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(theme=gr.themes.Ocean()) as demo:
    gr.Markdown("# Tag Group Helper Demo")
    gr.Markdown("Click on the tags below to add them to the prompt textboxes.")
    gr.Markdown("<span>💻 <a href='https://github.com/DEVAIEXP/gradio_component_taggrouphelper'>GitHub Code</a></span>")
    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.Sidebar(position="right"):           
            # 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=", ",
                interactive=True,
                width=250,
                font_size_scale=90
                
            )
            
            # 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",
                separator=", ",
                interactive=True,
                width=250,                
                font_size_scale=90,
                open=False
            )

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

TagGroupHelper

Initialization

typing.Optional[typing.Dict[str, typing.List[str]]][
    typing.Dict[str, typing.List[str]][
        str, typing.List[str][str]
    ],
    None,
]

int | None

int | None

str | None

int

bool

float | None

bool | None

bool

int | None

int

bool | None

str | None

str

bool

str | None

list[str] | str | None

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