find_my_waifu / app.py
narugo1992
dev(narugo): configure hf_hub_download
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raw
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import io
import json
import os
import gradio as gr
import markdown
import pandas as pd
from gchar.games.dispatch.access import get_character
from gchar.resources.pixiv import get_pixiv_keywords, get_pixiv_posts
from gchar.resources.sites import list_available_sites, get_site_tag
from gchar.utils import get_requests_session
from huggingface_hub import hf_hub_url, configure_http_backend
from pycivitai import civitai_find_online
from pycivitai.client import ModelNotFound
from character import get_ch_name
from civitai import try_find_title
from huggingface import get_hf_fs
hf_fs = get_hf_fs()
configure_http_backend(get_requests_session)
def query(chr_name):
ch = get_character(chr_name, allow_fuzzy=True)
# get character info
info_columns = ['Property', 'Value']
info_data = []
info_data.append(('Index', ch.index))
ennames = [str(enname) for enname in ch.ennames]
if ennames:
info_data.append(('EN Name', ', '.join(ennames)))
cnnames = [str(cnname) for cnname in ch.cnnames]
if cnnames:
info_data.append(('CN Name', ', '.join(cnnames)))
jpnames = [str(jpname) for jpname in ch.jpnames]
if jpnames:
info_data.append(('JP Name', ', '.join(jpnames)))
if hasattr(ch, 'krnames'):
krnames = [str(krname) for krname in ch.krnames]
if krnames:
info_data.append(('KR Name', ', '.join(krnames)))
info_data.append(('Sex', ch.gender.name))
info_data.append(('Source', ch.__official_name__))
info_df = pd.DataFrame(columns=info_columns, data=info_data)
# get skins
skin_dir = f'datasets/deepghs/game_character_skins/{ch.__game_name__}/{ch.index}'
meta_json = f'{skin_dir}/.meta.json'
skin_urls = []
if hf_fs.exists(meta_json):
meta = json.loads(hf_fs.read_text(meta_json))
for item in meta['files']:
skin_url = hf_hub_url(
'deepghs/game_character_skins',
filename=f'{ch.__game_name__}/{ch.index}/{item["name"]}',
repo_type='dataset',
)
skin_name = item['metadata']['name']
skin_urls.append((skin_url, skin_name))
# get repo info
repo = f'CyberHarem/{get_ch_name(ch)}'
with io.StringIO() as sf:
if hf_fs.exists(f'{repo}/meta.json'):
model_url = f'https://huggingface.co/{repo}'
print(f'Model: [{model_url}]({model_url})', file=sf)
else:
print(f'Model not found.', file=sf)
print(file=sf)
if hf_fs.exists(f'datasets/{repo}/dataset-raw.zip'):
ds_url = f'https://huggingface.co/datasets/{repo}'
print(f'Dataset: [{ds_url}]({ds_url})', file=sf)
else:
print('Dataset not found.', file=sf)
print(file=sf)
try:
model_name = try_find_title(str(ch.enname), ch.__game_name__)
resource = civitai_find_online(model_name)
civit_url = f'https://civitai.com/models/{resource.model_id}'
print(f'CivitAI Model: [{civit_url}]({civit_url})', file=sf)
except ModelNotFound:
print('No CivitAI published model found.', file=sf)
print(file=sf)
html = markdown.markdown(sf.getvalue())
# get tags on all sites
tags_columns = ['Site', 'Posts', 'Tag']
tags_data = []
tags_data.append(('Pixiv (ALL)', get_pixiv_posts(ch)[0], get_pixiv_keywords(ch)))
tags_data.append(('Pixiv (R18)', get_pixiv_posts(ch)[1], get_pixiv_keywords(ch, includes=['R-18'])))
for site in list_available_sites():
tag_retval = get_site_tag(ch, site, with_posts=True, sure_only=True)
if tag_retval is not None:
tag_name, tag_cnt = tag_retval
tags_data.append((site, tag_cnt, tag_name))
tags_data = sorted(tags_data, key=lambda x: (-x[1], x[0]))
tags_df = pd.DataFrame(columns=tags_columns, data=tags_data)
return info_df, skin_urls, html, tags_df
if __name__ == '__main__':
with gr.Blocks() as demo:
gr_input = gr.Textbox(
label='Character Name',
placeholder='Enter name or alias of the character.'
)
gr_submit = gr.Button(value='Find My Waifu', variant='primary')
with gr.Row():
with gr.Column():
with gr.Row():
gr_info = gr.DataFrame(label='Character Info')
with gr.Row():
gr_skins = gr.Gallery(label='Skins')
with gr.Column():
with gr.Row():
gr_html = gr.HTML(label='Entry of Model and Dataset', value='(N/A)')
with gr.Row():
gr_tags = gr.DataFrame(label='Character Tags')
gr_submit.click(
query,
inputs=[
gr_input,
],
outputs=[
gr_info,
gr_skins,
gr_html,
gr_tags,
]
)
demo.queue(os.cpu_count()).launch()