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import hashlib
from input.input_validator import generate_random_md5
# autogenerated class to hold the input data
class InputObservation:
"""
A class to hold an input observation and associated metadata
Attributes:
image (Any):
The image associated with the observation.
latitude (float):
The latitude where the observation was made.
longitude (float):
The longitude where the observation was made.
author_email (str):
The email of the author of the observation.
date (str):
The date when the observation was made.
time (str):
The time when the observation was made.
date_option (str):
Additional date option for the observation.
time_option (str):
Additional time option for the observation.
uploaded_filename (Any):
The uploaded filename associated with the observation.
Methods:
__str__():
Returns a string representation of the observation.
__repr__():
Returns a string representation of the observation.
__eq__(other):
Checks if two observations are equal.
__ne__(other):
Checks if two observations are not equal.
__hash__():
Returns the hash of the observation.
to_dict():
Converts the observation to a dictionary.
from_dict(data):
Creates an observation from a dictionary.
from_input(input):
Creates an observation from another input observation.
"""
def __init__(self, image=None, latitude=None, longitude=None,
author_email=None, date=None, time=None, date_option=None, time_option=None,
uploaded_filename=None):
self.image = image
self.latitude = latitude
self.longitude = longitude
self.author_email = author_email
self.date = date
self.time = time
self.date_option = date_option
self.time_option = time_option
self.uploaded_filename = uploaded_filename
self._top_predictions = []
def set_top_predictions(self, top_predictions:list):
self._top_predictions = top_predictions
# add a method to get the top predictions (property?)
@property
def top_predictions(self):
return self._top_predictions
def __str__(self):
return f"Observation: {self.image}, {self.latitude}, {self.longitude}, {self.author_email}, {self.date}, {self.time}, {self.date_option}, {self.time_option}, {self.uploaded_filename}"
def __repr__(self):
return f"Observation: {self.image}, {self.latitude}, {self.longitude}, {self.author_email}, {self.date}, {self.time}, {self.date_option}, {self.time_option}, {self.uploaded_filename}"
def __eq__(self, other):
return (self.image == other.image and self.latitude == other.latitude and self.longitude == other.longitude and
self.author_email == other.author_email and self.date == other.date and self.time == other.time and
self.date_option == other.date_option and self.time_option == other.time_option and self.uploaded_filename == other.uploaded_filename)
def __ne__(self, other):
return not self.__eq__(other)
def __hash__(self):
return hash((self.image, self.latitude, self.longitude, self.author_email, self.date, self.time, self.date_option, self.time_option, self.uploaded_filename))
def to_dict(self):
return {
#"image": self.image,
"image_filename": self.uploaded_filename.name if self.uploaded_filename else None,
"image_md5": hashlib.md5(self.uploaded_filename.read()).hexdigest() if self.uploaded_filename else generate_random_md5(),
"latitude": self.latitude,
"longitude": self.longitude,
"author_email": self.author_email,
"date": self.date,
"time": self.time,
"date_option": str(self.date_option),
"time_option": str(self.time_option),
"uploaded_filename": self.uploaded_filename
}
@classmethod
def from_dict(cls, data):
return cls(data["image"], data["latitude"], data["longitude"], data["author_email"], data["date"], data["time"], data["date_option"], data["time_option"], data["uploaded_filename"])
@classmethod
def from_input(cls, input):
return cls(input.image, input.latitude, input.longitude, input.author_email, input.date, input.time, input.date_option, input.time_option, input.uploaded_filename)
@staticmethod
def from_input(input):
return InputObservation(input.image, input.latitude, input.longitude, input.author_email, input.date, input.time, input.date_option, input.time_option, input.uploaded_filename)
@staticmethod
def from_dict(data):
return InputObservation(data["image"], data["latitude"], data["longitude"], data["author_email"], data["date"], data["time"], data["date_option"], data["time_option"], data["uploaded_filename"])
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