mirror of
https://github.com/mealie-recipes/mealie.git
synced 2025-07-19 13:19:41 +02:00
feat: adding the rest ofthe nutrition properties from schema.org (#4301)
This commit is contained in:
parent
3aea229f2d
commit
02c0fe993b
16 changed files with 279 additions and 57 deletions
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@ -0,0 +1,39 @@
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"""'add the rest of the schema.org nutrition properties'
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Revision ID: 602927e1013e
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Revises: 1fe4bd37ccc8
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Create Date: 2024-10-01 14:17:00.611398
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"""
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import sqlalchemy as sa
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from alembic import op
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# revision identifiers, used by Alembic.
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revision = "602927e1013e"
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down_revision: str | None = "1fe4bd37ccc8"
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branch_labels: str | tuple[str, ...] | None = None
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depends_on: str | tuple[str, ...] | None = None
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def upgrade():
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# ### commands auto generated by Alembic - please adjust! ###
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with op.batch_alter_table("recipe_nutrition", schema=None) as batch_op:
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batch_op.add_column(sa.Column("cholesterol_content", sa.String(), nullable=True))
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batch_op.add_column(sa.Column("saturated_fat_content", sa.String(), nullable=True))
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batch_op.add_column(sa.Column("trans_fat_content", sa.String(), nullable=True))
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batch_op.add_column(sa.Column("unsaturated_fat_content", sa.String(), nullable=True))
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# ### end Alembic commands ###
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def downgrade():
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# ### commands auto generated by Alembic - please adjust! ###
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with op.batch_alter_table("recipe_nutrition", schema=None) as batch_op:
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batch_op.drop_column("unsaturated_fat_content")
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batch_op.drop_column("trans_fat_content")
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batch_op.drop_column("saturated_fat_content")
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batch_op.drop_column("cholesterol_content")
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# ### end Alembic commands ###
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@ -17,6 +17,14 @@ export function useNutritionLabels() {
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label: i18n.tc("recipe.calories"),
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suffix: i18n.tc("recipe.calories-suffix"),
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},
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carbohydrateContent: {
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label: i18n.tc("recipe.carbohydrate-content"),
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suffix: i18n.tc("recipe.grams"),
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},
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cholesterolContent: {
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label: i18n.tc("recipe.cholesterol-content"),
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suffix: i18n.tc("recipe.milligrams"),
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},
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fatContent: {
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label: i18n.tc("recipe.fat-content"),
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suffix: i18n.tc("recipe.grams"),
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@ -29,6 +37,10 @@ export function useNutritionLabels() {
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label: i18n.tc("recipe.protein-content"),
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suffix: i18n.tc("recipe.grams"),
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},
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saturatedFatContent: {
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label: i18n.tc("recipe.saturated-fat-content"),
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suffix: i18n.tc("recipe.grams"),
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},
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sodiumContent: {
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label: i18n.tc("recipe.sodium-content"),
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suffix: i18n.tc("recipe.milligrams"),
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@ -37,8 +49,12 @@ export function useNutritionLabels() {
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label: i18n.tc("recipe.sugar-content"),
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suffix: i18n.tc("recipe.grams"),
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},
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carbohydrateContent: {
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label: i18n.tc("recipe.carbohydrate-content"),
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transFatContent: {
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label: i18n.tc("recipe.trans-fat-content"),
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suffix: i18n.tc("recipe.grams"),
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},
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unsaturatedFatContent: {
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label: i18n.tc("recipe.unsaturated-fat-content"),
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suffix: i18n.tc("recipe.grams"),
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},
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};
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@ -461,6 +461,7 @@
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"calories-suffix": "calories",
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"carbohydrate-content": "Carbohydrate",
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"categories": "Categories",
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"cholesterol-content": "Cholesterol",
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"comment-action": "Comment",
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"comment": "Comment",
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"comments": "Comments",
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@ -507,6 +508,7 @@
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"recipe-updated": "Recipe updated",
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"remove-from-favorites": "Remove from Favorites",
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"remove-section": "Remove Section",
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"saturated-fat-content": "Saturated fat",
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"save-recipe-before-use": "Save recipe before use",
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"section-title": "Section Title",
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"servings": "Servings",
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@ -517,7 +519,9 @@
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"sugar-content": "Sugar",
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"title": "Title",
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"total-time": "Total Time",
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"trans-fat-content": "Trans-fat",
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"unable-to-delete-recipe": "Unable to Delete Recipe",
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"unsaturated-fat-content": "Unsaturated fat",
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"no-recipe": "No Recipe",
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"locked-by-owner": "Locked by Owner",
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"join-the-conversation": "Join the Conversation",
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@ -194,12 +194,16 @@ export interface MergeUnit {
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}
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export interface Nutrition {
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calories?: string | null;
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fatContent?: string | null;
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proteinContent?: string | null;
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carbohydrateContent?: string | null;
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cholesterolContent?: string | null;
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fatContent?: string | null;
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fiberContent?: string | null;
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proteinContent?: string | null;
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saturatedFatContent?: string | null;
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sodiumContent?: string | null;
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sugarContent?: string | null;
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transFatContent?: string | null;
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unsaturatedFatContent?: string | null;
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}
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export interface ParsedIngredient {
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input?: string | null;
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@ -486,7 +490,7 @@ export interface ScrapeRecipeTest {
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url: string;
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useOpenAI?: boolean;
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}
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export interface SlugResponse {}
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export interface SlugResponse { }
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export interface TagIn {
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name: string;
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}
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@ -9,28 +9,52 @@ class Nutrition(SqlAlchemyBase):
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__tablename__ = "recipe_nutrition"
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id: Mapped[int] = mapped_column(sa.Integer, primary_key=True)
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recipe_id: Mapped[GUID | None] = mapped_column(GUID, sa.ForeignKey("recipes.id"), index=True)
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calories: Mapped[str | None] = mapped_column(sa.String)
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carbohydrate_content: Mapped[str | None] = mapped_column(sa.String)
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cholesterol_content: Mapped[str | None] = mapped_column(sa.String)
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fat_content: Mapped[str | None] = mapped_column(sa.String)
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fiber_content: Mapped[str | None] = mapped_column(sa.String)
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protein_content: Mapped[str | None] = mapped_column(sa.String)
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carbohydrate_content: Mapped[str | None] = mapped_column(sa.String)
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saturated_fat_content: Mapped[str | None] = mapped_column(sa.String)
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# `serving_size` is not a scaling factor, but a per-serving volume or mass
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# according to schema.org. E.g., "2 L", "500 g", "5 cups", etc.
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#
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# Ignoring for now because it's too difficult to work around variable units
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# in translation for the frontend. Also, it causes cognitive dissonance wrt
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# "servings" (i.e., "serves 2" etc.), which is an unrelated concept that
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# might cause confusion.
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#
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# serving_size: Mapped[str | None] = mapped_column(sa.String)
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sodium_content: Mapped[str | None] = mapped_column(sa.String)
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sugar_content: Mapped[str | None] = mapped_column(sa.String)
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trans_fat_content: Mapped[str | None] = mapped_column(sa.String)
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unsaturated_fat_content: Mapped[str | None] = mapped_column(sa.String)
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def __init__(
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self,
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calories=None,
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carbohydrate_content=None,
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cholesterol_content=None,
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fat_content=None,
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fiber_content=None,
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protein_content=None,
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saturated_fat_content=None,
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sodium_content=None,
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sugar_content=None,
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carbohydrate_content=None,
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trans_fat_content=None,
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unsaturated_fat_content=None,
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) -> None:
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self.calories = calories
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self.carbohydrate_content = carbohydrate_content
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self.cholesterol_content = cholesterol_content
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self.fat_content = fat_content
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self.fiber_content = fiber_content
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self.protein_content = protein_content
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self.saturated_fat_content = saturated_fat_content
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self.sodium_content = sodium_content
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self.sugar_content = sugar_content
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self.carbohydrate_content = carbohydrate_content
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self.trans_fat_content = trans_fat_content
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self.unsaturated_fat_content = unsaturated_fat_content
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@ -187,7 +187,7 @@ class RecipeModel(SqlAlchemyBase, BaseMixins):
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settings: dict | None = None,
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**_,
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) -> None:
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self.nutrition = Nutrition(**nutrition) if nutrition else Nutrition()
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self.nutrition = Nutrition(**(nutrition or {}))
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if recipe_instructions is not None:
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self.recipe_instructions = [RecipeInstruction(**step, session=session) for step in recipe_instructions]
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@ -198,7 +198,7 @@ class RecipeModel(SqlAlchemyBase, BaseMixins):
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if assets:
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self.assets = [RecipeAsset(**a) for a in assets]
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self.settings = RecipeSettings(**settings) if settings else RecipeSettings()
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self.settings = RecipeSettings(**(settings or {}))
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if notes:
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self.notes = [Note(**n) for n in notes]
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@ -104,15 +104,7 @@ def content_with_meta(group_slug: str, recipe: Recipe) -> str:
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ingredients.append(s)
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nutrition: dict[str, str | None] = {}
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if recipe.nutrition:
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nutrition["calories"] = recipe.nutrition.calories
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nutrition["fatContent"] = recipe.nutrition.fat_content
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nutrition["fiberContent"] = recipe.nutrition.fiber_content
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nutrition["proteinContent"] = recipe.nutrition.protein_content
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nutrition["carbohydrateContent"] = recipe.nutrition.carbohydrate_content
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nutrition["sodiumContent"] = recipe.nutrition.sodium_content
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nutrition["sugarContent"] = recipe.nutrition.sugar_content
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nutrition: dict[str, str | None] = recipe.nutrition.model_dump(by_alias=True) if recipe.nutrition else {}
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as_schema_org = {
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"@context": "https://schema.org",
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from pydantic import ConfigDict
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from pydantic.alias_generators import to_camel
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from mealie.schema._mealie import MealieModel
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class Nutrition(MealieModel):
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calories: str | None = None
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fat_content: str | None = None
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protein_content: str | None = None
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carbohydrate_content: str | None = None
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cholesterol_content: str | None = None
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fat_content: str | None = None
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fiber_content: str | None = None
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protein_content: str | None = None
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saturated_fat_content: str | None = None
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sodium_content: str | None = None
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sugar_content: str | None = None
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model_config = ConfigDict(from_attributes=True, coerce_numbers_to_str=True)
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trans_fat_content: str | None = None
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unsaturated_fat_content: str | None = None
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model_config = ConfigDict(
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from_attributes=True,
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coerce_numbers_to_str=True,
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alias_generator=to_camel,
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)
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@ -12,6 +12,18 @@ from mealie.services.scraper import cleaner
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from ._migration_base import BaseMigrator
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from .utils.migration_helpers import scrape_image, split_by_line_break, split_by_semicolon
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nutrition_map = {
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"carbohydrate": "carbohydrateContent",
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"protein": "proteinContent",
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"fat": "fatContent",
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"saturatedfat": "saturatedFatContent",
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"transfat": "transFatContent",
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"sodium": "sodiumContent",
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"fiber": "fiberContent",
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"sugar": "sugarContent",
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"unsaturatedfat": "unsaturatedFatContent",
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}
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class MyRecipeBoxMigrator(BaseMigrator):
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def __init__(self, **kwargs):
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except Exception:
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return None
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def parse_nutrition(self, input: Any) -> dict | None:
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if not input or not isinstance(input, str):
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def parse_nutrition(self, input_: Any) -> dict | None:
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if not input_ or not isinstance(input_, str):
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return None
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nutrition = {}
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vals = [x.strip() for x in input.split(",") if x]
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vals = (x.strip() for x in input_.split("\n") if x)
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for val in vals:
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try:
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key, value = val.split(":", maxsplit=1)
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key, value = (x.strip() for x in val.split(":", maxsplit=1))
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if not (key and value):
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continue
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key = nutrition_map.get(key.lower(), key)
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except ValueError:
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continue
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nutrition[key.strip()] = value.strip()
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nutrition[key] = value
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return cleaner.clean_nutrition(nutrition) if nutrition else None
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@ -37,6 +37,19 @@ def get_value_as_string_or_none(dictionary: dict, key: str):
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return None
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nutrition_map = {
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"Calories": "calories",
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"Fat": "fatContent",
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"Saturated Fat": "saturatedFatContent",
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"Cholesterol": "cholesterolContent",
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"Sodium": "sodiumContent",
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"Sugar": "sugarContent",
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"Carbohydrate": "carbohydrateContent",
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"Fiber": "fiberContent",
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"Protein": "proteinContent",
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}
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class PlanToEatMigrator(BaseMigrator):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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def _parse_recipe_nutrition_from_row(self, row: dict) -> dict:
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"""Parses the nutrition data from the row"""
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nut_dict: dict = {}
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nut_dict["calories"] = get_value_as_string_or_none(row, "Calories")
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nut_dict["fatContent"] = get_value_as_string_or_none(row, "Fat")
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nut_dict["proteinContent"] = get_value_as_string_or_none(row, "Protein")
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nut_dict["carbohydrateContent"] = get_value_as_string_or_none(row, "Carbohydrate")
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nut_dict["fiberContent"] = get_value_as_string_or_none(row, "Fiber")
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nut_dict["sodiumContent"] = get_value_as_string_or_none(row, "Sodium")
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nut_dict["sugarContent"] = get_value_as_string_or_none(row, "Sugar")
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nut_dict = {normalized_k: row[k] for k, normalized_k in nutrition_map.items() if k in row}
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return cleaner.clean_nutrition(nut_dict)
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@ -495,7 +495,7 @@ def clean_nutrition(nutrition: dict | None) -> dict[str, str]:
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list of valid keys
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Assumptionas:
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- All units are supplied in grams, expect sodium which maybe be in milligrams
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- All units are supplied in grams, expect sodium and cholesterol which maybe be in milligrams
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Returns:
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dict[str, str]: If the argument is None, or not a dictionary, an empty dictionary is returned
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@ -509,9 +509,10 @@ def clean_nutrition(nutrition: dict | None) -> dict[str, str]:
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if matched_digits := MATCH_DIGITS.search(val):
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output_nutrition[key] = matched_digits.group(0).replace(",", ".")
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if sodium := nutrition.get("sodiumContent", None):
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if isinstance(sodium, str) and "m" not in sodium and "g" in sodium:
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for key in ["sodiumContent", "cholesterolContent"]:
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if val := nutrition.get(key, None):
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if isinstance(val, str) and "m" not in val and "g" in val:
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with contextlib.suppress(AttributeError, TypeError):
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output_nutrition["sodiumContent"] = str(float(output_nutrition["sodiumContent"]) * 1000)
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output_nutrition[key] = str(float(output_nutrition[key]) * 1000)
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return output_nutrition
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13
tests/fixtures/fixture_users.py
vendored
13
tests/fixtures/fixture_users.py
vendored
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@ -173,8 +173,7 @@ def g2_user(session: Session, admin_token, api_client: TestClient):
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pass
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@fixture(scope="module")
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def unique_user(session: Session, api_client: TestClient):
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def _unique_user(session: Session, api_client: TestClient):
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registration = utils.user_registration_factory()
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response = api_client.post("/api/users/register", json=registration.model_dump(by_alias=True))
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assert response.status_code == 201
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@ -213,6 +212,16 @@ def unique_user(session: Session, api_client: TestClient):
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pass
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@fixture(scope="function")
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def unique_user_fn_scoped(session: Session, api_client: TestClient):
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yield from _unique_user(session, api_client)
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@fixture(scope="module")
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def unique_user(session: Session, api_client: TestClient):
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yield from _unique_user(session, api_client)
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@fixture(scope="module")
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def user_tuple(session: Session, admin_token, api_client: TestClient) -> Generator[list[utils.TestUser], None, None]:
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group_name = utils.random_string()
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@ -1,5 +1,5 @@
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import os
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from dataclasses import dataclass
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from dataclasses import dataclass, field
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from pathlib import Path
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from tempfile import TemporaryDirectory
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from zipfile import ZipFile
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@ -8,6 +8,7 @@ import pytest
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from fastapi.testclient import TestClient
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from mealie.schema.group.group_migration import SupportedMigrations
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from mealie.schema.recipe.recipe import Recipe
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from mealie.schema.reports.reports import ReportEntryOut
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from tests import data as test_data
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from tests.utils import api_routes
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@ -19,18 +20,94 @@ from tests.utils.fixture_schemas import TestUser
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class MigrationTestData:
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typ: SupportedMigrations
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archive: Path
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search_slug: str
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nutrition_filter: set[str] = field(default_factory=set)
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nutrition_entries: set[str] = field(
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default_factory=lambda: {
|
||||
"calories",
|
||||
"carbohydrateContent",
|
||||
"cholesterolContent",
|
||||
"fatContent",
|
||||
"fiberContent",
|
||||
"proteinContent",
|
||||
"saturatedFatContent",
|
||||
"sodiumContent",
|
||||
"sugarContent",
|
||||
"transFatContent",
|
||||
"unsaturatedFatContent",
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
test_cases = [
|
||||
MigrationTestData(typ=SupportedMigrations.nextcloud, archive=test_data.migrations_nextcloud),
|
||||
MigrationTestData(typ=SupportedMigrations.paprika, archive=test_data.migrations_paprika),
|
||||
MigrationTestData(typ=SupportedMigrations.chowdown, archive=test_data.migrations_chowdown),
|
||||
MigrationTestData(typ=SupportedMigrations.copymethat, archive=test_data.migrations_copymethat),
|
||||
MigrationTestData(typ=SupportedMigrations.mealie_alpha, archive=test_data.migrations_mealie),
|
||||
MigrationTestData(typ=SupportedMigrations.tandoor, archive=test_data.migrations_tandoor),
|
||||
MigrationTestData(typ=SupportedMigrations.plantoeat, archive=test_data.migrations_plantoeat),
|
||||
MigrationTestData(typ=SupportedMigrations.myrecipebox, archive=test_data.migrations_myrecipebox),
|
||||
MigrationTestData(typ=SupportedMigrations.recipekeeper, archive=test_data.migrations_recipekeeper),
|
||||
MigrationTestData(
|
||||
typ=SupportedMigrations.nextcloud,
|
||||
archive=test_data.migrations_nextcloud,
|
||||
search_slug="skillet-shepherd-s-pie",
|
||||
nutrition_filter={
|
||||
"transFatContent",
|
||||
"unsaturatedFatContent",
|
||||
},
|
||||
),
|
||||
MigrationTestData(
|
||||
typ=SupportedMigrations.paprika,
|
||||
archive=test_data.migrations_paprika,
|
||||
search_slug="zucchini-kartoffel-frittata",
|
||||
nutrition_entries=set(),
|
||||
),
|
||||
MigrationTestData(
|
||||
typ=SupportedMigrations.chowdown,
|
||||
archive=test_data.migrations_chowdown,
|
||||
search_slug="roasted-okra",
|
||||
nutrition_entries=set(),
|
||||
),
|
||||
MigrationTestData(
|
||||
typ=SupportedMigrations.copymethat,
|
||||
archive=test_data.migrations_copymethat,
|
||||
search_slug="spam-zoodles",
|
||||
nutrition_entries=set(),
|
||||
),
|
||||
MigrationTestData(
|
||||
typ=SupportedMigrations.mealie_alpha,
|
||||
archive=test_data.migrations_mealie,
|
||||
search_slug="old-fashioned-beef-stew",
|
||||
nutrition_filter={
|
||||
"cholesterolContent",
|
||||
"saturatedFatContent",
|
||||
"transFatContent",
|
||||
"unsaturatedFatContent",
|
||||
},
|
||||
),
|
||||
MigrationTestData(
|
||||
typ=SupportedMigrations.tandoor,
|
||||
archive=test_data.migrations_tandoor,
|
||||
search_slug="texas-red-chili",
|
||||
nutrition_entries=set(),
|
||||
),
|
||||
MigrationTestData(
|
||||
typ=SupportedMigrations.plantoeat,
|
||||
archive=test_data.migrations_plantoeat,
|
||||
search_slug="test-recipe",
|
||||
nutrition_filter={
|
||||
"unsaturatedFatContent",
|
||||
"transFatContent",
|
||||
},
|
||||
),
|
||||
MigrationTestData(
|
||||
typ=SupportedMigrations.myrecipebox,
|
||||
archive=test_data.migrations_myrecipebox,
|
||||
search_slug="beef-cheese-piroshki",
|
||||
nutrition_filter={
|
||||
"cholesterolContent",
|
||||
},
|
||||
),
|
||||
MigrationTestData(
|
||||
typ=SupportedMigrations.recipekeeper,
|
||||
archive=test_data.migrations_recipekeeper,
|
||||
search_slug="zucchini-bread",
|
||||
nutrition_entries=set(),
|
||||
),
|
||||
]
|
||||
|
||||
test_ids = [
|
||||
|
@ -47,7 +124,8 @@ test_ids = [
|
|||
|
||||
|
||||
@pytest.mark.parametrize("mig", test_cases, ids=test_ids)
|
||||
def test_recipe_migration(api_client: TestClient, unique_user: TestUser, mig: MigrationTestData) -> None:
|
||||
def test_recipe_migration(api_client: TestClient, unique_user_fn_scoped: TestUser, mig: MigrationTestData) -> None:
|
||||
unique_user = unique_user_fn_scoped
|
||||
payload = {
|
||||
"migration_type": mig.typ.value,
|
||||
}
|
||||
|
@ -91,6 +169,19 @@ def test_recipe_migration(api_client: TestClient, unique_user: TestUser, mig: Mi
|
|||
events = query_data["items"]
|
||||
assert len(events)
|
||||
|
||||
# Validate recipe content
|
||||
response = api_client.get(api_routes.recipes_slug(mig.search_slug), headers=unique_user.token)
|
||||
recipe = Recipe(**assert_deserialize(response))
|
||||
|
||||
if mig.nutrition_entries:
|
||||
assert recipe.nutrition is not None
|
||||
nutrition = recipe.nutrition.model_dump(by_alias=True)
|
||||
|
||||
for k in mig.nutrition_entries.difference(mig.nutrition_filter):
|
||||
assert k in nutrition and nutrition[k] is not None
|
||||
|
||||
# TODO: validate other types of content
|
||||
|
||||
|
||||
def test_bad_mealie_alpha_data_is_ignored(api_client: TestClient, unique_user: TestUser):
|
||||
with TemporaryDirectory() as tmpdir:
|
||||
|
|
|
@ -481,20 +481,24 @@ nutrition_test_cases = (
|
|||
},
|
||||
),
|
||||
CleanerCase(
|
||||
test_id="special support for sodiumContent (g -> mg)",
|
||||
test_id="special support for sodiumContent/cholesterolContent (g -> mg)",
|
||||
input={
|
||||
"cholesterolContent": "10g",
|
||||
"sodiumContent": "10g",
|
||||
},
|
||||
expected={
|
||||
"cholesterolContent": "10000.0",
|
||||
"sodiumContent": "10000.0",
|
||||
},
|
||||
),
|
||||
CleanerCase(
|
||||
test_id="special support for sodiumContent (mg -> mg)",
|
||||
test_id="special support for sodiumContent/cholesterolContent (mg -> mg)",
|
||||
input={
|
||||
"cholesterolContent": "10000mg",
|
||||
"sodiumContent": "10000mg",
|
||||
},
|
||||
expected={
|
||||
"cholesterolContent": "10000",
|
||||
"sodiumContent": "10000",
|
||||
},
|
||||
),
|
||||
|
|
|
@ -23,6 +23,12 @@ async def test_recipe_parser(recipe_test_data: RecipeSiteTestCase):
|
|||
recipe, _ = await scraper.create_from_html(recipe_test_data.url, translator)
|
||||
|
||||
assert recipe.slug == recipe_test_data.expected_slug
|
||||
|
||||
assert len(recipe.recipe_instructions or []) == recipe_test_data.num_steps
|
||||
|
||||
assert len(recipe.recipe_ingredient) == recipe_test_data.num_ingredients
|
||||
|
||||
actual = recipe.nutrition.model_dump() if recipe.nutrition else {}
|
||||
assert recipe_test_data.num_nutrition_entries == len(actual.items())
|
||||
|
||||
assert recipe.org_url == recipe_test_data.url
|
||||
|
|
|
@ -13,6 +13,7 @@ class RecipeSiteTestCase:
|
|||
num_steps: int
|
||||
html_file: Path
|
||||
|
||||
num_nutrition_entries: int = 0
|
||||
include_tags: bool = False
|
||||
expected_tags: set[str] | None = None
|
||||
|
||||
|
@ -26,6 +27,7 @@ def get_recipe_test_cases():
|
|||
expected_slug="taiwanese-three-cup-chicken-san-bei-ji-recipe",
|
||||
num_ingredients=10,
|
||||
num_steps=3,
|
||||
num_nutrition_entries=11,
|
||||
),
|
||||
RecipeSiteTestCase(
|
||||
url="https://www.rezeptwelt.de/backen-herzhaft-rezepte/schinken-kaese-waffeln-ohne-viel-schnickschnack/4j0bkiig-94d4d-106529-cfcd2-is97x2ml",
|
||||
|
|
Loading…
Add table
Add a link
Reference in a new issue