src/input/input_observation.py

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"])