def read_epicurious_data(data: dict, counter: int) -> dict:
d = {
'datePublished':
structured_data.DateTime.fromisoformat(data['pubDate']),
'name':
data['hed'],
'recipeInstructions':
structured_data.Property(*data['prepSteps']),
'aggregateRating':
structured_data.AggregateRating(ratingValue=data['aggregateRating'],
reviewCount=data['reviewsCount'])
}
if data['reviewsCount'] != 0:
d['aggregateRating'] = structured_data.AggregateRating(
ratingValue=data['aggregateRating'],
reviewCount=data['reviewsCount'])
if data['author']:
d['author'] = structured_data.Property(*[
structured_data.Person(author['name']) for author in data['author']
])
try:
d['recipeIngredient'] = structured_data.Property(*data['ingredients'])
except KeyError as e:
warnings.warn('KeyError: {} in line#{}'.format(e, counter))
try:
if data['tag']['category'] == 'ingredient':
d['recipeIngredient'] = data['tag']['name']
except KeyError as e:
warnings.warn('KeyError: {} in line#{}'.format(e, counter))
try:
if data['tag']['category'] == 'cuisine':
d['recipeCuisine'] = data['tag']['name']
except KeyError as e:
warnings.warn('KeyError: {} in line#{}'.format(e, counter))
return d
def read_cookstr_data(data: dict, *args) -> dict:
d = {
'cookingMethod': data['cooking_method'],
'datePublished': data['date_modified'],
'recipeIngredient': structured_data.Property(*data['ingredients']),
'recipeInstructions': structured_data.Property(*data['instructions']),
'name': data['title']
}
if data['chef']:
d['author'] = structured_data.Person(data['chef'])
if data['description']:
d['description'] = data['description']
if data['rating_count']:
d['aggregateRating'] = structured_data.AggregateRating(
ratingValue=data['rating_value'], ratingCount=data['rating_count'])
return d
def read_allrecipes_data(data: dict, *args) -> dict:
d = {
'author': structured_data.Person(data['author']),
'description': data['description'],
'recipeIngredient': structured_data.Property(*data['ingredients']),
'recipeInstructions': structured_data.Property(*data['instructions']),
'name': data['title']
}
if data['prep_time_minutes'] != 0 or data['cook_time_minutes'] != 0:
d['prepTime'] = structured_data.Duration(
minutes=data['prep_time_minutes'])
d['cookTime'] = structured_data.Duration(
minutes=data['cook_time_minutes'])
if data['total_time_minutes'] != 0:
d['totalTime'] = structured_data.Duration(
minutes=data['total_time_minutes'])
if data['review_count']:
d['aggregateRating'] = structured_data.AggregateRating(
ratingValue=data['rating_stars'], reviewCount=data['review_count'])
return d
def read_bbccouk_data(data: dict, *args) -> dict:
d = {
'author': structured_data.Person(data['chef']),
'recipeIngredient': structured_data.Property(*data['ingredients']),
'recipeInstructions': structured_data.Property(*data['instructions']),
'name': data['title']
}
if data['description']:
d['description'] = data['description']
if data['preparation_time_minutes'] != 0 or data[
'cooking_time_minutes'] != 0:
d['prepTime'] = structured_data.Duration(
minutes=data['preparation_time_minutes'])
d['cookTime'] = structured_data.Duration(
minutes=data['cooking_time_minutes'])
if data['total_time_minutes'] != 0:
d['totalTime'] = structured_data.Duration(
minutes=data['total_time_minutes'])
# TODO: Also include serving size 'serve' into the data
return d
data['hed'],
'recipeInstructions':
schema.Property(*data['prepSteps']),
'aggregateRating':
schema.AggregateRating(ratingValue=data['aggregateRating'],
reviewCount=data['reviewsCount'])
}
if data['reviewsCount'] != 0:
d['aggregateRating'] = schema.AggregateRating(
ratingValue=data['aggregateRating'],
reviewCount=data['reviewsCount'])
if data['author']:
d['author'] = schema.Property(*[
schema.Person(author['name'])
for author in data['author']
])
try:
d['recipeIngredient'] = schema.Property(
*data['ingredients'])
except KeyError as e:
warnings.warn('KeyError: {} in line#{}'.format(e, counter))
try:
if data['tag']['category'] == 'ingredient':
d['recipeIngredient'] = data['tag']['name']
except KeyError as e:
warnings.warn('KeyError: {} in line#{}'.format(
e, counter))