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import streamlit as st
import numpy as np
import random
import pandas as pd
import glob
import csv
from PIL import Image
import datasets
from datasets import load_dataset, Dataset, load_from_disk
from huggingface_hub import login
import os
import requests
SCORE_NAME_MAPPING = {'clip': 'clip_score', 'rank': 'avg_rank', 'pop': 'model_download_count'}
class GalleryApp:
def __init__(self, promptBook):
self.promptBook = promptBook
st.set_page_config(layout="wide")
def gallery_masonry(self, items, col_num, info):
cols = st.columns(col_num)
# # sort items by brisque score
# items = items.sort_values(by=['brisque'], ascending=True).reset_index(drop=True)
for idx in range(len(items)):
with cols[idx % col_num]:
image = st.session_state.images[items.iloc[idx]['row_idx'].item()]['image']
st.image(image,
use_column_width=True,
)
# with st.expander('Similarity Info'):
# tab1, tab2 = st.tabs(['Most Similar', 'Least Similar'])
# with tab1:
# st.image(image, use_column_width=True)
# with tab2:
# st.image(image, use_column_width=True)
# show checkbox
self.promptBook.loc[items.iloc[idx]['row_idx'].item(), 'checked'] = st.checkbox(
'Select', value=self.promptBook.loc[items.iloc[idx]['row_idx'].item(), 'checked'],
key=f'select_{idx}')
for key in info:
st.write(f"**{key}**: {items.iloc[idx][key]}")
def gallery_standard(self, items, col_num, info):
rows = len(items) // col_num + 1
containers = [st.container() for _ in range(rows*2)]
for idx in range(0, len(items), col_num):
# assign one container for each row
row_idx = (idx // col_num) * 2
with containers[row_idx]:
cols = st.columns(col_num)
for j in range(col_num):
if idx + j < len(items):
with cols[j]:
# show image
image = st.session_state.images[items.iloc[idx+j]['row_idx'].item()]['image']
# image = list(st.session_state.images.skip(items.iloc[idx+j]['row_idx'].item()).take(1))[0]['image']
st.image(image,
use_column_width=True,
)
# show checkbox
self.promptBook.loc[items.iloc[idx+j]['row_idx'].item(), 'checked'] = st.checkbox('Select', value=self.promptBook.loc[items.iloc[idx+j]['row_idx'].item(), 'checked'], key=f'select_{idx+j}')
# show selected info
for key in info:
st.write(f"**{key}**: {items.iloc[idx+j][key]}")
# st.write(row_idx/2, idx+j, rows)
# extra_info = st.checkbox('Extra Info', key=f'extra_info_{idx+j}')
# if extra_info:
# with containers[row_idx+1]:
# st.image(image, use_column_width=True)
def app(self):
st.title('Model Coffer Gallery')
st.write('This is a gallery of images generated by the models in the Model Coffer')
with st.sidebar:
prompt_tags = self.promptBook['tag'].unique()
# sort tags by alphabetical order
prompt_tags = np.sort(prompt_tags)[::-1]
tag = st.selectbox('Select a tag', prompt_tags)
items = self.promptBook[self.promptBook['tag'] == tag].reset_index(drop=True)
original_prompts = np.sort(items['prompt'].unique())[::-1]
# remove the first four items in the prompt, which are mostly the same
if tag != 'abstract':
prompts = [', '.join(x.split(', ')[4:]) for x in original_prompts]
prompt = st.selectbox('Select prompt', prompts)
idx = prompts.index(prompt)
prompt_full = ', '.join(original_prompts[idx].split(', ')[:4]) + ', ' + prompt
else:
prompt_full = st.selectbox('Select prompt', original_prompts)
prompt_id = items[items['prompt'] == prompt_full]['prompt_id'].unique()[0]
items = items[items['prompt_id'] == prompt_id].reset_index(drop=True)
st.write('**Prompt ID**')
st.caption(f"{prompt_id}")
st.write('**Prompt**')
st.caption(f"{items['prompt'][0]}")
st.write('**Negative Prompt**')
st.caption(f"{items['negativePrompt'][0]}")
st.write('**Sampler**')
st.caption(f"{items['sampler'][0]}")
st.write('**cfgScale**')
st.caption(f"{items['cfgScale'][0]}")
st.write('**Size**')
st.caption(f"width: {items['size'][0].split('x')[0]}, height: {items['size'][0].split('x')[1]}")
st.write('**Seed**')
st.caption(f"{items['seed'][0]}")
# # for tag as civitai, add civitai reference
# if tag == 'civitai':
# st.write('**Reference**')
#
# res = requests.get(f'https://civitai.com/images', params={'post_id': prompt_id})
# st.write(res)
# image_url = res.json()['items'][0]['url']
# st.image(image_url, use_column_width=True)
# with images:
# selecters = st.columns([2, 1, 2, 0.5])
selecters = st.columns([4, 1, 1])
with selecters[0]:
# # sort_by = st.selectbox('Sort by', items.columns[11: -1])
# sort_by = st.selectbox('Sort by', ['model_download_count', 'clip_score', 'avg_rank', 'model_name', 'model_id',
# 'modelVersion_name', 'modelVersion_id'])
print(items.columns)
types = st.columns([1, 3])
with types[0]:
sort_type = st.selectbox('Sort by', ['IDs and Names', 'Scores'])
with types[1]:
if sort_type == 'IDs and Names':
sort_by = st.selectbox('Sort by', ['model_name', 'model_id', 'modelVersion_name', 'modelVersion_id'], label_visibility='hidden')
elif sort_type == 'Scores':
sort_by = st.multiselect('Sort by', ['clip_score', 'avg_rank', 'popularity'], label_visibility='hidden', default=['clip_score', 'avg_rank', 'popularity'])
# process sort_by to map to the column name
if len(sort_by) == 3:
sort_by = 'clip+rank+pop'
elif len(sort_by) == 2:
if 'clip_score' in sort_by and 'avg_rank' in sort_by:
sort_by = 'clip+rank'
elif 'clip_score' in sort_by and 'popularity' in sort_by:
sort_by = 'clip+pop'
elif 'avg_rank' in sort_by and 'popularity' in sort_by:
sort_by = 'rank+pop'
elif len(sort_by) == 1:
if 'popularity' in sort_by:
sort_by = 'model_download_count'
else:
sort_by = sort_by[0]
print(sort_by)
with selecters[1]:
order = st.selectbox('Order', ['Ascending', 'Descending'], index=1 if sort_type == 'Scores' else 0)
if order == 'Ascending':
order = True
else:
order = False
items = items.sort_values(by=[sort_by], ascending=order).reset_index(drop=True)
with selecters[2]:
filter = st.selectbox('Filter', ['All', 'Checked', 'Unchecked'])
if filter == 'Checked':
items = items[items['checked'] is True].reset_index(drop=True)
elif filter == 'Unchecked':
items = items[items['checked'] is False].reset_index(drop=True)
info = st.multiselect('Show Info',
['model_download_count', 'clip_score', 'avg_rank', 'model_name', 'model_id',
'modelVersion_name', 'modelVersion_id', 'clip+rank', 'clip+pop', 'rank+pop', 'clip+rank+pop'],
default=sort_by)
print('info', info)
# add one annotation
mentioned_scores = []
for i in info:
if '+' in i:
mentioned = i.split('+')
for m in mentioned:
if SCORE_NAME_MAPPING[m] not in mentioned_scores:
mentioned_scores.append(SCORE_NAME_MAPPING[m])
if len(mentioned_scores) > 0:
st.write(f"**Note: ** The scores {mentioned_scores} are normalized to [0, 1] for each score type, and then added together. The higher the score, the better the model.")
col_num = st.slider('Number of columns', min_value=1, max_value=9, value=4, step=1, key='col_num')
with st.form(key=f'{prompt_id}', clear_on_submit=False):
buttons = st.columns([1, 1, 1])
with buttons[0]:
submit = st.form_submit_button('Save selections', on_click=self.save_checked, use_container_width=True, type='primary')
with buttons[1]:
submit = st.form_submit_button('Reset current prompt', on_click=self.reset_current_prompt, kwargs={'prompt_id': prompt_id} , use_container_width=True)
with buttons[2]:
submit = st.form_submit_button('Reset all selections', on_click=self.reset_all, use_container_width=True)
self.gallery_standard(items, col_num, info)
def reset_current_prompt(self, prompt_id):
# reset current prompt
self.promptBook.loc[self.promptBook['prompt_id'] == prompt_id, 'checked'] = False
self.save_checked()
def reset_all(self):
# reset all
self.promptBook.loc[:, 'checked'] = False
self.save_checked()
def save_checked(self):
# save checked images to huggingface dataset
dataset = load_dataset('NYUSHPRP/ModelCofferMetadata', split='train')
# get checked images
checked_info = self.promptBook['checked']
# print('checked_info: ', checked_info)
# for d in checked_info:
# if d is True:
# print('checked')
if 'checked' in dataset.column_names:
dataset = dataset.remove_columns('checked')
dataset = dataset.add_column('checked', checked_info)
# print('metadata dataset: ', dataset)
dataset.push_to_hub('NYUSHPRP/ModelCofferMetadata', split='train')
if __name__ == '__main__':
login(token=os.environ.get("HF_TOKEN"))
if 'roster' not in st.session_state:
print('loading roster')
# st.session_state.roster = pd.DataFrame(load_dataset('NYUSHPRP/ModelCofferRoster', split='train'))
st.session_state.roster = pd.DataFrame(load_from_disk(os.path.join(os.getcwd(), 'data', 'roster')))
st.session_state.roster = st.session_state.roster[['model_id', 'model_name', 'modelVersion_id', 'modelVersion_name',
'model_download_count']].drop_duplicates().reset_index(drop=True)
# add model download count from roster to promptbook dataframe
if 'promptBook' not in st.session_state:
print('loading promptBook')
st.session_state.promptBook = pd.DataFrame(load_dataset('NYUSHPRP/ModelCofferMetadata', split='train'))
# add 'checked' column to promptBook if not exist
if 'checked' not in st.session_state.promptBook.columns:
st.session_state.promptBook.loc[:, 'checked'] = False
st.session_state.images = load_from_disk(os.path.join(os.getcwd(), 'data', 'promptbook'))
# st.session_state.images = load_dataset('NYUSHPRP/ModelCofferPromptBook', split='train', streaming=True)
print(st.session_state.images)
print('images loaded')
# st.session_state.promptBook = pd.DataFrame(load_dataset('NYUSHPRP/ModelCofferPromptBook', split='train'))
st.session_state.promptBook = st.session_state.promptBook.merge(st.session_state.roster[['model_id', 'model_name', 'modelVersion_id', 'modelVersion_name', 'model_download_count']], on=['model_id', 'modelVersion_id'], how='left')
# add column to record current row index
st.session_state.promptBook['row_idx'] = st.session_state.promptBook.index
print('promptBook loaded')
# print(st.session_state.promptBook)
check_roster_error = False
if check_roster_error:
# print all rows with the same model_id and modelVersion_id but different model_download_count in roster
print(st.session_state.roster[st.session_state.roster.duplicated(subset=['model_id', 'modelVersion_id'], keep=False)].sort_values(by=['model_id', 'modelVersion_id']))
app = GalleryApp(promptBook=st.session_state.promptBook)
app.app()
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