gradio-taggrouphelper 0.0.2

A fast text generator based on tagged words

---

tags: [gradio-custom-component, ] title: gradio_taggrouphelper short_description: A fast prompt generator based on tagged words colorFrom: blue colorTo: yellow sdk: gradio pinned: false app_file: space.py

gradio_taggrouphelper

A fast text generator based on tagged words

Installation

pip install gradio_taggrouphelper

Usage

#
# demo/app.py
#
import gradio as gr
from gradio_taggrouphelper import TagGroupHelper 

# Example data structure for the tags and groups
TAG_DATA = {
    "Quality": [
        "best quality", "masterpiece", "high resolution", "4k", "8k", 
        "sharp focus", "detailed", "photorealistic"
    ],
    "Lighting": [
        "cinematic lighting", "volumetric lighting", "god rays", 
        "golden hour", "studio lighting", "dramatic lighting"
    ],
    "Style": [
        "anime style", "oil painting", "concept art", "fantasy", 
        "steampunk", "vaporwave", "line art"
    ],
    "Negative Prompts": [
        "blurry", "noisy", "low resolution", "low quality", "watermark",
        "text", "bad anatomy", "extra limbs", "disfigured"
    ]
}

with gr.Blocks() as demo:
    gr.Markdown("# Tag Group Helper Demo")
    gr.Markdown("Click on the tags below to add them to the prompt textboxes.")

    with gr.Row():
        with gr.Column(scale=2): # Give more space to the textboxes
            # Create the target Textbox and give it a unique `elem_id`.
            positive_prompt_box = gr.Textbox(
                label="Positive Prompt",
                placeholder="Click tags from 'Prompt Keywords' to add them here...",
                lines=5,
                elem_id="positive-prompt-textbox" # This ID must be unique
            )
            negative_prompt_box = gr.Textbox(
                label="Negative Prompt",
                placeholder="Click tags from 'Negative Keywords' to add them here...",
                lines=5,
                elem_id="negative-prompt-textbox" # This ID must be unique
            )

        with gr.Column(scale=1): # Give less space to the helpers
            # Create an instance of the TagGroupHelper for the Positive Prompt box.
            TagGroupHelper(
                label="Positive Prompt Keywords",
                value={k: v for k, v in TAG_DATA.items() if "Negative" not in k},
                target_textbox_id="positive-prompt-textbox",
                separator=" "
            )
            
            # Create another instance for the Negative Prompt box.
            TagGroupHelper(
                label="Negative Prompt Keywords",
                value={"Negative Prompts": TAG_DATA["Negative Prompts"]},
                target_textbox_id="negative-prompt-textbox",
                min_width=150,
                separator=", "
            )

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

TagGroupHelper

Initialization

name	type	default	description
value	typing.Optional[typing.Dict[str, typing.List[str]]][ typing.Dict[str, typing.List[str]][ str, typing.List[str][str] ], None, ]	None	A dictionary where keys are group names and values are lists of tags.
target_textbox_id	str | None	None	The `elem_id` of the `gr.Textbox` component to target. Required.
separator	str	", "	The string to use as a separator between tags. Defaults to ", ". Can be set to " " for space separation.
label	str | None	None	The label for this component, displayed above the groups.
visible	bool	True	If False, the component will be hidden.
elem_id	str | None	None	An optional string that is assigned as the id of this component in the HTML DOM.
scale	int | None	None	The relative size of the component compared to others in a `gr.Row` or `gr.Column`.
min_width	int | None	None	The minimum-width of the component in pixels.
container	bool	True	If False, the component will not be wrapped in a container.
elem_classes	list[str] | str | None	None	An optional list of strings to assign as CSS classes to the component.

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
) -> typing.Optional[typing.Dict[str, typing.List[str]]][
   typing.Dict[str, typing.List[str]][
       str, typing.List[str][str]
   ],
   None,
]:
    return value