gradio-propertysheet 0.0.1

Property sheet

---

tags: [gradio-custom-component, ] title: gradio_propertysheet short_description: Property sheet colorFrom: blue colorTo: yellow sdk: gradio pinned: false app_file: space.py

gradio_propertysheet

Property sheet

Installation

pip install gradio_propertysheet

Usage

import gradio as gr
from dataclasses import dataclass, field, asdict
from typing import Literal
from gradio_propertysheet import PropertySheet

# --- Main Configuration Dataclasses for the "Render Settings" Sheet ---
@dataclass
class ModelSettings:
    """Settings for loading models, VAEs, etc."""
    model_type: Literal["SD 1.5", "SDXL", "Pony", "Custom"] = field(
        default="SDXL",
        metadata={"component": "dropdown", "label": "Base Model"}
    )
    custom_model_path: str = field(
        default="/path/to/default.safetensors",
        metadata={"label": "Custom Model Path", "interactive_if": {"field": "model_type", "value": "Custom"}}
    )
    vae_path: str = field(
        default="",
        metadata={"label": "VAE Path (optional)"}
    )

@dataclass
class SamplingSettings:
    """Settings for the image sampling process."""
    sampler_name: Literal["Euler", "Euler a", "DPM++ 2M Karras", "UniPC"] = field(
        default="DPM++ 2M Karras",
        metadata={"component": "dropdown", "label": "Sampler", "help": "The algorithm for the diffusion process."}
    )
    steps: int = field(
        default=25,
        metadata={"component": "slider", "minimum": 1, "maximum": 150, "step": 1, "label": "Sampling Steps", "help": "More steps can improve quality."}
    )
    cfg_scale: float = field(
        default=7.0,
        metadata={"component": "slider", "minimum": 1.0, "maximum": 30.0, "step": 0.5, "label": "CFG Scale", "help": "How strongly the prompt is adhered to."}
    )

@dataclass
class ImageSettings:
    """Settings for image dimensions."""
    width: int = field(
        default=1024,
        metadata={"component": "slider", "minimum": 512, "maximum": 2048, "step": 64, "label": "Image Width"}
    )
    height: int = field(
        default=1024,
        metadata={"component": "slider", "minimum": 512, "maximum": 2048, "step": 64, "label": "Image Height"}
    )

@dataclass
class PostprocessingSettings:
    """Settings for image post-processing effects."""
    restore_faces: bool = field(
        default=True,
        metadata={"label": "Restore Faces", "help": "Use a secondary model to fix distorted faces."}
    )
    enable_hr: bool = field(
        default=False,
        metadata={"label": "Hires. fix", "help": "Enable a second pass at a higher resolution."}
    )
    denoising_strength: float = field(
        default=0.45,
        metadata={"component": "slider", "minimum": 0.0, "maximum": 1.0, "step": 0.01, "label": "Denoising Strength", "interactive_if": {"field": "enable_hr", "value": True}}
    )

@dataclass
class AdvancedSettings:
    """Advanced and rarely changed settings."""
    clip_skip: int = field(
        default=2,
        metadata={"component": "slider", "minimum": 1, "maximum": 12, "step": 1, "label": "CLIP Skip", "help": "Skip final layers of the text encoder."}
    )
    noise_schedule: Literal["Default", "Karras", "Exponential"] = field(
        default="Karras",
        metadata={"component": "dropdown", "label": "Noise Schedule"}
    )
    do_not_scale_cond_uncond: bool = field(
        default=False,
        metadata={"label": "Do not scale cond/uncond"}
    )
    s_churn: int = field(
        default=1,
        metadata={"component": "number_integer", "minimum": 1, "maximum": 12, "label": "S_churn", "help": "S_churn value for generation."}
    )

@dataclass
class ScriptSettings:
    """Settings for automation scripts like X/Y/Z plots."""
    script_name: Literal["None", "Prompt matrix", "X/Y/Z plot"] = field(
        default="None",
        metadata={"component": "dropdown", "label": "Script"}
    )
    x_values: str = field(
        default="-1, 10, 20",
        metadata={"label": "X axis values", "interactive_if": {"field": "script_name", "value": "X/Y/Z plot"}}
    )
    y_values: str = field(
        default="",
        metadata={"label": "Y axis values", "interactive_if": {"field": "script_name", "value": "X/Y/Z plot"}}
    )

@dataclass
class RenderConfig:
    """Main configuration object for rendering, grouping all settings."""
    seed: int = field(
        default=-1,
        metadata={"component": "number_integer", "label": "Seed (-1 for random)", "help": "The random seed for generation."}
    )
    batch_size: int = field(
        default=1,
        metadata={"component": "slider", "minimum": 1, "maximum": 8, "step": 1, "label": "Batch Size"}
    )
    # Nested groups
    model: ModelSettings = field(default_factory=ModelSettings)
    sampling: SamplingSettings = field(default_factory=SamplingSettings)
    image: ImageSettings = field(default_factory=ImageSettings)
    postprocessing: PostprocessingSettings = field(default_factory=PostprocessingSettings)
    scripts: ScriptSettings = field(default_factory=ScriptSettings)
    advanced: AdvancedSettings = field(default_factory=AdvancedSettings)


@dataclass
class Lighting:
    """Lighting settings for the environment."""
    sun_intensity: float = field(default=1.0, metadata={"component": "slider", "minimum": 0, "maximum": 5, "step": 0.1})
    ambient_occlusion: bool = field(default=True, metadata={"label": "Ambient Occlusion"})
    color: str = field(default="#FFDDBB", metadata={"component": "colorpicker", "label": "Sun Color"})

@dataclass
class EnvironmentConfig:
    """Main configuration for the environment."""
    background: Literal["Sky", "Color", "Image"] = field(default="Sky", metadata={"component": "dropdown"})
    lighting: Lighting = field(default_factory=Lighting)


# --- Initial Instances ---
initial_render_config = RenderConfig()
initial_env_config = EnvironmentConfig()

# --- Gradio Application ---
with gr.Blocks(title="PropertySheet Demo") as demo:
    gr.Markdown("# PropertySheet Component Demo")
    gr.Markdown("An example of a realistic application layout using the `PropertySheet` component as a sidebar for settings.")
    gr.Markdown("<span>💻 <a href='https://github.com/DEVAIEXP/gradio_component_propertysheet'>Component GitHub Code</a></span>")
    
    with gr.Row():
        # Main content area on the left
        with gr.Column(scale=3):
            #gr.Image(label="Main Viewport", height=500, value=None)
            gr.Textbox(label="AI Prompt", lines=3, placeholder="Enter your prompt here...")
            gr.Button("Generate", variant="primary")
            with gr.Row():
                output_render_json = gr.JSON(label="Live Render State")
                output_env_json = gr.JSON(label="Live Environment State")

        # Sidebar with Property Sheets on the right
        with gr.Column(scale=1):
            render_sheet = PropertySheet(
                value=initial_render_config, 
                label="Render Settings",
                width=400,
                height=550  # Set a fixed height to demonstrate internal scrolling
            )
            environment_sheet = PropertySheet(
                value=initial_env_config,
                label="Environment Settings",
                width=400,
                open=False  # Start collapsed to show the accordion feature
            )

    # --- Event Handlers ---
    def handle_render_change(updated_config: RenderConfig | None):
        """Callback to process changes from the Render Settings sheet."""
        if updated_config is None:
            return initial_render_config, asdict(initial_render_config)
        
        # Example of business logic: reset custom path if not in custom mode
        if updated_config.model.model_type != "Custom":
            updated_config.model.custom_model_path = "/path/to/default.safetensors"
            
        return updated_config, asdict(updated_config)

    def handle_env_change(updated_config: EnvironmentConfig | None):
        """Callback to process changes from the Environment Settings sheet."""
        if updated_config is None:
            return initial_env_config, asdict(initial_env_config)
        return updated_config, asdict(updated_config)

    render_sheet.change(
        fn=handle_render_change,
        inputs=[render_sheet],
        outputs=[render_sheet, output_render_json]
    )
    environment_sheet.change(
        fn=handle_env_change,
        inputs=[environment_sheet],
        outputs=[environment_sheet, output_env_json]
    )
   
    # Load initial state into JSON viewers on app load
    demo.load(
        fn=lambda: (asdict(initial_render_config), asdict(initial_env_config)),
        outputs=[output_render_json, output_env_json]
    )

if __name__ == "__main__":
    demo.launch()

PropertySheet

Initialization

name	type	default	description
value	typing.Optional[typing.Any][Any, None]	None	The initial dataclass instance to render.
label	str | None	None	The main label for the component, displayed in the accordion header.
visible	bool	True	If False, the component will be hidden.
open	bool	True	If False, the accordion will be collapsed by default.
elem_id	str | None	None	An optional string that is assigned as the id of this component in the DOM.
scale	int | None	None	The relative size of the component in its container.
width	int | str | None	None	The width of the component in pixels.
height	int | str | None	None	The maximum height of the component's content area in pixels before scrolling.
min_width	int | None	None	The minimum width of the component in pixels.
container	bool	True	If True, wraps the component in a container with a background.
elem_classes	list[str] | str | None	None	An optional list of strings that are assigned as the classes of this component in the DOM.

Events

name	description
change
input
expand
collapse

User function

The impact on the users predict function varies depending on whether the component is used as an input or output for an event (or both).

• When used as an Input, the component only impacts the input signature of the user function.
• When used as an output, the component only impacts the return signature of the user function.

The code snippet below is accurate in cases where the component is used as both an input and an output.

def predict(
    value: Any
) -> Any:
    return value