OpenAI: GPT-4 Vision

Description

Ability to understand images, in addition to all other GPT-4 Turbo capabilties. Training data: up to Apr 2023.

Note: heavily rate limited by OpenAI while in preview.

#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?

Response 1 (9.0)

First, you need to install the marshmallow library by running pip install marshmallow.

Next, you need to import required modules and define your data classes. Here is an example of how you could do that:

from dataclasses import dataclass
from datetime import datetime, date
from decimal import Decimal
from marshmallow import Schema, fields, post_load, EXCLUDE

@dataclass
class Order:
    created: datetime
    amount: Decimal

@dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: list[Order]

class OrderSchema(Schema):
    created = fields.DateTime(required=True)
    amount = fields.Decimal(as_string=True, required=True)

    @post_load
    def make_order(self, data, **kwargs):
        return Order(**data)

class PersonSchema(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = fields.Date(required=True)
    orders = fields.List(fields.Nested(OrderSchema), required=True)

    class Meta:
        unknown = EXCLUDE

    @post_load
    def make_person(self, data, **kwargs):
        return Person(**data)

# 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'
    }
  ]
}

person_schema = PersonSchema()
person = person_schema.load(json_data)
print(person)

This will convert the JSON data into instances of the Person and Order data classes, with the date_of_birth field as a date object, created as a datetime object, and amount as a Decimal. The EXCLUDE option in Meta class will make sure that if there are unknown fields in the input data, marshmallow will exclude them in the output.

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: (9.0) (1.01¢)

Let's evaluate this solution based on the provided marking scheme:

The solution correctly defines two dataclasses, Person and Order. (2 marks)
The solution correctly defines two schema classes, PersonSchema and OrderSchema. (2 marks)
The solution does not specify the date format for the date_of_birth field. It should have been fields.Date(format="%m/%d/%Y", required=True). (0 marks)
The decimal field correctly uses as_string=True. (1 mark)
The schema and dataclasses are correctly linked with @post_load methods on both schema classes. (2 marks)
Additional points:
The solution correctly imports necessary modules.
It handles unknown fields with unknown = EXCLUDE in the PersonSchema.
It provides a usage example.
(2 marks)

Break down of marks:

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

Overall score:

{
  "scores": [2, 2, 0, 1, 2, 2],
  "total_score": 9
}

Comments:
The solution is generally well-structured and covers most of the requirements. It correctly defines the dataclasses and schema classes, links them appropriately, and handles the decimal field correctly. The additional features like handling unknown fields and providing a usage example are good touches.

The main issue is the lack of date format specification for the date_of_birth field, which is important for parsing the given date string correctly. This oversight could lead to errors when trying to parse the date in the given format.

Despite this oversight, the solution demonstrates a good understanding of using marshmallow with dataclasses and would work well for most of the given JSON structure.

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

The provided solution effectively uses Python's dataclass along with the marshmallow library to serialize and deserialize the specified JSON data into custom Python objects. The solution performs the following correctly:

Defines two data classes, Order and Person, to store order details and personal information, respectively. (2 marks)
Defines two schema classes, OrderSchema and PersonSchema, which are crucial in serializing and deserializing JSON data according to the specification of the custom data classes. (2 marks)
The Decimal field in OrderSchema is declared with as_string=True to ensure that decimal values are correctly handled as string representations for precision. (1 mark)
Implements the @post_load decorator in both schemas to construct data class instances from deserialized data. (2 marks)

However, there are areas where the solution is lacking:

The specification of the date and datetime formats is not directly controlled by the fields.DateTime and fields.Date. Although marshmallow's defaults can handle these common formats implicitly, best practice would involve explicitly specifying the date format for date_of_birth using format="%m/%d/%Y".
Although the general requirement is met for handling dates and decimals, the explicit date format was not specified for better clarity and certainty in code behavior, which would make the solution robust against potential unexpected input formats.

As a result, given that explicit format instructions were detailed in the problem but not adhered to in the provided solution, there will be a dock in marks.

Final Score:

{
  "score": 7
}

Response 2 (9.0)

To generate dataclasses using the marshmallow library in Python, you'll first need to install the library by running pip install marshmallow and pip install marshmallow-dataclass. Then, you can define your dataclasses and create marshmallow schemas with the appropriate field types to handle the serialization and deserialization of the JSON data. Here's an example of how to do this:

from datetime import datetime, date
from decimal import Decimal
from dataclasses import dataclass
from marshmallow import Schema, fields, post_load
from marshmallow.validate import Range
from marshmallow_dataclass import class_schema

# Define your dataclasses
@dataclass
class Order:
    created: datetime
    amount: Decimal

@dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: list[Order]

# Create marshmallow schemas for the dataclasses
class OrderSchema(Schema):
    created = fields.DateTime(required=True)
    amount = fields.Decimal(places=2, as_string=True, validate=Range(min_value=Decimal('0.01')))

    @post_load
    def make_order(self, data, **kwargs):
        return Order(**data)

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

    @post_load
    def make_person(self, data, **kwargs):
        return Person(**data)

# Create instances of the schemas
order_schema = OrderSchema()
person_schema = PersonSchema()

# Example 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 JSON to Python objects
person = person_schema.load(json_data)
print(person)

# Serialize Python objects back to JSON
person_json = person_schema.dump(person)
print(person_json)

Make sure to validate the data according to your requirements and convert the amount to a Decimal and date_of_birth and created to date and datetime objects, respectively.

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: (9.0) (0.93¢)

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

The solution defines two dataclasses, Order and Person. (2 marks)
The solution defines two schema classes, OrderSchema and PersonSchema. (2 marks)
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)
The decimal field correctly uses as_string=True. (1 mark)
The schema and dataclasses are linked with @post_load methods on both schema classes. (2 marks)
Additional points:
The solution imports necessary modules.
It includes validation for the amount field using Range.
It provides an example of how to use the schemas for serialization and deserialization.
(2 marks)

Breakdown of marks:

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

Total score:

{
  "scores": [2, 2, 0, 1, 2, 2],
  "total_score": 9
}

The solution is very good overall, addressing most of the requirements effectively. The main issue is the lack of date format specification for the date_of_birth field. Otherwise, it demonstrates a solid understanding of using marshmallow with dataclasses, including proper typing, validation, and serialization/deserialization examples.

openai/gpt-4-0125-preview says: (8.5) ()

This submitted solution demonstrates how to utilize marshmallow and dataclasses in Python to deserialize and serialize JSON data. Let's analyze the solution based on the marking scheme:

Definition of two dataclasses, one for the person and one for the order:
The solution defines two dataclasses (Order and Person) using the @dataclass decorator, fulfilling this requirement. (2 marks)
Definition of two schema classes, one for the person and one for the order:
OrderSchema and PersonSchema are defined appropriately within the solution, thus meeting this criterion. (2 marks)
Specifying the date format for the date_of_birth field (format="%m/%d/%Y"):
The solution does not specify the date format for the date_of_birth field in PersonSchema. This is an issue as fields.Date() without formatting might lead to incorrect date parsing or errors. (0 marks)
Decimal field with as_string=True:
The solution correctly specifies the amount field in OrderSchema to be a Decimal with as_string=True. This is correct and meets the specified requirement. (1 mark)
Linking schemas and dataclasses with a @post_load method on the schema classes:
The solution includes @post_load methods (make_order and make_person) within both schema classes, correctly constructing Order and Person class instances from the loaded data. (2 marks)
Additional aspects:
Proper use of importing required libraries.
Correct handling of nested serialization and deserialization.
Addition of field validation (e.g., the use of Range in OrderSchema for the amount).
Given the clear and functional implementation, and despite lacking the date_of_birth format specification but including additional validation which was not necessarily required by the problem, only a minor deduction applies. (1.5 marks)

In conclusion, the solution correctly implements most of the specified requirements and demonstrates a solid understanding of using dataclasses with marshmallow but misses specifying the required date format.

Final score:

{
  "score": 8.5
}

Response 3 (9.0)

To generate dataclasses using the marshmallow library in Python, you need to define a schema for each dataclass that you want to create. Here's how you can do it for the JSON provided:

First, install marshmallow if you haven't already:

pip install marshmallow

Then, you can create your dataclasses and Schemas as follows:

from dataclasses import dataclass
from datetime import datetime, date
from decimal import Decimal
from marshmallow import Schema, fields, post_load, validates, ValidationError

# Create the dataclasses

@dataclass
class Order:
    created: datetime
    amount: Decimal

@dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: list[Order]

# Create the Schemas

class OrderSchema(Schema):
    created = fields.DateTime(format='iso', required=True)
    amount = fields.Decimal(as_string=True, required=True)

    @post_load
    def make_order(self, data, **kwargs):
        return Order(**data)

class PersonSchema(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = fields.Date(required=True)
    orders = fields.List(fields.Nested(OrderSchema))

    @post_load
    def make_person(self, data, **kwargs):
        return Person(**data)

    @validates('date_of_birth')
    def validate_dob(self, value):
        if value > date.today():
            raise ValidationError("Date of birth cannot be in the future.")

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

person_schema = PersonSchema()
person = person_schema.load(json_data)
print(person)

This code sets up a Person class and an Order class as dataclasses, along with corresponding Schemas PersonSchema and OrderSchema. Marshmallow's fields module is used to define the type of each field and how it should be deserialized.

The @post_load decorator is a marshmallow hook that allows you to instantiate the dataclass with the deserialized data.

Finally, we deserialize the json_data into a Person dataclass instance with all the data properly typed (string to str, ISO date to date, ISO datetime to datetime, and string to Decimal). If an incorrect date format is entered for date_of_birth, it will raise a validation error due to the custom validate_dob method.

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: (9.0) (0.99¢)

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

The solution defines two dataclasses, Person and Order. (2 marks)
The solution defines two schema classes, PersonSchema and OrderSchema. (2 marks)
The solution does not specify the correct date format for the date_of_birth field. It should be format="%m/%d/%Y", but this is missing. (0 marks)
The decimal field (amount) correctly uses as_string=True. (1 mark)
The schema and dataclasses are linked with @post_load methods on both schema classes. (2 marks)
Additional points:
The solution includes proper imports.
It uses type hints in the dataclasses.
It includes a custom validation for the date_of_birth field.
It provides an example of how to use the schema to deserialize data.
(2 marks)

Breakdown of marks:

{
  "Dataclasses defined": 2,
  "Schema classes defined": 2,
  "Date format specification": 0,
  "Decimal field configuration": 1,
  "Schema and dataclass linking": 2,
  "Additional features": 2
}

Score:

{
  "scores": [2, 2, 0, 1, 2, 2],
  "total_score": 9
}

The solution is very good overall, addressing most of the requirements effectively. The main issue is the missing date format specification for the date_of_birth field, which should have been fields.Date(format="%m/%d/%Y", required=True). Otherwise, the solution demonstrates a good understanding of marshmallow and dataclasses in Python.

openai/gpt-4-0125-preview says: (9.0) ()

The provided solution has a number of aspects that align with the requirements specified in the problem statement:

Dataclasses Definition (2 marks)
The solution properly defines two dataclasses: Order and Person.
Schema Classes Definition (2 marks)
Two corresponding schema classes, OrderSchema and PersonSchema, are correctly defined.
Date Format Specification (0.5 marks)
The requirement specifies that the date_of_birth should utilize the format "%m/%d/%Y", but in the solution, there is no explicit format provided for this field within PersonSchema. Instead, it defaults to ISO format implicitly, which might not be incorrect, but lacks explicitness for the given data's formatting ("03/16/1977" is not ISO format).
Decimal Field (1 mark)
The solution correctly sets as_string=True for the amount field in OrderSchema.
Linking Schemas and Classes with @post_load (2 marks)
The @post_load methods are implemented correctly in both schema classes to create instances of the dataclasses from the deserialized data.
Additional Critiques and Considerations (1.5 marks)
The solution includes a validate_dob method to ensure that the date_of_birth is not in the future, adding robustness to the data handling.
However, the date validation does not address the explicit format mismatch that might occur given the example JSON. It should ensure that the date is parsed correctly based on the specific format in the JSON data.

Overall Score Calculation:
- Dataclasses Definition: 2/2
- Schema Classes Definition: 2/2
- Date Format Specification: 0.5/1
- Decimal Field: 1/1
- Linking Schemas and Classes with @post_load: 2/2
- Additional Critiques and Considerations: 1.5/2

Total: 9/10

{
  "score": 9
}

1.00	0.90	0.91
0.90	1.00	0.95
0.91	0.95	1.00

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