightNum": str,
"Origin": str
}
api_form_values = {}
for api_field_name, api_field_type in api_field_type_map.items():
api_form_values[api_field_name] = request.form.get(api_field_name, type=api_field_type)
# Set the direct values, which excludes Date
prediction_features = {}
for key, value in api_form_values.items():
prediction_features[key] = value
# Set the derived values
prediction_features['Distance'] = predict_utils.get_flight_distance(
client, api_form_values['Origin'],
api_form_values['Dest']
)
# Turn the date into DayOfYear, DayOfMonth, DayOfWeek
date_features_dict = predict_utils.get_regression_date_args(
api_form_values['FlightDate']
)
for api_field_name, api_field_value in date_features_dict.items():
prediction_features[api_field_name] = api_field_value
# Add a timestamp
prediction_features['Timestamp'] = predict_utils.get_current_timestamp()
client.agile_data_science.prediction_tasks.insert_one(
prediction_features
)
return json_util.dumps(prediction_features)
