Google: Gemini Flash 1.5

Description

Gemini 1.5 Flash is a foundation model that performs well at a variety of multimodal tasks such as visual understanding, classification, summarization, and creating content from image, audio and video. It's adept at processing visual and text inputs such as photographs, documents, infographics, and screenshots.

Gemini 1.5 Flash is designed for high-volume, high-frequency tasks where cost and latency matter. On most common tasks, Flash achieves comparable quality to other Gemini Pro models at a significantly reduced cost. Flash is well-suited for applications like chat assistants and on-demand content generation where speed and scale matter.

Usage of Gemini is subject to Google's Gemini Terms of Use.

#multimodal

Prompt

I need to take this json:

{
'first_name': 'Jane',
'last_name': 'Smith',
'date_of_birth': '03/16/1977',
orders: [
{
'created': '2024-01-25T15:00:04',
'amount': '100.00'
}
]}

And generate some dataclasses.

date_of_birth needs to be a date.
created is a datetime.
amount is a decimal.

How would I do this using the marshmallow library in python?

from marshmallow import Schema, fields, post_load
from datetime import datetime, date
from decimal import Decimal

class OrderSchema(Schema):
    created = fields.DateTime()
    amount = fields.Decimal()

class UserSchema(Schema):
    first_name = fields.String()
    last_name = fields.String()
    date_of_birth = fields.Date()
    orders = fields.List(fields.Nested(OrderSchema))

    @post_load
    def make_user(self, data, **kwargs):
        return User(**data)

class User:
    def __init__(self, first_name, last_name, date_of_birth, orders):
        self.first_name = first_name
        self.last_name = last_name
        self.date_of_birth = date_of_birth
        self.orders = orders

# Example usage
json_data = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977',
    'orders': [
        {
            'created': '2024-01-25T15:00:04',
            'amount': '100.00'
        }
    ]
}

user_schema = UserSchema()
user = user_schema.load(json_data)

print(user.first_name)  # Output: Jane
print(user.date_of_birth)  # Output: 1977-03-16
print(user.orders[0].created)  # Output: 2024-01-25 15:00:04
print(user.orders[0].amount)  # Output: 100.00

Explanation:

  1. Import necessary libraries:
    • marshmallow for schema definition.
    • datetime and date for handling dates.
    • Decimal for handling decimal values.
  2. Define OrderSchema:
    • created: Uses fields.DateTime() to parse the 'created' string into a datetime object.
    • amount: Uses fields.Decimal() to parse the 'amount' string into a Decimal object.
  3. Define UserSchema:
    • first_name and last_name: Use fields.String() for simple string parsing.
    • date_of_birth: Uses fields.Date() to parse the 'date_of_birth' string into a date object.
    • orders: Uses fields.List with fields.Nested(OrderSchema) to handle the list of orders, each parsed using the OrderSchema.
    • @post_load: This decorator defines a method that is called after all fields are loaded. It takes the loaded data and returns a User instance, ensuring type consistency and proper object creation.
  4. Define User class:
    • This class is a simple model to store the user data with the appropriate types.
  5. Example Usage:
    • The code demonstrates loading the JSON data using user_schema.load(json_data).
    • It then prints various attributes of the loaded User object, showcasing that the data has been correctly parsed and converted to the desired types.

This approach leverages Marshmallow's powerful features for schema definition and data validation. It ensures that your JSON data is properly parsed and converted to the required Python types, making it easier to work with in your application.

Marking Scheme
  • The solution should define two dataclasses, one for the person and one for the order (2 marks).
  • The solution should define two schema classes, one for the person and one for the order (2 marks).
  • The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark)
  • The decimal field needs as_string=True. (1 mark)
  • The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks)
  • Anything else: (2 marks)

anthropic/claude-3.5-sonnet says: (6.0) (1.18¢)

I'll evaluate the solution based on the provided marking scheme:

  1. The solution defines two classes: User and OrderSchema. The User class acts as a dataclass for the person, but OrderSchema is not a dataclass for the order. (1 mark out of 2)

  2. The solution correctly defines two schema classes: UserSchema and OrderSchema. (2 marks)

  3. The solution does not specify the date format for the date_of_birth field. It should have used fields.Date(format="%m/%d/%Y"). (0 marks out of 1)

  4. The decimal field does not include as_string=True. It should have been fields.Decimal(as_string=True). (0 marks out of 1)

  5. The schema and dataclasses are linked with a @post_load method on the UserSchema class. However, there's no @post_load method for the OrderSchema. (1 mark out of 2)

  6. Additional points:

  7. The solution imports necessary libraries.
  8. It provides an example usage of the schema.
  9. It demonstrates how to access the parsed data.
    (2 marks)

Breakdown of marks:

{
  "Dataclass definition": 1,
  "Schema class definition": 2,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "Post_load method": 1,
  "Additional points": 2
}

Score:

{
  "scores": [1, 2, 0, 0, 1, 2],
  "total_score": 6
}

Comments:
The solution demonstrates a good understanding of using Marshmallow for data parsing and validation. However, it misses some important details:

  1. It should have used dataclasses for both User and Order.
  2. The date format for date_of_birth needs to be specified.
  3. The amount field in OrderSchema should have as_string=True.
  4. An @post_load method should be added to OrderSchema to create Order objects.

These improvements would make the solution more robust and fully compliant with the requirements.

openai/gpt-4-0125-preview says: (4.5) ()

The submitted solution achieves several goals set out in the problem but misses some specific details from the task description. Here's an evaluation based on the provided marking scheme:

  1. Dataclass Definition:
  2. The solution defines a class User but does not use the dataclasses module, hence it is not a dataclass.
  3. The Order class is missing entirely.

  4. Score: 0/2

  5. Schema Classes:

  6. OrderSchema and UserSchema are correctly defined.

  7. Score: 2/2

  8. Date and Decimal Formats:

  9. date_of_birth is processed using fields.Date() but does not specify the format ("%m/%d/%Y"), which is critical for correct string to date conversion.
  10. amount is parsed as a Decimal but doesn't have as_string=True to ensure it remains a string when serialized.

  11. Score: 0/2

  12. Linking Schema with Dataclasses (@post_load Decorator):

  13. The make_user function with @post_load exists in the UserSchema, correctly creating an instance of the User class but since it isn't a dataclass, this submission misses full marks.
  14. There is no similar linking function for the Order schema since the corresponding dataclass is missing.

  15. Score: 1/2

  16. Other Considerations:

  17. Error handling, customization of fields beyond the basic type, or additional validation logic are absent in the solution which are areas that could improve the robustness of the solution.
  18. The use of actual dataclasses would be beneficial for clarity and Pythonic best practices.
  19. The examples provided demonstrate usage correctly, showing understanding of how to use the schemas to parse and utilize JSON data.
  20. Score: 1.5/2

Overall, the solution demonstrates a basic understanding of using marshmallow schemas to serialize and deserialize data but misses out on some specific requirements of the task. Enhancements for format specification, using Python dataclasses, and handling all requirements could greatly improve this solution.

Final score:

{
  "score": 4.5
}

from datetime import date, datetime
from decimal import Decimal

from marshmallow import Schema, fields, post_load

class OrderSchema(Schema):
    created = fields.DateTime()
    amount = fields.Decimal()

class CustomerSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()
    orders = fields.Nested(OrderSchema, many=True)

    @post_load
    def make_customer(self, data, **kwargs):
        return Customer(**data)

class Customer:
    def __init__(self, first_name, last_name, date_of_birth, orders):
        self.first_name = first_name
        self.last_name = last_name
        self.date_of_birth = date_of_birth
        self.orders = orders

# Load the JSON data
json_data = {
  'first_name': 'Jane',
  'last_name': 'Smith',
  'date_of_birth': '03/16/1977',
  'orders': [
    {
      'created': '2024-01-25T15:00:04',
      'amount': '100.00'
    }
  ]
}

# Deserialize the JSON into dataclasses
customer_schema = CustomerSchema()
customer = customer_schema.load(json_data)

# Print the customer details
print(f"Customer: {customer.first_name} {customer.last_name}")
print(f"Date of Birth: {customer.date_of_birth}")
print("Orders:")
for order in customer.orders:
    print(f"  Created: {order.created}")
    print(f"  Amount: {order.amount}")

Explanation:

  1. Import necessary libraries:
  2. datetime: For handling date and datetime objects.
  3. decimal: For representing decimal values accurately.

  4. marshmallow: The library for data serialization and deserialization.

  5. Define nested schema for Order:

  6. OrderSchema uses fields.DateTime for created and fields.Decimal for amount to correctly handle the types.

  7. Define top-level schema for Customer:

  8. CustomerSchema uses fields.Str for text fields, fields.Date for date_of_birth, and fields.Nested to include the OrderSchema for the orders list.

  9. @post_load decorator:

  10. This decorator is used to create a Customer instance from the deserialized data. It takes the data dictionary and uses it to initialize a new Customer object.

  11. Define Customer dataclass:

  12. This holds the customer data with appropriate types for each field.

  13. Load JSON data:

  14. json_data holds the JSON data you provided.

  15. Deserialize JSON into dataclasses:

  16. Create an instance of CustomerSchema.

  17. Use customer_schema.load(json_data) to deserialize the JSON data and create a Customer object.

  18. Print customer details:

  19. Access and print the attributes of customer, including its orders.

Output:

Customer: Jane Smith
Date of Birth: 1977-03-16
Orders:
  Created: 2024-01-25 15:00:04
  Amount: 100.00

This code demonstrates how to use Marshmallow to deserialize JSON data into dataclasses with proper type conversions. The @post_load decorator allows you to customize the data transformation process, making it flexible and easy to adapt to different scenarios.

Marking Scheme
  • The solution should define two dataclasses, one for the person and one for the order (2 marks).
  • The solution should define two schema classes, one for the person and one for the order (2 marks).
  • The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark)
  • The decimal field needs as_string=True. (1 mark)
  • The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks)
  • Anything else: (2 marks)

openai/gpt-4-0125-preview says: (7.0) ()

Evaluation:

  1. Dataclasses Definition:
  2. The solution did not use Python's dataclasses module to define data classes, but rather defined the Customer object using a manual __init__ method, which does not meet the full expectations of using dataclasses. However, the intent to create a class to hold data is met.

  3. Marks: 1/2

  4. Schema Classes Definition:

  5. Two appropriate schema classes OrderSchema and CustomerSchema are correctly defined using the marshmallow library.

  6. Marks: 2/2

  7. Date Format Specification:

  8. The solution does not specify the date format ("%m/%d/%Y") for parsing the date_of_birth field as required.

  9. Marks: 0/1

  10. Decimal Field as_string=True:

  11. The decimal field configuration with as_string=True is missing, which is necessary to retain the decimal amount as a string during serialization.

  12. Marks: 0/1

  13. Linking Schema and Dataclasses with @post_load:

  14. A @post_load method is correctly implemented in the CustomerSchema to convert loaded data into a Customer instance, which effectively links the schema to the data structure.

  15. Marks: 2/2

  16. Additional Aspects:

  17. The integrity of the serialized and deserialized processes, including proper handling of nested schemas and data conversions, has been maintained.
  18. Points for handling and loading JSON data correctly through schemas are well done.
  19. Marks: 2/2

Total Score:

{
  "score": 7
}

anthropic/claude-3.5-sonnet says: (7.0) (1.23¢)

I'll evaluate the solution based on the provided marking scheme:

  1. The solution defines two classes: Customer and OrderSchema. While Customer is a regular class, not a dataclass, OrderSchema serves as a schema for orders. (1/2 marks)

  2. The solution defines two schema classes: CustomerSchema and OrderSchema. (2/2 marks)

  3. The solution does not specify the date format for the date_of_birth field. It should have used fields.Date(format="%m/%d/%Y"). (0/1 mark)

  4. The decimal field (amount) is not specified with as_string=True. It should have been fields.Decimal(as_string=True). (0/1 mark)

  5. The schema and dataclasses are linked with a @post_load method on the CustomerSchema class. (2/2 marks)

  6. Additional points:

  7. The solution imports necessary libraries.
  8. It handles nested structures correctly.
  9. It demonstrates how to use the schema to deserialize data.
  10. It includes a print section to show the deserialized data.
    (2/2 marks)

Breakdown of marks:

{
  "Dataclasses definition": 1,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "Schema and dataclasses linking": 2,
  "Additional points": 2
}

Score:

{
  "scores": [1, 2, 0, 0, 2, 2],
  "total_score": 7
}

Comments:
The solution demonstrates a good understanding of using Marshmallow for JSON deserialization. It correctly defines schema classes and links them to the data structure. However, it misses some important details:

  1. It uses a regular class instead of a dataclass for Customer.
  2. It doesn't specify the correct date format for date_of_birth.
  3. It doesn't use as_string=True for the decimal field.

These omissions could lead to issues with parsing the provided JSON data correctly. Despite these shortcomings, the overall structure and approach are sound, showing a good grasp of the Marshmallow library's usage for JSON deserialization.

from marshmallow import Schema, fields, post_load
from datetime import datetime, date
from decimal import Decimal

class OrderSchema(Schema):
    created = fields.DateTime()
    amount = fields.Decimal()

class UserSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()
    orders = fields.Nested(OrderSchema, many=True)

    @post_load
    def make_user(self, data, **kwargs):
        return User(**data)

class User:
    def __init__(self, first_name, last_name, date_of_birth, orders):
        self.first_name = first_name
        self.last_name = last_name
        self.date_of_birth = date_of_birth
        self.orders = orders

# Load the JSON data
json_data = {
  'first_name': 'Jane',
  'last_name': 'Smith',
  'date_of_birth': '03/16/1977',
  'orders': [
    {
      'created': '2024-01-25T15:00:04',
      'amount': '100.00'
    }
  ]
}

# Deserialize the JSON data into a User object
user_schema = UserSchema()
user = user_schema.load(json_data)

# Print the user data
print(f"First name: {user.first_name}")
print(f"Last name: {user.last_name}")
print(f"Date of birth: {user.date_of_birth}")
print(f"Orders:")
for order in user.orders:
    print(f"- Created: {order.created}")
    print(f"- Amount: {order.amount}")

Explanation:

  1. Import necessary libraries: Import the Schema, fields, post_load from marshmallow, datetime for date and datetime objects, and Decimal for handling decimal numbers.

  2. Define OrderSchema:

  3. Create a Schema called OrderSchema to represent the structure of an order.

  4. Define the fields:

    • created: Use fields.DateTime to deserialize the string to a datetime object.
    • amount: Use fields.Decimal to deserialize the string to a Decimal object.

  5. Define UserSchema:

  6. Create a Schema called UserSchema to represent the structure of the user.
  7. Define the fields:
    • first_name: Use fields.Str for a string field.
    • last_name: Use fields.Str for a string field.
    • date_of_birth: Use fields.Date to deserialize the string to a date object.
    • orders: Use fields.Nested to represent a list of nested orders, specifying the OrderSchema for each order.

  8. Add a post_load method to the UserSchema to create a User object after deserialization.

  9. Define User Class:

  10. Create the User class to represent a user object with the corresponding attributes.

  11. Load the JSON data:

  12. Store the JSON data in the json_data variable.

  13. Deserialize the JSON data:

  14. Create an instance of UserSchema.

  15. Use user_schema.load(json_data) to deserialize the JSON data into a User object.

  16. Print the user data:

  17. Access the user data using the attributes of the user object and print them in a formatted way.
Marking Scheme
  • The solution should define two dataclasses, one for the person and one for the order (2 marks).
  • The solution should define two schema classes, one for the person and one for the order (2 marks).
  • The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark)
  • The decimal field needs as_string=True. (1 mark)
  • The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks)
  • Anything else: (2 marks)

anthropic/claude-3.5-sonnet says: (6.0) (1.10¢)

I'll evaluate the solution based on the provided marking scheme:

  1. The solution defines two classes: User and OrderSchema. The User class is a regular class, not a dataclass. The OrderSchema is not converted to a dataclass either. (1/2 marks)

  2. The solution defines two schema classes: UserSchema and OrderSchema. (2/2 marks)

  3. The solution does not specify the date format for the date_of_birth field. It should have used fields.Date(format="%m/%d/%Y"). (0/1 mark)

  4. The decimal field (amount) does not have as_string=True specified. (0/1 mark)

  5. The schema and classes are linked with a @post_load method on the UserSchema class. However, there's no @post_load method for the OrderSchema. (1/2 marks)

  6. Additional points:

  7. The solution imports necessary libraries.
  8. It provides a complete example of loading and printing data.
  9. The OrderSchema is properly nested within the UserSchema.
    (2/2 marks)

Here's the breakdown of marks:

{
  "Dataclasses": 1,
  "Schema classes": 2,
  "Date format": 0,
  "Decimal field": 0,
  "Post-load methods": 1,
  "Additional points": 2
}

Final score:

{
  "scores": [1, 2, 0, 0, 1, 2],
  "total_score": 6
}

The solution demonstrates a good understanding of using marshmallow for serialization and deserialization. However, it misses some key requirements such as using dataclasses, specifying the correct date format, and setting as_string=True for the decimal field. With some modifications, this solution could fully meet the requirements of the problem.

openai/gpt-4-0125-preview says: (6.0) ()

The solution presented attempts to define and deserialize JSON data using Marshmallow schemas and Python data classes. The overall implementation aligns with many requirements outlined in the problem statement. However, there are some concerning areas where the solution doesn't precisely meet the specified criteria. Let's analyze the solution based on the marking scheme:

  1. Definition of two dataclasses, one for the person and one for the order (2 marks):
  2. The solution clearly defines a User class, but no distinct Order class is defined. The handling of Order data is implicitly managed through dictionaries within the schema and post-loading process.

  3. Score: 1/2

  4. Two schema classes are defined, one for the person and one for the order (2 marks):

  5. Both UserSchema and OrderSchema are properly defined in the solution.

  6. Score: 2/2

  7. Specifying date format for the date_of_death field (format="%m/%d/%Y") (1 mark):

  8. The date_format specifier is missing in the date_of_birth fields definition. Thus, it relies on default Marshmallow parsing which might not strictly adhere to the given format.

  9. Score: 0/1

  10. Decimal field specification with as_string=True (1 mark):

  11. The amount field in OrderSchema should have been defined with the parameter as_string=True to serialize it as a string, as per the requirements. This is missing in the schema definition.

  12. Score: 0/1

  13. Linking schema and dataclasses with a @post_load method on the schema classes (2 marks):

  14. The @post_load decorator is accurately used in UserSchema to create a User instance from the loaded data.

  15. Score: 2/2

  16. Additional aspects (2 marks):

  17. Proper usage of Marshmallow's fields for type casting (Str, Date, Decimal, Nested).
  18. Adequate implementation of JSON loading, object creation, and accessible printing of attributes.
  19. However, due to missing aspects in specifying required field conversion parameters and the absence of an explicit Order class, not all JSON data features are extensively validated or correctly formatted.
  20. Score: 1/2

Overall, the solution specifies schemas and handles the deserialization well but misses certain formatting and specification requirements leading to potential data misrepresentation. Therefore, considering the missing pieces and partial adherence to the specifications:

{
  "score": 6
}
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