Pydantic to HTML Converter

Common errors

• AttributeError: 'BaseModel' object has no attribute 'dict'

  cause This error occurs when using Pydantic V2 or newer models with methods deprecated in V1, such as `.dict()` or `.json()`.
  fix Replace `.dict()` with `.model_dump()` and `.json()` with `.model_dump_json()` for Pydantic V2 models.

• pydantic. ValidationError: 1 validation error for [YourModelName] [field name] value is not a valid integer (type=type_error.integer)

  cause Input data for a field specified as an integer (e.g., `age: int`) contains a non-integer value that Pydantic cannot coerce, such as a string that doesn't represent a number, or a boolean.
  fix Ensure that the data provided for integer fields is either an actual integer or a string that can be successfully converted to an integer by Pydantic. Review the input data for the indicated field and correct its type or value.

• pydantic. ValidationError: 1 validation error for [YourModelName] [field name] field required

  cause A required field in your Pydantic model was not provided in the input data, or was explicitly set to `None` without being marked as `Optional` or having a default value.
  fix Provide a value for the required field. If the field should be optional, define it using `Optional[Type]` (e.g., `field: Optional[str]`) or provide a default value (e.g., `field: str = 'default_value'`).

Warnings

• breaking Pydantic V1 to V2 migration introduces significant breaking changes that affect `pydantic-to-html`'s underlying Pydantic models. Key method renames (e.g., `.dict()` to `.model_dump()`, `.json()` to `.model_dump_json()`) and configuration changes (e.g., `Config` class to `model_config` dictionary) are common pitfalls.
  Fix: Ensure your Pydantic models adhere to Pydantic V2 syntax. Use `model_dump()` for dictionary representation, `model_dump_json()` for JSON, and migrate `Config` classes to `model_config` attributes. Consider using the `bump-pydantic` tool for automated migration assistance.
• gotcha Pydantic's default behavior for type coercion can sometimes lead to unexpected data conversions (e.g., a string '10' being coerced to an integer 10). While convenient, it can mask underlying data issues if not explicitly handled or if strict validation is desired.
  Fix: For stricter validation, consider using Pydantic's `strict` mode (`model_config = {'strict': True}`) or specific Pydantic types like `StrictStr`, `StrictInt`. Always validate input data against your model's expectations.
• gotcha Debugging `ValidationError` exceptions from Pydantic models can be challenging due to their detailed, nested structure. Misinterpreting the `loc`, `msg`, and `type` fields in the error details can lead to incorrect fixes.
  Fix: When catching `ValidationError`, iterate through `exc.errors()` to inspect each individual error. Pay close attention to the `loc` field to pinpoint the exact problematic field in your model. Pydantic's documentation on error handling provides detailed explanations of error types and structures.

Install

• pip install pydantic-to-html Install pydantic-to-html

Imports

• render_html

  from pydantic_to_html import render_html

• BaseModel

  from pydantic import BaseModel

Quickstart

This quickstart demonstrates how to define a Pydantic model (including nested models), instantiate it with data, and then convert it into an editable HTML form using `render_html`. The `theme` parameter is used to apply basic styling (e.g., 'bootstrap').

from pydantic import BaseModel
from pydantic_to_html import render_html

class Address(BaseModel):
    street: str
    city: str
    zip_code: str

class User(BaseModel):
    name: str
    email: str
    age: int
    is_active: bool = True
    address: Address

user_data = User(
    name="Jane Doe",
    email="jane.doe@example.com",
    age=29,
    address=Address(street="123 Main St", city="Anytown", zip_code="12345")
)

html_output = render_html(user_data, editable=True, theme="bootstrap")
print(html_output)