gradio_taggrouphelper

A fast text generator based on tagged words

Tag Group Helper Demo

Click on the tags below to add them to the prompt textboxes.

💻 GitHub Code
{
  • "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"
    ]
}

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
            )

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

TagGroupHelper

Initialization
Parameters 
value: typing.Optional[typing.Dict[str, typing.List[str]]][
    typing.Dict[str, typing.List[str]][
        str, typing.List[str][str]
    ],
    None,
]
default = None

A dictionary where keys are group names and values are lists of tags.

height: int | None
default = None

The height of the component container in pixels.

width: int | None
default = None

The width of the component container in pixels.

label: str | None
default = None

The label for this component, displayed above the groups.

font_size_scale: int
default = 100

A percentage to scale the font size of group headers and tags. Defaults to 100.

every: float | None
default = None

If `value` is a callable, run the function 'every' seconds while the client connection is open.

show_label: bool | None
default = None

If False, the label is not displayed.

container: bool
default = True

If False, the component will not be wrapped in a container.

scale: int | None
default = None

The relative size of the component compared to others in a `gr.Row` or `gr.Column`.

min_width: int
default = 160

The minimum width of the component in pixels.

interactive: bool | None
default = None

if True, will be rendered as an selectable component; if False, editing will be disabled. If not provided, this is inferred based on whether the component is used as an input or output.

target_textbox_id: str | None
default = None

The `elem_id` of the `gr.Textbox` component to target. Required.

separator: str
default = ", "

The string to use as a separator between tags. Defaults to ", ". Can be set to " " for space separation.

visible: bool
default = True

If False, the component will be hidden.

elem_id: str | None
default = None

An optional string that is assigned as the id of this component in the HTML DOM.

elem_classes: list[str] | str | None
default = 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