app.py

import gradio as gr
from ctag import (MODELS, DEFAULT_DF, DEFAULT_SERIES_DF, search_char_dict, on_select_df,
                  search_series_dict, on_select_series_df, update_series_chars, apply_series, on_select_series_gallery)
from hft2i import (gen_image, save_gallery, get_models, get_def_model, change_model, warm_model, get_model_info_md, get_recom_prompt_mode, update_prompt)

MAX_IMAGES = 6
MAX_SEED = 2**32-1

CSS = """
.title { font-size: 3em; align-items: center; text-align: center; }
.info { align-items: center; text-align: center; }
img[src*="#center"] { display: block; margin: auto; }
.model_info { text-align: center; }
.output { width=112px; height=112px; max_width=112px; max_height=112px; !important; }
.gallery { min_width=512px; min_height=512px; max_height=1024px; !important; }
"""

with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', fill_width=True, css=CSS) as app:
    gr.Markdown("## 🔍 Text Search for Animagine / Illustrious / NoobAI XL tag characters", elem_classes="title")
    with gr.Column():
        with gr.Group():
            with gr.Row(equal_height=True):
                with gr.Column(scale=2):
                    search_input = gr.Textbox(label="Search for characters or series:", placeholder="sousou no frieren")
                    search_detail = gr.Checkbox(label="Show character detail", value=True)
                search_model = gr.CheckboxGroup(label="Models", choices=MODELS, value=MODELS, scale=1)
            with gr.Accordion("Search from series", open=False):
                with gr.Row(equal_height=True):
                    search_series_input = gr.Textbox(label="Search for series:", placeholder="sousou no frieren")
                    search_series_detail = gr.Checkbox(label="Show series detail", value=True)
                with gr.Row(equal_height=True):
                    search_series = gr.Textbox(label="Selected series", value="", interactive=False)
                    search_chars = gr.Dropdown(label="Characters", choices=[""], value="", allow_custom_value=True)
                    search_series_button = gr.Button("Add to search")
                with gr.Row(equal_height=True):
                    search_series_output = gr.Dataframe(label="Select series", value=DEFAULT_SERIES_DF, type="pandas", wrap=True, interactive=False)
                    with gr.Column():
                        search_series_md = gr.Markdown("<br><br><br>", elem_classes="info")
                        search_series_gallery = gr.Gallery(label="Characters", allow_preview=False, value=[], columns=4, rows=4, elem_id="gallery", show_share_button=False, interactive=False)
        with gr.Group():
            with gr.Row(equal_height=True):
                search_tag = gr.Textbox(label="Output tag", value="", show_copy_button=True, interactive=False)
                search_tag_model = gr.Textbox(label="Model", value="", visible=False)
                search_md = gr.Markdown("<br><br><br>", elem_classes="info")
            search_output = gr.Dataframe(label="Select character", value=DEFAULT_DF, type="pandas", wrap=True, interactive=False)
        with gr.Group():
            prompt = gr.Text(label="Prompt", lines=2, max_lines=8, placeholder="1girl, solo, ...", show_copy_button=True)
            with gr.Accordion("Advanced options", open=False):
                nprompt = gr.Text(label="Negative Prompt", lines=1, max_lines=8, placeholder="")
                with gr.Row():
                    width = gr.Slider(label="Width", info="If 0, default value is used.", maximum=2048, step=32, value=0)
                    height = gr.Slider(label="Height", info="If 0, default value is used.", maximum=2048, step=32, value=0)
                    steps = gr.Slider(label="Number of inference steps", info="If 0, default value is used.", maximum=100, step=1, value=0)
                    cfg = gr.Slider(label="Guidance scale", info="If 0, default value is used.", maximum=30.0, step=0.1, value=0)
                    seed = gr.Slider(label="Seed", info="If -1, use Randomized Seed.", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
                with gr.Row():
                    model_name = [None] * MAX_IMAGES
                    model_info = [None] * MAX_IMAGES
                    for i in range(MAX_IMAGES):
                        with gr.Column():
                            model_name[i] = gr.Dropdown(label=f"Select Model {int(i) + 1}", choices=get_models(), value=get_def_model(i), allow_custom_value=True)
                            model_info[i] = gr.Markdown(value=get_model_info_md(get_def_model(i)), elem_classes="model_info")
                recom_prompt_mode = gr.Radio(label="Recommened prompt", choices=get_recom_prompt_mode(), value="Common")
            gen_button = gr.Button("Generate Image", variant="primary")
        with gr.Group():
            with gr.Row():
                output_images = [gr.Image(label='', elem_classes="output", type="filepath", format="png",
                                          show_download_button=True, show_share_button=False,
                                          interactive=False, min_width=80, visible=True, width=112, height=112) for _ in range(MAX_IMAGES)]
            gallery = gr.Gallery(label="Gallery", elem_classes="gallery", interactive=False, show_download_button=True, show_share_button=False,
                                 container=True, format="png", object_fit="cover", columns=1, rows=1)
            image_files = gr.Files(label="Download", interactive=False)
            clear_image_button = gr.Button("Clear Gallery / Download 🗑️", variant="secondary")

    gr.on(triggers=[search_input.change, search_model.change], fn=search_char_dict,
          inputs=[search_input, search_model], outputs=[search_output], trigger_mode="always_last")
    search_output.select(on_select_df, [search_output, search_detail], [search_tag, search_tag_model, search_md], queue=False, show_api=False)
    search_tag.change(update_prompt, [search_tag, search_tag_model], [prompt, model_name[0], recom_prompt_mode], queue=False, show_api=False)
    search_series_input.change(search_series_dict, [search_series_input], [search_series_output])
    search_series_output.select(on_select_series_df, [search_series_output, search_series_detail], [search_series, search_series_md, search_series_gallery], queue=False, show_api=False)
    search_series.change(update_series_chars, [search_series], [search_chars], queue=False, show_api=False)
    search_series_gallery.select(on_select_series_gallery, [search_series_gallery], [search_input], queue=False, show_api=False)
    search_series_button.click(apply_series, [search_series, search_chars], [search_input], queue=False, show_api=False)
    for i, o in enumerate(output_images):
        img_i = gr.Number(i, visible=False)
        model_name[i].change(change_model, [model_name[i]], [model_info[i]], queue=False, show_api=False)\
        .success(warm_model, [model_name[i]], None, queue=False, show_api=False)\
        .then(lambda x: gr.update(label=x.split("/")[-1]), [model_name[i]], [o], queue=False, show_api=False)
        gen_event = gr.on(triggers=[gen_button.click, prompt.submit],
         fn=lambda m, t1, t2, n1, n2, n3, n4, n5, t3: gen_image(m, t1, t2, n1, n2, n3, n4, n5, t3),
         inputs=[model_name[i], prompt, nprompt, height, width, steps, cfg, seed, recom_prompt_mode],
         outputs=[o], queue=False, show_api=False)
        o.change(save_gallery, [o, gallery], [gallery, image_files], show_api=False)

app.launch(ssr_mode=False)