data_models.py

"""
Pydantic models used throughout the codebase.

In particular, these are the types that are used as input and output of each step of the pipeline.
"""
import json
from typing import Any, Optional, Union, List

from pydantic import BaseModel, Field


class RedirectInput(BaseModel):
    pipeline_arn: str
    job_id: str


class NutrientTableQuantity(BaseModel):
    measurementUnitCode: str
    value: str
    precisionCode: str

    def __str__(self):
        return f"{self.precisionCode} {self.value} {self.measurementUnitCode}"


class NutrientTableDailyValueIntake(BaseModel):
    value: str
    precisionCode: str

    def __str__(self):
        return f"{self.precisionCode} {self.value}%"


def s(
    text: Optional[Union[NutrientTableQuantity, NutrientTableDailyValueIntake, str]]
) -> str:
    """
    Returns None as "?", otherwise argument as string.
    """
    if text:
        return str(text)
    else:
        return "?"


class NutrientTableElement(BaseModel):
    coordinates: str
    probability: float
    nutrientTypeCode: Optional[str]
    quantityContained: NutrientTableQuantity
    dailyValueIntakePercent: Optional[NutrientTableDailyValueIntake]
    precisionCode: str

    def __str__(self):
        return " ".join(
            [
                s(self.nutrientTypeCode),
                s(self.quantityContained),
                f"({s(self.dailyValueIntakePercent)})",
            ]
        )


class NutrientTable(BaseModel):
    nutrientBasisQuantityValue: Optional[str]
    nutrientBasisQuantityMeasurementUnitCode: Optional[str]
    preperationStateCode: Optional[str]
    values: List[NutrientTableElement]

    def __str__(self):
        top = "Nutrients per " + " ".join(
            [
                s(self.nutrientBasisQuantityValue),
                s(self.nutrientBasisQuantityMeasurementUnitCode),
                f"({s(self.preperationStateCode)})",
            ]
        )
        vals = "\n\t".join([str(v) for v in self.values])
        return f"{top}\n\t{vals}"


class Attribute(BaseModel):
    coordinates: str
    entity: str
    probability: float
    value: Union[str, List[NutrientTable]]
    model: str


class AttributeCommunicationChannel(BaseModel):
    coordinates: str
    probability: float
    model: str
    entity: str
    communicationChannelCode: str
    communicationValue: str


class AttributeAllergen(BaseModel):
    coordinates: str
    probability: float
    model: str
    entity: str
    allergenTypeCode: str
    levelOfContainmentCode: str


class NetContentAttribute(BaseModel):
    coordinates: str
    probability: float
    model: str
    entity: str
    measurementUnitCode: str
    value: str


class AllergensOut(BaseModel):
    entity: str
    values: List[AttributeAllergen]
    model: str


class CommunicationChannelsOut(BaseModel):
    entity: str
    values: List[AttributeCommunicationChannel]
    model: str


class PipelineInput(BaseModel):
    image_key: str


class PipelineOutput(BaseModel):
    attributes: List[
        Union[Attribute, CommunicationChannelsOut, AllergensOut, NetContentAttribute]
    ]
    job_id: str = Field(alias="job-id")
    text: str

    class Config:
        allow_population_by_field_name = True


class TextWithLanguage(BaseModel):
    text: str
    lang_code: str


class OCRTextOut(BaseModel):
    blocks: List[str]
    full_text: str
    sentences: List[TextWithLanguage]


class OCRTableOut(BaseModel):
    tables: List[List[List[str]]]


class OCROut(BaseModel):
    result: Union[OCRTextOut, OCRTableOut]
    job_id: str


class OCROutList(BaseModel):
    __root__: List[OCROut]

    def __iter__(self):
        return iter(self.__root__)

    def __getitem__(self, item):
        return self.__root__[item]


class OCRWrapperOut(BaseModel):
    blocks: List[str]
    full_text: str
    job_id: str
    sentences: List[TextWithLanguage]
    tables: List[List[List[str]]]


class ClassifiedText(BaseModel):
    text: str
    attribute: str
    confidence: float


class CommunicationChannels(BaseModel):
    confidence: float
    attribute: str
    communicationChannelCode: str
    communicationValue: str
    text: Optional[str] = ""


class Allergen(BaseModel):
    confidence: float
    attribute: str
    allergenTypeCode: str
    levelOfContainmentCode: str
    text: Optional[str] = ""


class NetContent(BaseModel):
    confidence: float
    attribute: str
    measurementUnitCode: str
    value: str
    text: Optional[str] = ""


class ModelOut(BaseModel):
    blocks: List[Union[NetContent, Allergen, CommunicationChannels, ClassifiedText]]
    tables: Optional[List[NutrientTable]]
    job_id: str
    model: str
    full_text: str

    def toJSON(self):
        return json.dumps(self, default=lambda o: o.__dict__, sort_keys=True)


class ModelOutList(BaseModel):
    __root__: List[ModelOut]

    def __iter__(self):
        return iter(self.__root__)

    def __getitem__(self, item):
        return self.__root__[item]


class TrainModelOut(BaseModel):
    # To be defined later when we have a List of accepted formats
    model: Optional[Any] = None
    artifacts: Optional[Any] = None