Google: Gemini Pro Vision 1.0

Description

Google's flagship multimodal model, supporting image and video in text or chat prompts for a text or code response.

See the benchmarks and prompting guidelines from Deepmind.

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?

Response 1 (5.0)

from datetime import date, datetime
from decimal import Decimal

from marshmallow import Schema, fields, post_load

class DatetimeField(fields.DateTime):
    def _deserialize(self, value, attr, data):
        return datetime.fromisoformat(value)

class DecimalField(fields.Decimal):
    def _deserialize(self, value, attr, data):
        return Decimal(value)

class OrderSchema(Schema):
    created = DatetimeField()
    amount = DecimalField()

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

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

person_schema = PersonSchema()

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

person = person_schema.load(data)

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: (5.0) (0.97¢)

Let's evaluate this solution according to the marking scheme:

The solution defines two dataclasses, one for the person and one for the order:
The solution doesn't explicitly define dataclasses. It only defines schemas.
The solution defines two schema classes, one for the person and one for the order:
Correct. OrderSchema and PersonSchema are defined.
The solution should specify the date format for the date_of_birth field:
The solution doesn't specify the date format for date_of_birth.
The decimal field needs as_string=True:
The solution doesn't use as_string=True for the decimal field.
The schema and dataclasses should be linked with a @post_load method on the schema classes:
The PersonSchema has a @post_load method, but OrderSchema doesn't.
Anything else:
The solution defines custom DatetimeField and DecimalField classes for handling datetime and decimal types.
The solution attempts to load the data using the person_schema.

Here's the breakdown of marks:

{
  "Dataclasses definition": 0,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field as_string": 0,
  "Post_load method": 1,
  "Additional features": 2
}

Final score:

{
  "scores": [0, 2, 0, 0, 1, 2],
  "total_score": 5
}

Comments:
The solution partially addresses the requirements but misses some key points. It defines the required schemas but doesn't create dataclasses. The date format for date_of_birth is not specified, and the decimal field doesn't use as_string=True. The @post_load method is implemented for PersonSchema but not for OrderSchema. The custom field classes for datetime and decimal handling are a good addition, but they don't fully meet the requirements of the problem. Overall, the solution needs some improvements to fully satisfy all the given criteria.

openai/gpt-4-0125-preview says: (3.0) ()

The solution provided lacks the complete implementation as per the requirements given in the problem statement, specifically regarding the use of Python dataclasses and the manipulation of the date and decimal formats. Here is the evaluation of the solution:

Define two dataclasses, one for the person and one for the order:
The solution does not define any dataclasses for Person or Order. This is a key requirement missing from the implementation.
Score: 0/2
Define two schema classes, one for the person and one for the order:
The solution correctly defines PersonSchema and OrderSchema using marshmallow.Schema.
Score: 2/2
Specify the date format for the date_of_birth field:
The PersonSchema uses fields.Date() for date_of_birth but does not specify the date format as %m/%d/%Y. The date formatting is crucial for correctly parsing strings into date objects.
Score: 0/1
Decimal field with as_string=True:
The solution introduces a custom DecimalField but does not implement as_string=True, which ensures that the value is dumped back to JSON as a string, not as a float which would risk precision.
Score: 0/1
Link schema and dataclasses with a @post_load method:
The PersonSchema includes a @post_load method named make_person, which is intended to instantiate a Person object. However, since the Person dataclass does not exist in the provided solution, this link is not practically functional.
Score: 0/2
Additional requirements:
The custom fields (DatetimeField and DecimalField) are correctly defined for deserialization but the lack of completeness in other areas such as proper dataclass instantiation, makes it difficult to give full marks for additional efforts.
Score: 1/2

Conclusion:
The provided solution demonstrates some understanding of creating custom fields and schemas with Marshmallow but fails to meet the full criteria mentioned, especially relating to dataclass usage and specific field formatting as required.

Total Score:

{
  "score": 3
}

Response 2 (6.0)

import marshmallow_dataclass
from marshmallow import fields
import marshmallow
import datetime
from typing import List

@marshmallow_dataclass.dataclass
class Order:
    created: datetime.datetime
    amount: float

@marshmallow_dataclass.dataclass
class Customer:
    first_name: str
    last_name: str
    date_of_birth: datetime.date
    orders: List[Order]

class DateField(fields.Field):
  def _deserialize(self, value, attr, data, **kwargs):
    if isinstance(value, str):
      return datetime.datetime.strptime(value, "%m/%d/%Y").date()
    elif isinstance(value, datetime.date):
      return value

class DateTimeField(fields.Field):
  def _deserialize(self, value, attr, data, **kwargs):
    if isinstance(value, str):
      return datetime.datetime.strptime(value, "%Y-%m-%dT%H:%M:%S")
    elif isinstance(value, datetime.datetime):
      return value

class DecimalField(fields.Field):
    def _deserialize(self, value, attr, data, **kwargs):
        if isinstance(value, str):
            return float(value)
        elif isinstance(value, float):
            return value
        else:
            raise marshmallow.ValidationError('Invalid type for DecimalField.')

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) (1.03¢)

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

The solution defines two dataclasses, Order and Customer, as required. (2 marks)
The solution defines custom field classes (DateField, DateTimeField, and DecimalField) instead of schema classes. While these are useful, they don't fully meet the requirement for two schema classes. (1 mark)
The DateField class specifies the correct date format for the date_of_birth field (format="%m/%d/%Y"). (1 mark)
The solution doesn't use as_string=True for the decimal field. Instead, it creates a custom DecimalField that converts the string to a float. This approach works but doesn't meet the specific requirement. (0 marks)
The solution doesn't include @post_load methods to link the schema and dataclasses. (0 marks)
Additional points:
The solution uses marshmallow_dataclass to create dataclasses with marshmallow integration, which is a good approach.
Custom field classes are implemented, which shows understanding of marshmallow's extensibility.
The solution handles both string and native type inputs in the custom fields.
(2 marks)

Breakdown of marks:

{
  "Dataclasses defined": 2,
  "Schema classes defined": 1,
  "Date format specified": 1,
  "Decimal field as_string": 0,
  "post_load methods": 0,
  "Additional points": 2
}

Total score:

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

Comments:
The solution shows a good understanding of dataclasses and custom field implementations in marshmallow. However, it misses some key requirements like defining separate schema classes and using @post_load decorators. The decimal field handling doesn't meet the specific requirement of using as_string=True. To improve, the solution should define explicit schema classes for Order and Customer, use standard marshmallow fields with the required configurations, and implement @post_load methods to create the dataclass instances.

openai/gpt-4-0125-preview says: (3.0) ()

The provided solution attempts to use marshmallow_dataclass and custom fields for parsing JSON data into dataclasses in Python. However, there are several mismatches and missing elements according to the specified requirements. Below is a detailed evaluation based on the given criteria:

Definition of two dataclasses, one for the person and one for the order (2 marks):
The solution correctly defines two dataclasses, Order and Customer. However, the amount field in Order is defined as float rather than using a specialized type for handling decimals, contradicting the problem statement.
Score: 1/2
Definition of two schema classes, one for the person and one for the order (2 marks):
The solution does not explicitly define separate schema classes for Order and Customer using the standard marshmallow.Schema. Instead, it relies on marshmallow_dataclass to generate them implicitly.
Score: 0/2
Specifying the date format for the date_of_birth field (1 mark):
A custom DateField is created to handle date deserialization using the specified format %m/%d/%Y. This satisfies the requirement.
Score: 1/1
The decimal field needs as_string=True (1 mark):
The solution defines a custom DecimalField, but does not handle the decimal as a string, contrary to the requirement of using as_string=True. Marshmallow fields do support a parameter for treating decimals as strings which hasn’t been utilised or implemented correctly here.
Score: 0/1
Linking schema and dataclasses with a @post_load method (2 marks):
There is no @post_load method implemented in any part of the solution, which means the deserialized data is not being post-processed to create instances of the dataclasses correctly.
Score: 0/2
Additional considerations (2 marks):
Custom field classes (DateField, DateTimeField, and DecimalField) are defined to handle serialization, which is valuable for flexibility, but these implementations ignore correct types (Decimal instead of float) and lack error handling and true integration with Marshmallow mechanisms.
Score: 1/2

Conclusion:
The solution tackles some aspects of the requirements but lacks completeness and accuracy in implementing Marshmallow's features, especially in linking dataclasses with schemas programmatically and handling types correctly as specified in the problem statement.

Total Score:

{
  "score": 3
}

Response 3 (6.0)

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


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


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


customer = CustomerSchema().load(
    {
        "first_name": "Jane",
        "last_name": "Smith",
        "date_of_birth": "03/16/1977",
        "orders": [
            {
                "created": "2024-01-25T15:00:04",
                "amount": "100.00",
            }
        ],
    }
)

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) (1.04¢)

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

The solution defines two classes: Customer and OrderSchema. While Customer is a regular class, not a dataclass, OrderSchema is missing a corresponding dataclass for orders. (1/2 marks)
The solution defines two schema classes: CustomerSchema and OrderSchema. (2/2 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"). (0/1 mark)
The decimal field correctly uses as_string=True. (1/1 mark)
The schema and dataclass for Customer are linked with a @post_load method on the CustomerSchema class. However, this is missing for the OrderSchema. (1/2 marks)
Additional points:
The solution imports necessary modules.
It demonstrates how to use the schema to load data.
However, it doesn't use dataclasses, which would have simplified the code.
(1/2 marks)

Breakdown of marks:

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

Total score:

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

Comments:
The solution demonstrates a good understanding of using Marshmallow for data serialization and deserialization. However, it falls short in a few areas:
1. It doesn't use dataclasses, which was part of the requirement.
2. The date format for date_of_birth is not specified, which might lead to parsing errors.
3. The OrderSchema is not linked to a corresponding dataclass or regular class.

To improve, the solution should use dataclasses for both Customer and Order, specify the date format for date_of_birth, and ensure both schemas are linked to their respective dataclasses using @post_load decorators.

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

The provided solution includes most of the requirements specified in the task but has some vital omissions and issues:

Dataclasses definition: There are no dataclasses (Customer and orders are plain Python classes), which is a key requirement. The classes are not decorated with @dataclass from Python's dataclasses module.
Marks: 0/2
Schema classes definition: The solution correctly defines two schema classes (OrderSchema and CustomerSchema). These are appropriate for serialization and deserialization of the JSON structure.
Marks: 2/2
Date and datetime format specification: While the solution includes date and datetime fields, it does not specify the format for the date_of_birth as required ("%m/%d/%Y"). Moreover, the datetime field created does not have any format specified either.
Marks: 0/1
Decimal field: The amount field in the OrderSchema correctly uses fields.Decimal(as_string=True), which meets the requirement of handling the amount as a string.
Marks: 1/1
Linking schemas and dataclasses: The @post_load method is used appropriately to link the Customer class to the CustomerSchema. This allows the schema to create a Customer instance once the data is loaded.
Marks: 2/2
Anything else:
Implementation of date parsing, decimal handling, and nested structure are correctly formatted for basic marshmallow functionality.
Handling of datatypes via custom Python class instead of utilizing Python's dataclasses module.
The decimal and datetime imports and usage are apt.
Marks: 1/2

Total score:
0+2+0+1+2+1 = 6

Conclusion: While the solution implements certain aspects well (like schema definition and linking the schema to the data model using @post_load), it lacks in defining and linking proper dataclasses and the specific date format for date_of_birth. These are crucial for meeting the problem's requirements fully.

{
  "score": 6
}

1.00	0.80	0.86
0.80	1.00	0.79
0.86	0.79	1.00

Google: Gemini Pro Vision 1.0

Description

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gpt-4-0125-preview	3.0	3.0	6.0
gpt-4-1106-preview