Add python-version of the dynamodb model. Implement test-case to veri…

…fy that everything is parsed properly without validation issues.

alexa-skill/lambda-pdm/pyproject.toml

@@ -19,6 +19,7 @@ license = {text = "MIT"}
 lint = "ruff check --fix ."
 format = "ruff format ."
 typecheck = "mypy ./src/"
+test = "pytest tests/"
 
 [tool.pdm.dev-dependencies]
 lint = [
@@ -31,6 +32,9 @@ typecheck = [
 i18n = [
     "babel>=2.15.0",
 ]
+test = [
+    "pytest>=8.2.0",
+]
 
 [tool.ruff.lint]
 select = [
@@ -52,4 +56,3 @@ convention = "pep257"
 
 [tool.pdm]
 distribution = false
-

alexa-skill/lambda-pdm/pytest.ini

@@ -0,0 +1,2 @@
+[pytest]
+pythonpath = "src"

alexa-skill/lambda-pdm/src/config.py

@@ -4,6 +4,7 @@
 
 class DynamoDBConfig(BaseModel):
     """A configuration class for the DynamoDB table connection."""
+
     table_name: str
     region: str
     assume_role_arn: str
@@ -12,6 +13,7 @@ class DynamoDBConfig(BaseModel):
 
 class Config(BaseSettings):
     """The main configuration class for the skill."""
+
     model_config = SettingsConfigDict(env_nested_delimiter='__', env_file='.env', env_file_encoding='utf-8')
     dynamodb_config: DynamoDBConfig
 

alexa-skill/lambda-pdm/src/data/model.py

@@ -0,0 +1,56 @@
+import datetime
+from enum import Enum
+from typing import List, Optional, Set
+
+from pydantic import BaseModel, Field, field_validator
+
+
+class NutritionFlag(str, Enum):
+    """An enumeration of the possible nutrition flags."""
+
+    VEGETARIAN = "vegetarian"
+    VEGAN = "vegan"
+    PORK = "pork"
+    BEEF = "beef"
+    FISH = "fish"
+    CHICKEN = "chicken"
+
+
+class MensaMenuExtra(BaseModel):
+    """A model for the extra menu items."""
+
+    name: str = Field(alias='Name')
+    description: str = Field(alias='Description')
+
+
+class MensaMenu(BaseModel):
+    """A model for the menu items."""
+
+    name: str = Field(alias='Name')
+    contents: List[str] = Field(alias='Contents')
+    price: Optional[str] = Field(alias='Price', default=None)
+    nutrition_flags: Set[NutritionFlag] = Field(alias='NutritionFlags', default_factory=set)
+
+    @field_validator('nutrition_flags', mode='before')
+    def parse_nutrition_flags(cls, value):
+        """Parse the nutrition flags from the expected input format to a set of NutritionFlag enums.
+
+        Sample input value: {
+             "NutritionFlags": {
+                "vegetarian": {}
+             },
+        }
+        """
+        if isinstance(value, dict):
+            return {NutritionFlag(flag) for flag in value.keys()}
+        return value
+
+
+class MensaDayMenus(BaseModel):
+    """A model for the dynamodb mensa menu item."""
+
+    date: datetime.date = Field(alias='Date')
+    menus: List[MensaMenu] = Field(alias='Menus')
+    extras: List[MensaMenuExtra] = Field(alias='Extras')
+    mensa_id: str = Field(alias='MensaId')
+    LanguageKey: str = Field(alias='LanguageKey')

alexa-skill/lambda-pdm/tests/data/example_model.json

@@ -0,0 +1,139 @@
+{
+  "MensaId": "mensa-academica",
+  "Date": "2024-05-15",
+  "Menus": [
+    {
+      "NutritionFlags": {
+        "fish": {}
+      },
+      "Contents": [
+        "Potato creme soup",
+        "Smoked salmon",
+        "Bread roll"
+      ],
+      "Price": "2,00 €",
+      "Name": "Stew"
+    },
+    {
+      "NutritionFlags": {
+        "vegetarian": {}
+      },
+      "Contents": [
+        "Potato creme soup",
+        "Bread roll"
+      ],
+      "Price": "1,60 €",
+      "Name": "Vegetarian table dish"
+    },
+    {
+      "NutritionFlags": {
+        "vegetarian": {},
+        "vegan": {}
+      },
+      "Contents": [
+        "Crispy sesame-carrot-stick",
+        "Sweet sour sauce"
+      ],
+      "Price": "2,20 €",
+      "Name": "Vegetarian"
+    },
+    {
+      "NutritionFlags": {
+        "pork": {}
+      },
+      "Contents": [
+        "Asparagus with ham",
+        "White wine hollandaise",
+        "Potatoes"
+      ],
+      "Price": "6,50 €",
+      "Name": "Suggestion of the day"
+    },
+    {
+      "NutritionFlags": {
+        "beef": {}
+      },
+      "Contents": [
+        "Beef strips",
+        "Mushrooms"
+      ],
+      "Price": "2,80 €",
+      "Name": "Classics"
+    },
+    {
+      "NutritionFlags": {
+        "chicken": {}
+      },
+      "Contents": [
+        "Sweet n spicy chicken",
+        "Banana peanut sauce",
+        "Basmati rice"
+      ],
+      "Price": "3,80 €",
+      "Name": "Wok"
+    },
+    {
+      "NutritionFlags": {
+        "vegetarian": {},
+        "vegan": {}
+      },
+      "Contents": [
+        "Sweet-spicy vegetables",
+        "Banana peanut sauce",
+        "Basmati rice"
+      ],
+      "Price": "3,80 €",
+      "Name": "Wok"
+    },
+    {
+      "NutritionFlags": {
+        "beef": {}
+      },
+      "Contents": [
+        "Cheeseburger",
+        "French fries",
+        "Soft drink 0,33 L"
+      ],
+      "Price": "4,90 €",
+      "Name": "Burger Classics"
+    },
+    {
+      "NutritionFlags": {
+        "vegetarian": {}
+      },
+      "Contents": [
+        "Veggie burger",
+        "French fries",
+        "Soft drink 0,33 L"
+      ],
+      "Price": "4,90 €",
+      "Name": "Burger Classics"
+    },
+    {
+      "NutritionFlags": {
+        "beef": {}
+      },
+      "Contents": [
+        "Italian burger",
+        "Rocket, tomatoes",
+        "cheese sauce",
+        "French fries",
+        "Soft drink 0,33 L"
+      ],
+      "Price": "4,90 €",
+      "Name": "Burger of the week"
+    }
+  ],
+  "Extras": [
+    {
+      "Name": "Main side-dish",
+      "Description": "potatoes or rice"
+    },
+    {
+      "Name": "secondary",
+      "Description": "Romanesco or Mixed salad"
+    }
+  ],
+  "LanguageKey": "en",
+  "MensaIdLanguageKeyDate": "mensa-academica;en;2024-05-15"
+}

alexa-skill/lambda-pdm/tests/data/test_model_parsing.py

@@ -0,0 +1,16 @@
+import json
+from pathlib import Path
+
+from data.model import MensaDayMenus
+
+
+def test_model_parsing():
+    """Test the parsing of the model from a json file."""
+    model_path = Path(__file__).parent / 'example_model.json'
+    assert model_path.exists()
+    assert model_path.is_file()
+
+    with open(model_path, 'r') as f:
+        model = json.load(f)
+
+    MensaDayMenus(**model)