fh-pydantic-form

fh-pydantic-form

Generate HTML forms from Pydantic models for your FastHTML applications.

fh-pydantic-form simplifies creating web forms for FastHTML by automatically generating the necessary HTML input elements based on your Pydantic model definitions. It integrates seamlessly with and leverages MonsterUI components for styling. This makes it ideal for annotation workflows for structured outputs: when the schema updates, so does your annotation app.

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Table of Contents

• Purpose
• Installation
• Quick Start
• Key Features
• Spacing & Styling
• Working with Lists
• Nested Models
• Literal & Enum Fields
• Initial Values & Enum Parsing
• Disabling & Excluding Fields
• Refreshing & Resetting
• Label Colors
• Metrics & Highlighting
• ComparisonForm
• Schema Drift Resilience
• Custom Renderers
• API Reference
• Contributing

Purpose

• Reduce Boilerplate: Automatically render form inputs (text, number, checkbox, select, date, time, etc.) based on Pydantic field types and annotations.
• Data Validation: Leverage Pydantic's validation rules directly from form submissions.
• Nested Structures: Support for nested Pydantic models and lists of models/simple types with accordion UI.
• Dynamic Lists: Built-in HTMX endpoints and JavaScript for adding, deleting, and reordering items in lists within the form.
• Customization: Easily register custom renderers for specific Pydantic types or fields.
• Robust Schema Handling: Gracefully handles model changes and missing fields in initial data.

Installation

You can install fh-pydantic-form using either pip or uv.

Using pip:

pip install fh-pydantic-form

Using uv:

uv add fh-pydantic-form

This will also install necessary dependencies like pydantic, python-fasthtml, and monsterui.

Quick Start

# examples/simple_example.py
import fasthtml.common as fh
import monsterui.all as mui
from pydantic import BaseModel, ValidationError

# 1. Import the form renderer
from fh_pydantic_form import PydanticForm

app, rt = fh.fast_app(
    hdrs=[
        mui.Theme.blue.headers(),
        # Add list_manipulation_js() if using list fields
        # from fh_pydantic_form import list_manipulation_js
        # list_manipulation_js(),
    ],
    pico=False, # Using MonsterUI, not PicoCSS
    live=True,  # Enable live reload for development
)

# 2. Define your Pydantic model
class SimpleModel(BaseModel):
    """Model representing a simple form"""
    name: str = "Default Name"
    age: int
    is_active: bool = True

# 3. Create a form renderer instance
#    - 'my_form': Unique name for the form (used for prefixes and routes)
#    - SimpleModel: The Pydantic model class
form_renderer = PydanticForm("my_form", SimpleModel)

# (Optional) Register list manipulation routes if your model has List fields
# form_renderer.register_routes(app)

# 4. Define routes
@rt("/")
def get():
    """Display the form"""
    return fh.Div(
        mui.Container(
            mui.Card(
                mui.CardHeader("Simple Pydantic Form"),
                mui.CardBody(
                    # Use MonsterUI Form component for structure
                    mui.Form(
                        # Render the inputs using the renderer
                        form_renderer.render_inputs(),
                        # Add standard form buttons
                        fh.Div(
                            mui.Button("Submit", type="submit", cls=mui.ButtonT.primary),
                            form_renderer.refresh_button("🔄"),
                            form_renderer.reset_button("↩️"),
                            cls="mt-4 flex items-center gap-2",
                        ),
                        # HTMX attributes for form submission
                        hx_post="/submit_form",
                        hx_target="#result", # Target div for response
                        hx_swap="innerHTML",
                        # Set a unique ID for the form itself for refresh/reset inclusion
                        id=f"{form_renderer.name}-form",
                    )
                ),
            ),
            # Div to display validation results
            fh.Div(id="result"),
        ),
    )

@rt("/submit_form")
async def post_submit_form(req):
    """Handle form submission and validation"""
    try:
        # 5. Validate the request data against the model
        validated_data: SimpleModel = await form_renderer.model_validate_request(req)

        # Success: Display the validated data
        return mui.Card(
            mui.CardHeader(fh.H3("Validation Successful")),
            mui.CardBody(
                fh.Pre(
                    validated_data.model_dump_json(indent=2),
                )
            ),
            cls="mt-4",
        )
    except ValidationError as e:
        # Validation Error: Display the errors
        return mui.Card(
            mui.CardHeader(fh.H3("Validation Error", cls="text-red-500")),
            mui.CardBody(
                fh.Pre(
                    e.json(indent=2),
                )
            ),
            cls="mt-4",
        )

if __name__ == "__main__":
    fh.serve()

Key Features

• Automatic Field Rendering: Handles str, int, float, bool, date, time, Optional, Literal, nested BaseModels, and Lists out-of-the-box.
• Sensible Defaults: Uses appropriate HTML5 input types (text, number, date, time, checkbox, select).
• Labels & Placeholders: Generates labels from field names (converting snake_case to Title Case) and basic placeholders.
• Descriptions as Tooltips: Uses Field(description=...) from Pydantic to create tooltips (uk-tooltip via UIkit).
• Required Fields: Automatically adds the required attribute based on field definitions (considering Optional and defaults).
• Disabled Fields: Disable the whole form with disabled=True or disable specific fields with disabled_fields
• Collapsible Nested Models: Renders nested Pydantic models in accordion-style components for better form organization and space management.
• List Manipulation:
  • Renders lists of simple types or models in accordion-style cards with an enhanced UI.
  • Provides HTMX endpoints (registered via register_routes) for adding and deleting list items.
  • Includes JavaScript (list_manipulation_js()) for client-side reordering (moving items up/down).
  • Click list field labels to toggle all items open/closed.
• Form Refresh & Reset:
  • Provides HTMX-powered "Refresh" and "Reset" buttons (form_renderer.refresh_button(), form_renderer.reset_button()).
  • Refresh updates list item summaries or other dynamic parts without full page reload.
  • Reset reverts the form to its initial values.
• Custom Renderers: Register your own BaseFieldRenderer subclasses for specific Pydantic types or complex field logic using FieldRendererRegistry or by passing custom_renderers during PydanticForm initialization.
• Form Data Parsing: Includes logic (form_renderer.parse and form_renderer.model_validate_request) to correctly parse submitted form data (handling prefixes, list indices, nested structures, boolean checkboxes, etc.) back into a dictionary suitable for Pydantic validation.

Spacing & Styling

fh-pydantic-form ships with two spacing presets to fit different UI requirements:

Theme	Purpose	Usage
normal (default)	Comfortable margins & borders – great for desktop forms	PydanticForm(..., spacing="normal")
compact	Ultra-dense UIs, mobile layouts, or forms with many fields	PydanticForm(..., spacing="compact")

# Example: side-by-side normal vs compact forms
form_normal = PydanticForm("normal_form", MyModel, spacing="normal")
form_compact = PydanticForm("compact_form", MyModel, spacing="compact")

Important: The compact CSS is now scoped with .fhpf-compact classes and only affects form inputs, not layout containers. This prevents conflicts with your application's layout system.

Working with Lists

When your Pydantic models contain List[str], List[int], or List[BaseModel] fields, fh-pydantic-form provides rich list manipulation capabilities:

Basic Setup

from fh_pydantic_form import PydanticForm, list_manipulation_js
from typing import List

app, rt = fh.fast_app(
    hdrs=[
        mui.Theme.blue.headers(),
        list_manipulation_js(),  # Required for list manipulation
    ],
    pico=False,
    live=True,
)

class ListModel(BaseModel):
    name: str = ""
    tags: List[str] = Field(["tag1", "tag2"])
    addresses: List[Address] = Field(default_factory=list)

form_renderer = PydanticForm("list_model", ListModel)
form_renderer.register_routes(app)  # Register HTMX endpoints

List Features

• Add Items: Each list has an "Add Item" button that creates new entries
• Delete Items: Each list item has a delete button with confirmation
• Reorder Items: Move items up/down with arrow buttons
• Toggle All: Click the list field label to expand/collapse all items at once
• Refresh Display: Use the 🔄 icon next to list labels to update item summaries
• Smart Defaults: New items are created with sensible default values

The list manipulation uses HTMX for seamless updates without page reloads, and includes JavaScript for client-side reordering.

Nested Models

Nested Pydantic models are automatically rendered in collapsible accordion components:

class Address(BaseModel):
    street: str = "123 Main St"
    city: str = "Anytown"
    is_billing: bool = False

class User(BaseModel):
    name: str
    address: Address  # Rendered as collapsible accordion
    backup_addresses: List[Address]  # List of accordions

Key behaviors:

• Nested models inherit disabled and spacing settings from the parent form
• Field prefixes are automatically managed (e.g., user_address_street)
• Accordions are open by default for better user experience
• Schema drift is handled gracefully - missing fields use defaults, unknown fields are ignored

Literal & Enum Fields

fh-pydantic-form provides comprehensive support for choice-based fields through Literal, Enum, and IntEnum types, all automatically rendered as dropdown selects:

Literal Fields

from typing import Literal, Optional

class OrderModel(BaseModel):
    # Required Literal field - only defined choices available
    shipping_method: Literal["STANDARD", "EXPRESS", "OVERNIGHT"] = "STANDARD"
    
    # Optional Literal field - includes "-- None --" option
    category: Optional[Literal["ELECTRONICS", "CLOTHING", "BOOKS", "OTHER"]] = None

Enum Fields

from enum import Enum, IntEnum

class OrderStatus(Enum):
    """Order status enum with string values."""
    PENDING = "pending"
    CONFIRMED = "confirmed" 
    SHIPPED = "shipped"
    DELIVERED = "delivered"
    CANCELLED = "cancelled"

class Priority(IntEnum):
    """Priority levels using IntEnum for numeric ordering."""
    LOW = 1
    MEDIUM = 2
    HIGH = 3
    URGENT = 4

class OrderModel(BaseModel):
    # Required Enum field with default
    status: OrderStatus = OrderStatus.PENDING
    
    # Optional Enum field without default
    payment_method: Optional[PaymentMethod] = None
    
    # Required IntEnum field with default
    priority: Priority = Priority.MEDIUM
    
    # Optional IntEnum field without default
    urgency_level: Optional[Priority] = Field(
        None, description="Override priority for urgent orders"
    )
    
    # Enum field without default (required)
    fulfillment_status: OrderStatus = Field(
        ..., description="Current fulfillment status"
    )

Field Rendering Behavior

Field Type	Required	Optional	Notes
Literal	Shows only defined choices	Includes "-- None --" option	String values displayed as-is
Enum	Shows enum member values	Includes "-- None --" option	Displays enum.value in dropdown
IntEnum	Shows integer values	Includes "-- None --" option	Maintains numeric ordering

Key features:

• Automatic dropdown generation for all choice-based field types
• Proper value handling - enum values are correctly parsed during form submission
• Optional field support - includes None option when fields are Optional
• Field descriptions become tooltips on hover
• Default value selection - dropdowns pre-select the appropriate default value

Initial Values & Enum Parsing

fh-pydantic-form intelligently parses initial values from dictionaries, properly converting strings and integers to their corresponding enum types:

Setting Initial Values

# Example initial values from a dictionary
initial_values_dict = {
    "shipping_method": "EXPRESS",      # Literal value as string
    "category": "ELECTRONICS",         # Optional Literal value
    "status": "shipped",               # Enum value (parsed to OrderStatus.SHIPPED)
    "payment_method": "paypal",        # Optional Enum (parsed to PaymentMethod.PAYPAL)
    "priority": 3,                     # IntEnum as integer (parsed to Priority.HIGH)
    "urgency_level": 4,                # Optional IntEnum as integer (parsed to Priority.URGENT)
    "fulfillment_status": "confirmed"  # Required Enum (parsed to OrderStatus.CONFIRMED)
}

# Create form with initial values
form_renderer = PydanticForm("order_form", OrderModel, initial_values=initial_values_dict)

Parsing Behavior

The form automatically handles conversion between different value formats:

Input Type	Target Type	Example	Result
String	Enum	"shipped"	OrderStatus.SHIPPED
String	Optional[Enum]	"paypal"	PaymentMethod.PAYPAL
Integer	IntEnum	3	Priority.HIGH
Integer	Optional[IntEnum]	4	Priority.URGENT
String	Literal	"EXPRESS"	"EXPRESS" (unchanged)

Benefits:

• Flexible data sources - works with database records, API responses, or any dictionary
• Type safety - ensures enum values are valid during parsing
• Graceful handling - invalid enum values are passed through for Pydantic validation
• Consistent behavior - same parsing logic for required and optional fields

Example Usage

@rt("/")
def get():
    return mui.Form(
        form_renderer.render_inputs(),  # Pre-populated with parsed enum values
        fh.Div(
            mui.Button("Submit", type="submit", cls=mui.ButtonT.primary),
            form_renderer.refresh_button("🔄"),
            form_renderer.reset_button("↩️"),  # Resets to initial parsed values
            cls="mt-4 flex items-center gap-2",
        ),
        hx_post="/submit_order",
        hx_target="#result",
        id=f"{form_renderer.name}-form",
    )

@rt("/submit_order")
async def post_submit_order(req):
    try:
        # Validates and converts form data back to proper enum types
        validated_order: OrderModel = await form_renderer.model_validate_request(req)
        
        # Access enum properties
        print(f"Status: {validated_order.status.value} ({validated_order.status.name})")
        print(f"Priority: {validated_order.priority.value} ({validated_order.priority.name})")
        
        return success_response(validated_order)
    except ValidationError as e:
        return error_response(e)

This makes it easy to work with enum-based forms when loading data from databases, APIs, or configuration files.

Disabling & Excluding Fields

Disabling Fields

You can disable the entire form or specific fields:

# Disable all fields
form_renderer = PydanticForm("my_form", FormModel, disabled=True)

# Disable specific fields only
form_renderer = PydanticForm(
    "my_form",
    FormModel,
    disabled_fields=["field1", "field3"]
)

Excluding Fields

Exclude specific fields from being rendered in the form:

form_renderer = PydanticForm(
    "my_form",
    FormModel,
    exclude_fields=["internal_field", "computed_field"]
)

Important: When fields are excluded from the UI, fh-pydantic-form automatically injects their default values during form parsing and validation. This ensures:

• Hidden fields with defaults are still included in the final validated data
• Required fields without defaults will still cause validation errors if not provided elsewhere
• Default factories are executed to provide computed default values
• Nested BaseModel defaults are converted to dictionaries for consistency

This automatic default injection means you can safely exclude fields that shouldn't be user-editable while maintaining data integrity.

Refreshing & Resetting

Forms support dynamic refresh and reset functionality:

mui.Form(
    form_renderer.render_inputs(),
    fh.Div(
        mui.Button("Submit", type="submit", cls=mui.ButtonT.primary),
        form_renderer.refresh_button("🔄 Refresh"),  # Update display
        form_renderer.reset_button("↩️ Reset"),      # Restore initial values
        cls="mt-4 flex items-center gap-2",
    ),
    # ... rest of form setup
)

• Refresh button updates the form display based on current values (useful for updating list item summaries)
• Reset button restores all fields to their initial values with confirmation
• Both use HTMX for seamless updates without page reloads

Label Colors

Customize the appearance of field labels with the label_colors parameter:

form_renderer = PydanticForm(
    "my_form",
    MyModel,
    label_colors={
        "name": "text-blue-600",    # Tailwind CSS class
        "score": "#E12D39",        # Hex color value
        "status": "text-green-500", # Another Tailwind class
    },
)

Supported formats:

• Tailwind CSS classes: "text-blue-600", "text-red-500", etc.
• Hex color values: "#FF0000", "#0066CC", etc.
• CSS color names: "red", "blue", "darkgreen", etc.

This can be useful for e.g. highlighting the values of different fields in a pdf with different highlighting colors matching the form input label color.

Metrics & Highlighting

fh-pydantic-form provides a powerful metrics system for visual highlighting of form fields based on extraction quality scores and confidence assessments. This is particularly useful for evaluating LLM structured output extraction, comparing generated data against ground truth, and building quality assessment interfaces.

Basic Metrics Usage

from fh_pydantic_form import PydanticForm

# Define metrics for your form fields
metrics_dict = {
    "title": {
        "metric": 0.95,
        "comment": "Excellent title quality - clear and engaging"
    },
    "rating": {
        "metric": 0.3,
        "comment": "Low rating needs attention"
    },
    "status": {
        "metric": 0.0,
        "comment": "Critical status issue - requires immediate review"
    }
}

# Create form with metrics
form_renderer = PydanticForm(
    "my_form",
    MyModel,
    metrics_dict=metrics_dict
)

Metrics Dictionary Structure

Each field can have the following metrics properties:

Property	Type	Description
metric	float or str	Numeric score (0.0-1.0) or string assessment
color	str	Custom color (overrides automatic color-coding)
comment	str	Tooltip text shown on hover

Automatic Color Coding

Numeric metrics are automatically color-coded:

• 1.0: Bright green (perfect score)
• 0.5-1.0: Medium green (good range)
• 0.0-0.5: Dark red (needs attention)
• 0.0: Bright red (critical issue)

metrics_dict = {
    "field1": {"metric": 1.0, "comment": "Perfect!"},      # Bright green
    "field2": {"metric": 0.8, "comment": "Very good"},     # Medium green
    "field3": {"metric": 0.3, "comment": "Needs work"},    # Dark red
    "field4": {"metric": 0.0, "comment": "Critical"},      # Bright red
}

Custom Colors

Override automatic colors with custom values:

metrics_dict = {
    "status": {
        "metric": 0.0,
        "color": "purple",  # Custom color overrides red
        "comment": "Status requires special attention"
    },
    "priority": {
        "metric": 1.0,
        "color": "#FF6B35",  # Custom hex color
        "comment": "High priority with custom highlight"
    }
}

String Metrics

Use string values for qualitative assessments:

metrics_dict = {
    "validation_status": {
        "metric": "NEEDS_REVIEW",
        "color": "#F59E0B",  # Amber color
        "comment": "Requires human review"
    },
    "data_quality": {
        "metric": "EXCELLENT",
        "color": "#10B981",  # Green color
        "comment": "Data quality exceeds standards"
    }
}

Nested Field Metrics

Support for nested objects and list items:

metrics_dict = {
    # Nested object fields
    "author.name": {
        "metric": 0.95,
        "comment": "Author name perfectly formatted"
    },
    "author.email": {
        "metric": 0.9,
        "comment": "Email format excellent"
    },
    
    # List item metrics
    "tags[0]": {
        "metric": 1.0,
        "comment": "First tag is perfect"
    },
    "tags[1]": {
        "metric": 0.8,
        "comment": "Second tag very good"
    },
    
    # Complex nested paths
    "author.addresses[0].street": {
        "metric": 1.0,
        "comment": "Street address perfectly formatted"
    },
    "author.addresses[1].city": {
        "metric": 0.1,
        "color": "teal",
        "comment": "City has verification problems"
    }
}

Practical Use Cases

LLM Structured Output Evaluation:

# Evaluate LLM extraction quality against ground truth
extraction_metrics = {
    "product.name": {
        "metric": 0.9,
        "comment": "Name extracted with minor formatting issue: missing space"
    },
    "product.category": {
        "metric": 0.0,
        "comment": "Critical error: LLM misclassified Electronics instead of Sports"
    },
    "key_features": {
        "metric": 0.6,
        "comment": "LLM missed 2 of 5 key features from source text"
    },
    "extraction_confidence": {
        "metric": 1.0,
        "comment": "LLM confidence score accurately reflects actual performance"
    }
}

Document Processing Quality:

# Highlight extraction quality from documents
doc_extraction_metrics = {
    "invoice_number": {
        "metric": 1.0,
        "comment": "Invoice number perfectly extracted from PDF"
    },
    "line_items": {
        "metric": 0.75,
        "comment": "3/4 line items extracted correctly"
    },
    "total_amount": {
        "metric": 0.0,
        "comment": "Amount extraction failed - currency symbol confusion"
    }
}

See examples/metrics_example.py for a comprehensive demonstration of all metrics features.

ComparisonForm

The ComparisonForm component provides side-by-side comparison of two related forms, perfect for evaluating LLM structured output against ground truth, annotation correction workflows, and comparing extraction results.

Basic Usage

from fh_pydantic_form import PydanticForm, ComparisonForm

# Create two forms to compare
left_form = PydanticForm(
    "ground_truth",
    ProductModel,
    initial_values=annotated_ground_truth,
    disabled=False  # Editable for annotation correction
)

right_form = PydanticForm(
    "llm_output", 
    ProductModel,
    initial_values=llm_extracted_data,
    disabled=True,  # Read-only LLM output
    metrics_dict=extraction_quality_metrics
)

# Create comparison form
comparison_form = ComparisonForm(
    name="extraction_evaluation",
    left_form=left_form,
    right_form=right_form,
    left_label="📝 Ground Truth (Editable)",
    right_label="🤖 LLM Output (with Quality Scores)"
)

Required JavaScript

Include the comparison form JavaScript in your app headers:

from fh_pydantic_form import comparison_form_js

app, rt = fh.fast_app(
    hdrs=[
        mui.Theme.blue.headers(),
        comparison_form_js(),  # Required for comparison forms
    ],
    pico=False,
    live=True,
)

Complete Example

@rt("/")
def get():
    return fh.Div(
        mui.Container(
            mui.Card(
                mui.CardHeader(
                    fh.H1("LLM Extraction Evaluation")
                ),
                mui.CardBody(
                    # Render the comparison form
                    comparison_form.form_wrapper(
                        fh.Div(
                            comparison_form.render_inputs(),
                            
                            # Action buttons
                            fh.Div(
                                mui.Button(
                                    "Update Ground Truth",
                                    type="submit",
                                    hx_post="/update_ground_truth",
                                    hx_target="#result"
                                ),
                                comparison_form.left_reset_button("Reset Left"),
                                comparison_form.left_refresh_button("Refresh Left"),
                                cls="mt-4 flex gap-2"
                            ),
                            
                            fh.Div(id="result", cls="mt-4")
                        )
                    )
                )
            )
        )
    )

@rt("/update_ground_truth")
async def post_update_ground_truth(req):
    # Validate left form (ground truth side)
    validated = await comparison_form.left_form.model_validate_request(req)
    
    # Process the ground truth update
    return success_response(validated)

# Register routes for both forms
comparison_form.register_routes(app)

Key Features

• Aligned fields input fields are horizontally aligned for easy comparison.
• Synchronized Accordions: Expanding/collapsing sections syncs between both forms
• Independent Controls: Separate refresh and reset buttons for each side
• Metrics Integration: Right side typically shows LLM output quality scores
• Flexible Layout: Responsive design works on desktop and mobile
• Form Validation: Standard validation works with either form

Common Patterns

LLM Output Evaluation:

# Left: Editable ground truth
# Right: Read-only LLM output with extraction quality metrics
truth_form = PydanticForm(..., disabled=False, metrics_dict={})
llm_form = PydanticForm(..., disabled=True, metrics_dict=extraction_metrics)

Document Extraction Comparison:

# Left: Manual annotation
# Right: Automated LLM extraction
manual_form = PydanticForm(..., initial_values=manual_annotation)
auto_form = PydanticForm(..., initial_values=llm_extraction, metrics_dict=quality_scores)

Annotation Correction Workflow:

# Left: Correctable ground truth
# Right: LLM output with confidence scores
ground_truth_form = PydanticForm(..., disabled=False)
llm_output_form = PydanticForm(..., disabled=True, metrics_dict=confidence_scores)

See examples/comparison_example.py for a complete LLM extraction evaluation interface demonstration.

Setting Initial Values

You can set initial form values of the form by passing a model instance or dictionary:

initial_data = MyModel(name="John", tags=["happy", "joy"])
form_renderer = PydanticForm("my_form", MyModel, initial_values=initial_data)


initial_data_dict = {"name": "John"} 
form_renderer = PydanticForm("my_form", MyModel, initial_values=initial_values_dict)

The dictionary does not have to be complete, and we try to handle schema drift gracefully. If you exclude fields from the form, we fill those fields with the initial_values or the default values.

Reusing Form Configuration with Different Values

The with_initial_values() method allows you to create a new form instance with the same configuration but different initial values:

# Create a base form configuration
base_form = PydanticForm(
    "product_form",
    ProductModel,
    disabled_fields=["id"],
    label_colors={"name": "text-blue-600", "price": "text-green-600"},
    spacing="compact"
)

# Create forms with different initial values using the same configuration
form_for_product_a = base_form.with_initial_values({"name": "Product A", "price": 29.99})
form_for_product_b = base_form.with_initial_values({"name": "Product B", "price": 45.50})

# Or with model instances
existing_product = ProductModel(name="Existing Product", price=19.99)
form_for_existing = base_form.with_initial_values(existing_product)

This is particularly useful for:

• Editing workflows where you need the same form configuration for different records
• Template forms where you want to reuse styling and field configurations
• Bulk operations where you process multiple items with the same form structure

Schema Drift Resilience

fh-pydantic-form gracefully handles model evolution and schema changes:

Initial values can come from older or newer versions of your model – unknown fields are ignored gracefully and missing fields use defaults.

# Your model evolves over time
class UserModel(BaseModel):
    name: str
    email: str           # Added in v2
    phone: Optional[str] # Added in v3

# Old data still works
old_data = {"name": "John"}  # Missing newer fields
form = PydanticForm("user", UserModel, initial_values=old_data)

# Newer data works too
new_data = {"name": "Jane", "email": "[e-mail]", "phone": "555-1234", "removed_field": "ignored"}
form = PydanticForm("user", UserModel, initial_values=new_data)

Benefits:

• Backward compatibility: Old data structures continue to work
• Forward compatibility: Unknown fields are silently ignored
• Graceful degradation: Missing fields fall back to model defaults
• Production stability: No crashes during rolling deployments

Custom Renderers

The library is extensible through custom field renderers for specialized input types:

from fh_pydantic_form.field_renderers import BaseFieldRenderer
from fh_pydantic_form import FieldRendererRegistry

class CustomDetail(BaseModel):
    value: str = "Default value"
    confidence: Literal["HIGH", "MEDIUM", "LOW"] = "MEDIUM"

    def __str__(self) -> str:
        return f"{self.value} ({self.confidence})"

class CustomDetailFieldRenderer(BaseFieldRenderer):
    """Display value input and dropdown side by side"""

    def render_input(self):
        value_input = fh.Div(
            mui.Input(
                value=self.value.get("value", ""),
                id=f"{self.field_name}_value",
                name=f"{self.field_name}_value",
                placeholder=f"Enter {self.original_field_name.replace('_', ' ')} value",
                cls="uk-input w-full",  
            ),
            cls="flex-grow",
        )

        confidence_options = [
            fh.Option(
                opt, value=opt, selected=(opt == self.value.get("confidence", "MEDIUM"))
            )
            for opt in ["HIGH", "MEDIUM", "LOW"]
        ]

        confidence_select = mui.Select(
            *confidence_options,
            id=f"{self.field_name}_confidence",
            name=f"{self.field_name}_confidence",
            cls_wrapper="w-[110px] min-w-[110px] flex-shrink-0",
        )

        return fh.Div(
            value_input,
            confidence_select,
            cls="flex items-start gap-2 w-full",
        )

# Register the custom renderer (multiple ways)
FieldRendererRegistry.register_type_renderer(CustomDetail, CustomDetailFieldRenderer)

# Or pass directly to PydanticForm
form_renderer = PydanticForm(
    "my_form",
    MyModel,
    custom_renderers=[(CustomDetail, CustomDetailFieldRenderer)],
)

Registration Methods

• Type-based: register_type_renderer(CustomDetail, CustomDetailFieldRenderer)
• Type name: register_type_name_renderer("CustomDetail", CustomDetailFieldRenderer)
• Predicate: register_type_renderer_with_predicate(lambda field: isinstance(field.annotation, CustomDetail), CustomDetailFieldRenderer)

API Reference

PydanticForm Constructor

Parameter	Type	Default	Description
form_name	str	Required	Unique identifier for the form (used for HTMX routes and prefixes)
model_class	Type[BaseModel]	Required	The Pydantic model class to render
initial_values	Optional[Union[BaseModel, Dict]]	None	Initial form values as model instance or dictionary
custom_renderers	Optional[List[Tuple[Type, Type[BaseFieldRenderer]]]]	None	List of (type, renderer_class) pairs for custom rendering
disabled	bool	False	Whether to disable all form inputs
disabled_fields	Optional[List[str]]	None	List of specific field names to disable
label_colors	Optional[Dict[str, str]]	None	Mapping of field names to CSS colors or Tailwind classes
exclude_fields	Optional[List[str]]	None	List of field names to exclude from rendering (auto-injected on submission)
spacing	SpacingValue	"normal"	Spacing theme: "normal", "compact", or SpacingTheme enum
metrics_dict	Optional[Dict[str, Dict]]	None	Field metrics for highlighting and tooltips

ComparisonForm Constructor

Parameter	Type	Default	Description
name	str	Required	Unique identifier for the comparison form
left_form	PydanticForm	Required	Form to display on the left side
right_form	PydanticForm	Required	Form to display on the right side
left_label	str	"Left"	Label for the left form
right_label	str	"Right"	Label for the right form

PydanticForm Methods

Method	Purpose
render_inputs()	Generate the HTML form inputs (without <form> wrapper)
with_initial_values(initial_values)	Create a new form instance with same configuration but different initial values
refresh_button(text=None, **kwargs)	Create a refresh button component
reset_button(text=None, **kwargs)	Create a reset button component
register_routes(app)	Register HTMX endpoints for list manipulation
parse(form_dict)	Parse raw form data into model-compatible dictionary
model_validate_request(req)	Extract, parse, and validate form data from request

ComparisonForm Methods

Method	Purpose
render_inputs()	Generate side-by-side form inputs
form_wrapper(content)	Wrap content with comparison form structure
left_reset_button(text=None, **kwargs)	Reset button for left form
right_reset_button(text=None, **kwargs)	Reset button for right form
left_refresh_button(text=None, **kwargs)	Refresh button for left form
right_refresh_button(text=None, **kwargs)	Refresh button for right form
register_routes(app)	Register HTMX endpoints for both forms

Utility Functions

Function	Purpose
list_manipulation_js()	JavaScript for list reordering and toggle functionality
comparison_form_js()	JavaScript for comparison form accordion synchronization
default_dict_for_model(model_class)	Generate default values for all fields in a model
default_for_annotation(annotation)	Get sensible default for a type annotation

Contributing

Contributions are welcome! Please feel free to open an issue or submit a pull request.