Spaces:
Running
Running
beta version of new gallery view
Browse files- Archive/Gallery_beta0913.py +643 -0
- Archive/agraphTest.py +0 -170
- Archive/bokehTest.py +0 -182
- Archive/optimization.py +0 -37
- Archive/optimization2.py +0 -40
- pages/Gallery.py +112 -100
Archive/Gallery_beta0913.py
ADDED
@@ -0,0 +1,643 @@
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1 |
+
import os
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2 |
+
import requests
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3 |
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4 |
+
import altair as alt
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5 |
+
import numpy as np
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6 |
+
import pandas as pd
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7 |
+
import streamlit as st
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8 |
+
import streamlit.components.v1 as components
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9 |
+
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10 |
+
from bs4 import BeautifulSoup
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11 |
+
from datasets import load_dataset, Dataset, load_from_disk
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12 |
+
from huggingface_hub import login
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13 |
+
from streamlit_agraph import agraph, Node, Edge, Config
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14 |
+
from streamlit_extras.switch_page_button import switch_page
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15 |
+
from sklearn.svm import LinearSVC
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16 |
+
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17 |
+
SCORE_NAME_MAPPING = {'clip': 'clip_score', 'rank': 'msq_score', 'pop': 'model_download_count'}
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+
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+
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20 |
+
class GalleryApp:
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+
def __init__(self, promptBook, images_ds):
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22 |
+
self.promptBook = promptBook
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self.images_ds = images_ds
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+
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25 |
+
def gallery_standard(self, items, col_num, info):
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26 |
+
rows = len(items) // col_num + 1
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27 |
+
containers = [st.container() for _ in range(rows)]
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28 |
+
for idx in range(0, len(items), col_num):
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29 |
+
row_idx = idx // col_num
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30 |
+
with containers[row_idx]:
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31 |
+
cols = st.columns(col_num)
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32 |
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for j in range(col_num):
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33 |
+
if idx + j < len(items):
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34 |
+
with cols[j]:
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35 |
+
# show image
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36 |
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# image = self.images_ds[items.iloc[idx + j]['row_idx'].item()]['image']
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37 |
+
image = f"https://modelcofferbucket.s3-accelerate.amazonaws.com/{items.iloc[idx + j]['image_id']}.png"
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38 |
+
st.image(image, use_column_width=True)
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39 |
+
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40 |
+
# handel checkbox information
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41 |
+
prompt_id = items.iloc[idx + j]['prompt_id']
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42 |
+
modelVersion_id = items.iloc[idx + j]['modelVersion_id']
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43 |
+
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44 |
+
check_init = True if modelVersion_id in st.session_state.selected_dict.get(prompt_id, []) else False
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45 |
+
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46 |
+
# st.write("Position: ", idx + j)
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47 |
+
|
48 |
+
# show checkbox
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49 |
+
st.checkbox('Select', key=f'select_{prompt_id}_{modelVersion_id}', value=check_init)
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50 |
+
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51 |
+
# show selected info
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52 |
+
for key in info:
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53 |
+
st.write(f"**{key}**: {items.iloc[idx + j][key]}")
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54 |
+
|
55 |
+
def gallery_graph(self, items):
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+
items = load_tsne_coordinates(items)
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57 |
+
|
58 |
+
# sort items to be popularity from low to high, so that most popular ones will be on the top
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59 |
+
items = items.sort_values(by=['model_download_count'], ascending=True).reset_index(drop=True)
|
60 |
+
|
61 |
+
scale = 50
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62 |
+
items.loc[:, 'x'] = items['x'] * scale
|
63 |
+
items.loc[:, 'y'] = items['y'] * scale
|
64 |
+
|
65 |
+
nodes = []
|
66 |
+
edges = []
|
67 |
+
|
68 |
+
for idx in items.index:
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69 |
+
# if items.loc[idx, 'modelVersion_id'] in st.session_state.selected_dict.get(items.loc[idx, 'prompt_id'], 0):
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70 |
+
# opacity = 0.2
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71 |
+
# else:
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72 |
+
# opacity = 1.0
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73 |
+
|
74 |
+
nodes.append(Node(id=items.loc[idx, 'image_id'],
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75 |
+
# label=str(items.loc[idx, 'model_name']),
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76 |
+
title=f"model name: {items.loc[idx, 'model_name']}\nmodelVersion name: {items.loc[idx, 'modelVersion_name']}\nclip score: {items.loc[idx, 'clip_score']}\nmcos score: {items.loc[idx, 'mcos_score']}\npopularity: {items.loc[idx, 'model_download_count']}",
|
77 |
+
size=20,
|
78 |
+
shape='image',
|
79 |
+
image=f"https://modelcofferbucket.s3-accelerate.amazonaws.com/{items.loc[idx, 'image_id']}.png",
|
80 |
+
x=items.loc[idx, 'x'].item(),
|
81 |
+
y=items.loc[idx, 'y'].item(),
|
82 |
+
# fixed=True,
|
83 |
+
color={'background': '#E0E0E1', 'border': '#ffffff', 'highlight': {'border': '#F04542'}},
|
84 |
+
# opacity=opacity,
|
85 |
+
shadow={'enabled': True, 'color': 'rgba(0,0,0,0.4)', 'size': 10, 'x': 1, 'y': 1},
|
86 |
+
borderWidth=2,
|
87 |
+
shapeProperties={'useBorderWithImage': True},
|
88 |
+
)
|
89 |
+
)
|
90 |
+
|
91 |
+
config = Config(width='100%',
|
92 |
+
height='600',
|
93 |
+
directed=True,
|
94 |
+
physics=False,
|
95 |
+
hierarchical=False,
|
96 |
+
interaction={'navigationButtons': True, 'dragNodes': False, 'multiselect': False},
|
97 |
+
# **kwargs
|
98 |
+
)
|
99 |
+
|
100 |
+
return agraph(nodes=nodes,
|
101 |
+
edges=edges,
|
102 |
+
config=config,
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103 |
+
)
|
104 |
+
|
105 |
+
def selection_panel(self, items):
|
106 |
+
# temperal function
|
107 |
+
|
108 |
+
selecters = st.columns([1, 4])
|
109 |
+
|
110 |
+
if 'score_weights' not in st.session_state:
|
111 |
+
st.session_state.score_weights = [1.0, 0.8, 0.2, 0.8]
|
112 |
+
|
113 |
+
# select sort type
|
114 |
+
with selecters[0]:
|
115 |
+
sort_type = st.selectbox('Sort by', ['Scores', 'IDs and Names'])
|
116 |
+
if sort_type == 'Scores':
|
117 |
+
sort_by = 'weighted_score_sum'
|
118 |
+
|
119 |
+
# select other options
|
120 |
+
with selecters[1]:
|
121 |
+
if sort_type == 'IDs and Names':
|
122 |
+
sub_selecters = st.columns([3, 1])
|
123 |
+
# select sort by
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124 |
+
with sub_selecters[0]:
|
125 |
+
sort_by = st.selectbox('Sort by',
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126 |
+
['model_name', 'model_id', 'modelVersion_name', 'modelVersion_id', 'norm_nsfw'],
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127 |
+
label_visibility='hidden')
|
128 |
+
|
129 |
+
continue_idx = 1
|
130 |
+
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131 |
+
else:
|
132 |
+
# add custom weights
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133 |
+
sub_selecters = st.columns([1, 1, 1, 1])
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134 |
+
|
135 |
+
with sub_selecters[0]:
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136 |
+
clip_weight = st.number_input('Clip Score Weight', min_value=-100.0, max_value=100.0, value=1.0, step=0.1, help='the weight for normalized clip score')
|
137 |
+
with sub_selecters[1]:
|
138 |
+
mcos_weight = st.number_input('Dissimilarity Weight', min_value=-100.0, max_value=100.0, value=0.8, step=0.1, help='the weight for m(eam) s(imilarity) q(antile) score for measuring distinctiveness')
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139 |
+
with sub_selecters[2]:
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140 |
+
pop_weight = st.number_input('Popularity Weight', min_value=-100.0, max_value=100.0, value=0.2, step=0.1, help='the weight for normalized popularity score')
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141 |
+
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142 |
+
items.loc[:, 'weighted_score_sum'] = round(items[f'norm_clip'] * clip_weight + items[f'norm_mcos'] * mcos_weight + items[
|
143 |
+
'norm_pop'] * pop_weight, 4)
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144 |
+
|
145 |
+
continue_idx = 3
|
146 |
+
|
147 |
+
# save latest weights
|
148 |
+
st.session_state.score_weights[0] = round(clip_weight, 2)
|
149 |
+
st.session_state.score_weights[1] = round(mcos_weight, 2)
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150 |
+
st.session_state.score_weights[2] = round(pop_weight, 2)
|
151 |
+
|
152 |
+
# select threshold
|
153 |
+
with sub_selecters[continue_idx]:
|
154 |
+
nsfw_threshold = st.number_input('NSFW Score Threshold', min_value=0.0, max_value=1.0, value=0.8, step=0.01, help='Only show models with nsfw score lower than this threshold, set 1.0 to show all images')
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155 |
+
items = items[items['norm_nsfw'] <= nsfw_threshold].reset_index(drop=True)
|
156 |
+
|
157 |
+
# save latest threshold
|
158 |
+
st.session_state.score_weights[3] = nsfw_threshold
|
159 |
+
|
160 |
+
# draw a distribution histogram
|
161 |
+
if sort_type == 'Scores':
|
162 |
+
try:
|
163 |
+
with st.expander('Show score distribution histogram and select score range'):
|
164 |
+
st.write('**Score distribution histogram**')
|
165 |
+
chart_space = st.container()
|
166 |
+
# st.write('Select the range of scores to show')
|
167 |
+
hist_data = pd.DataFrame(items[sort_by])
|
168 |
+
mini = hist_data[sort_by].min().item()
|
169 |
+
mini = mini//0.1 * 0.1
|
170 |
+
maxi = hist_data[sort_by].max().item()
|
171 |
+
maxi = maxi//0.1 * 0.1 + 0.1
|
172 |
+
st.write('**Select the range of scores to show**')
|
173 |
+
r = st.slider('Select the range of scores to show', min_value=mini, max_value=maxi, value=(mini, maxi), step=0.05, label_visibility='collapsed')
|
174 |
+
with chart_space:
|
175 |
+
st.altair_chart(altair_histogram(hist_data, sort_by, r[0], r[1]), use_container_width=True)
|
176 |
+
# event_dict = altair_component(altair_chart=altair_histogram(hist_data, sort_by))
|
177 |
+
# r = event_dict.get(sort_by)
|
178 |
+
if r:
|
179 |
+
items = items[(items[sort_by] >= r[0]) & (items[sort_by] <= r[1])].reset_index(drop=True)
|
180 |
+
# st.write(r)
|
181 |
+
except:
|
182 |
+
pass
|
183 |
+
|
184 |
+
display_options = st.columns([1, 4])
|
185 |
+
|
186 |
+
with display_options[0]:
|
187 |
+
# select order
|
188 |
+
order = st.selectbox('Order', ['Ascending', 'Descending'], index=1 if sort_type == 'Scores' else 0)
|
189 |
+
if order == 'Ascending':
|
190 |
+
order = True
|
191 |
+
else:
|
192 |
+
order = False
|
193 |
+
|
194 |
+
with display_options[1]:
|
195 |
+
|
196 |
+
# select info to show
|
197 |
+
info = st.multiselect('Show Info',
|
198 |
+
['model_name', 'model_id', 'modelVersion_name', 'modelVersion_id',
|
199 |
+
'weighted_score_sum', 'model_download_count', 'clip_score', 'mcos_score',
|
200 |
+
'nsfw_score', 'norm_nsfw'],
|
201 |
+
default=sort_by)
|
202 |
+
|
203 |
+
# apply sorting to dataframe
|
204 |
+
items = items.sort_values(by=[sort_by], ascending=order).reset_index(drop=True)
|
205 |
+
|
206 |
+
# select number of columns
|
207 |
+
col_num = st.slider('Number of columns', min_value=1, max_value=9, value=4, step=1, key='col_num')
|
208 |
+
|
209 |
+
return items, info, col_num
|
210 |
+
|
211 |
+
def sidebar(self):
|
212 |
+
with st.sidebar:
|
213 |
+
prompt_tags = self.promptBook['tag'].unique()
|
214 |
+
# sort tags by alphabetical order
|
215 |
+
prompt_tags = np.sort(prompt_tags)[::1]
|
216 |
+
|
217 |
+
tag = st.selectbox('Select a tag', prompt_tags, index=5)
|
218 |
+
|
219 |
+
items = self.promptBook[self.promptBook['tag'] == tag].reset_index(drop=True)
|
220 |
+
|
221 |
+
prompts = np.sort(items['prompt'].unique())[::1]
|
222 |
+
|
223 |
+
selected_prompt = st.selectbox('Select prompt', prompts, index=3)
|
224 |
+
|
225 |
+
mode = st.radio('Select a mode', ['Gallery', 'Graph'], horizontal=True, index=1)
|
226 |
+
|
227 |
+
items = items[items['prompt'] == selected_prompt].reset_index(drop=True)
|
228 |
+
prompt_id = items['prompt_id'].unique()[0]
|
229 |
+
note = items['note'].unique()[0]
|
230 |
+
|
231 |
+
# show source
|
232 |
+
if isinstance(note, str):
|
233 |
+
if note.isdigit():
|
234 |
+
st.caption(f"`Source: civitai`")
|
235 |
+
else:
|
236 |
+
st.caption(f"`Source: {note}`")
|
237 |
+
else:
|
238 |
+
st.caption("`Source: Parti-prompts`")
|
239 |
+
|
240 |
+
# show image metadata
|
241 |
+
image_metadatas = ['prompt', 'negativePrompt', 'sampler', 'cfgScale', 'size', 'seed']
|
242 |
+
for key in image_metadatas:
|
243 |
+
label = ' '.join(key.split('_')).capitalize()
|
244 |
+
st.write(f"**{label}**")
|
245 |
+
if items[key][0] == ' ':
|
246 |
+
st.write('`None`')
|
247 |
+
else:
|
248 |
+
st.caption(f"{items[key][0]}")
|
249 |
+
|
250 |
+
# for note as civitai image id, add civitai reference
|
251 |
+
if isinstance(note, str) and note.isdigit():
|
252 |
+
try:
|
253 |
+
st.write(f'**[Civitai Reference](https://civitai.com/images/{note})**')
|
254 |
+
res = requests.get(f'https://civitai.com/images/{note}')
|
255 |
+
# st.write(res.text)
|
256 |
+
soup = BeautifulSoup(res.text, 'html.parser')
|
257 |
+
image_section = soup.find('div', {'class': 'mantine-12rlksp'})
|
258 |
+
image_url = image_section.find('img')['src']
|
259 |
+
st.image(image_url, use_column_width=True)
|
260 |
+
except:
|
261 |
+
pass
|
262 |
+
|
263 |
+
return prompt_tags, tag, prompt_id, items, mode
|
264 |
+
|
265 |
+
def app(self):
|
266 |
+
st.title('Model Visualization and Retrieval')
|
267 |
+
st.write('This is a gallery of images generated by the models')
|
268 |
+
|
269 |
+
prompt_tags, tag, prompt_id, items, mode = self.sidebar()
|
270 |
+
# items, info, col_num = self.selection_panel(items)
|
271 |
+
|
272 |
+
# subset = st.radio('Select a subset', ['All', 'Selected Only'], index=0, horizontal=True)
|
273 |
+
# try:
|
274 |
+
# if subset == 'Selected Only':
|
275 |
+
# items = items[items['modelVersion_id'].isin(st.session_state.selected_dict[prompt_id])].reset_index(drop=True)
|
276 |
+
# except:
|
277 |
+
# pass
|
278 |
+
|
279 |
+
# add safety check for some prompts
|
280 |
+
safety_check = True
|
281 |
+
unsafe_prompts = {}
|
282 |
+
# initialize unsafe prompts
|
283 |
+
for prompt_tag in prompt_tags:
|
284 |
+
unsafe_prompts[prompt_tag] = []
|
285 |
+
# manually add unsafe prompts
|
286 |
+
unsafe_prompts['world knowledge'] = [83]
|
287 |
+
unsafe_prompts['abstract'] = [1, 3]
|
288 |
+
|
289 |
+
if int(prompt_id.item()) in unsafe_prompts[tag]:
|
290 |
+
st.warning('This prompt may contain unsafe content. They might be offensive, depressing, or sexual.')
|
291 |
+
safety_check = st.checkbox('I understand that this prompt may contain unsafe content. Show these images anyway.', key=f'safety_{prompt_id}')
|
292 |
+
|
293 |
+
if safety_check:
|
294 |
+
if mode == 'Gallery':
|
295 |
+
self.gallery_mode(prompt_id, items)
|
296 |
+
elif mode == 'Graph':
|
297 |
+
self.graph_mode(prompt_id, items)
|
298 |
+
|
299 |
+
|
300 |
+
def graph_mode(self, prompt_id, items):
|
301 |
+
graph_cols = st.columns([3, 1])
|
302 |
+
prompt = st.chat_input(f"Selected model version ids: {str(st.session_state.selected_dict.get(prompt_id, []))}",
|
303 |
+
disabled=False, key=f'{prompt_id}')
|
304 |
+
if prompt:
|
305 |
+
switch_page("ranking")
|
306 |
+
|
307 |
+
with graph_cols[0]:
|
308 |
+
graph_space = st.empty()
|
309 |
+
|
310 |
+
with graph_space.container():
|
311 |
+
return_value = self.gallery_graph(items)
|
312 |
+
|
313 |
+
with graph_cols[1]:
|
314 |
+
if return_value:
|
315 |
+
with st.form(key=f'{prompt_id}'):
|
316 |
+
image_url = f"https://modelcofferbucket.s3-accelerate.amazonaws.com/{return_value}.png"
|
317 |
+
|
318 |
+
st.image(image_url)
|
319 |
+
|
320 |
+
item = items[items['image_id'] == return_value].reset_index(drop=True).iloc[0]
|
321 |
+
modelVersion_id = item['modelVersion_id']
|
322 |
+
|
323 |
+
# handle selection
|
324 |
+
if 'selected_dict' in st.session_state:
|
325 |
+
if item['prompt_id'] not in st.session_state.selected_dict:
|
326 |
+
st.session_state.selected_dict[item['prompt_id']] = []
|
327 |
+
|
328 |
+
if modelVersion_id in st.session_state.selected_dict[item['prompt_id']]:
|
329 |
+
checked = True
|
330 |
+
else:
|
331 |
+
checked = False
|
332 |
+
|
333 |
+
if checked:
|
334 |
+
# deselect = st.button('Deselect', key=f'select_{item["prompt_id"]}_{item["modelVersion_id"]}', use_container_width=True)
|
335 |
+
deselect = st.form_submit_button('Deselect', use_container_width=True)
|
336 |
+
if deselect:
|
337 |
+
st.session_state.selected_dict[item['prompt_id']].remove(item['modelVersion_id'])
|
338 |
+
self.remove_ranking_states(item['prompt_id'])
|
339 |
+
st.experimental_rerun()
|
340 |
+
|
341 |
+
else:
|
342 |
+
# select = st.button('Select', key=f'select_{item["prompt_id"]}_{item["modelVersion_id"]}', use_container_width=True, type='primary')
|
343 |
+
select = st.form_submit_button('Select', use_container_width=True, type='primary')
|
344 |
+
if select:
|
345 |
+
st.session_state.selected_dict[item['prompt_id']].append(item['modelVersion_id'])
|
346 |
+
self.remove_ranking_states(item['prompt_id'])
|
347 |
+
st.experimental_rerun()
|
348 |
+
|
349 |
+
# st.write(item)
|
350 |
+
infos = ['model_name', 'modelVersion_name', 'model_download_count', 'clip_score', 'mcos_score',
|
351 |
+
'nsfw_score']
|
352 |
+
|
353 |
+
infos_df = item[infos]
|
354 |
+
# rename columns
|
355 |
+
infos_df = infos_df.rename(index={'model_name': 'Model', 'modelVersion_name': 'Version', 'model_download_count': 'Downloads', 'clip_score': 'Clip Score', 'mcos_score': 'mcos Score', 'nsfw_score': 'NSFW Score'})
|
356 |
+
st.table(infos_df)
|
357 |
+
|
358 |
+
# for info in infos:
|
359 |
+
# st.write(f"**{info}**:")
|
360 |
+
# st.write(item[info])
|
361 |
+
|
362 |
+
else:
|
363 |
+
st.info('Please click on an image to show')
|
364 |
+
|
365 |
+
|
366 |
+
def gallery_mode(self, prompt_id, items):
|
367 |
+
items, info, col_num = self.selection_panel(items)
|
368 |
+
|
369 |
+
if 'selected_dict' in st.session_state:
|
370 |
+
# st.write('checked: ', str(st.session_state.selected_dict.get(prompt_id, [])))
|
371 |
+
dynamic_weight_options = ['Grid Search', 'SVM', 'Greedy']
|
372 |
+
dynamic_weight_panel = st.columns(len(dynamic_weight_options))
|
373 |
+
|
374 |
+
if len(st.session_state.selected_dict.get(prompt_id, [])) > 0:
|
375 |
+
btn_disable = False
|
376 |
+
else:
|
377 |
+
btn_disable = True
|
378 |
+
|
379 |
+
for i in range(len(dynamic_weight_options)):
|
380 |
+
method = dynamic_weight_options[i]
|
381 |
+
with dynamic_weight_panel[i]:
|
382 |
+
btn = st.button(method, use_container_width=True, disabled=btn_disable, on_click=self.dynamic_weight, args=(prompt_id, items, method))
|
383 |
+
|
384 |
+
prompt = st.chat_input(f"Selected model version ids: {str(st.session_state.selected_dict.get(prompt_id, []))}", disabled=False, key=f'{prompt_id}')
|
385 |
+
if prompt:
|
386 |
+
switch_page("ranking")
|
387 |
+
|
388 |
+
with st.form(key=f'{prompt_id}'):
|
389 |
+
# buttons = st.columns([1, 1, 1])
|
390 |
+
buttons_space = st.columns([1, 1, 1, 1])
|
391 |
+
gallery_space = st.empty()
|
392 |
+
|
393 |
+
with buttons_space[0]:
|
394 |
+
continue_btn = st.form_submit_button('Confirm Selection', use_container_width=True, type='primary')
|
395 |
+
if continue_btn:
|
396 |
+
self.submit_actions('Continue', prompt_id)
|
397 |
+
|
398 |
+
with buttons_space[1]:
|
399 |
+
select_btn = st.form_submit_button('Select All', use_container_width=True)
|
400 |
+
if select_btn:
|
401 |
+
self.submit_actions('Select', prompt_id)
|
402 |
+
|
403 |
+
with buttons_space[2]:
|
404 |
+
deselect_btn = st.form_submit_button('Deselect All', use_container_width=True)
|
405 |
+
if deselect_btn:
|
406 |
+
self.submit_actions('Deselect', prompt_id)
|
407 |
+
|
408 |
+
with buttons_space[3]:
|
409 |
+
refresh_btn = st.form_submit_button('Refresh', on_click=gallery_space.empty, use_container_width=True)
|
410 |
+
|
411 |
+
with gallery_space.container():
|
412 |
+
with st.spinner('Loading images...'):
|
413 |
+
self.gallery_standard(items, col_num, info)
|
414 |
+
|
415 |
+
st.info("Don't forget to scroll back to top and click the 'Confirm Selection' button to save your selection!!!")
|
416 |
+
|
417 |
+
|
418 |
+
|
419 |
+
def submit_actions(self, status, prompt_id):
|
420 |
+
# remove counter from session state
|
421 |
+
# st.session_state.pop('counter', None)
|
422 |
+
self.remove_ranking_states('prompt_id')
|
423 |
+
if status == 'Select':
|
424 |
+
modelVersions = self.promptBook[self.promptBook['prompt_id'] == prompt_id]['modelVersion_id'].unique()
|
425 |
+
st.session_state.selected_dict[prompt_id] = modelVersions.tolist()
|
426 |
+
print(st.session_state.selected_dict, 'select')
|
427 |
+
st.experimental_rerun()
|
428 |
+
elif status == 'Deselect':
|
429 |
+
st.session_state.selected_dict[prompt_id] = []
|
430 |
+
print(st.session_state.selected_dict, 'deselect')
|
431 |
+
st.experimental_rerun()
|
432 |
+
# self.promptBook.loc[self.promptBook['prompt_id'] == prompt_id, 'checked'] = False
|
433 |
+
elif status == 'Continue':
|
434 |
+
st.session_state.selected_dict[prompt_id] = []
|
435 |
+
for key in st.session_state:
|
436 |
+
keys = key.split('_')
|
437 |
+
if keys[0] == 'select' and keys[1] == str(prompt_id):
|
438 |
+
if st.session_state[key]:
|
439 |
+
st.session_state.selected_dict[prompt_id].append(int(keys[2]))
|
440 |
+
# switch_page("ranking")
|
441 |
+
print(st.session_state.selected_dict, 'continue')
|
442 |
+
st.experimental_rerun()
|
443 |
+
|
444 |
+
def dynamic_weight(self, prompt_id, items, method='Grid Search'):
|
445 |
+
selected = items[
|
446 |
+
items['modelVersion_id'].isin(st.session_state.selected_dict[prompt_id])].reset_index(drop=True)
|
447 |
+
optimal_weight = [0, 0, 0]
|
448 |
+
|
449 |
+
if method == 'Grid Search':
|
450 |
+
# grid search method
|
451 |
+
top_ranking = len(items) * len(selected)
|
452 |
+
|
453 |
+
for clip_weight in np.arange(-1, 1, 0.1):
|
454 |
+
for mcos_weight in np.arange(-1, 1, 0.1):
|
455 |
+
for pop_weight in np.arange(-1, 1, 0.1):
|
456 |
+
|
457 |
+
weight_all = clip_weight*items[f'norm_clip'] + mcos_weight*items[f'norm_mcos'] + pop_weight*items['norm_pop']
|
458 |
+
weight_all_sorted = weight_all.sort_values(ascending=False).reset_index(drop=True)
|
459 |
+
# print('weight_all_sorted:', weight_all_sorted)
|
460 |
+
weight_selected = clip_weight*selected[f'norm_clip'] + mcos_weight*selected[f'norm_mcos'] + pop_weight*selected['norm_pop']
|
461 |
+
|
462 |
+
# get the index of values of weight_selected in weight_all_sorted
|
463 |
+
rankings = []
|
464 |
+
for weight in weight_selected:
|
465 |
+
rankings.append(weight_all_sorted.index[weight_all_sorted == weight].tolist()[0])
|
466 |
+
if sum(rankings) <= top_ranking:
|
467 |
+
top_ranking = sum(rankings)
|
468 |
+
print('current top ranking:', top_ranking, rankings)
|
469 |
+
optimal_weight = [clip_weight, mcos_weight, pop_weight]
|
470 |
+
print('optimal weight:', optimal_weight)
|
471 |
+
|
472 |
+
elif method == 'SVM':
|
473 |
+
# svm method
|
474 |
+
print('start svm method')
|
475 |
+
# get residual dataframe that contains models not selected
|
476 |
+
residual = items[~items['modelVersion_id'].isin(selected['modelVersion_id'])].reset_index(drop=True)
|
477 |
+
residual = residual[['norm_clip_crop', 'norm_mcos_crop', 'norm_pop']]
|
478 |
+
residual = residual.to_numpy()
|
479 |
+
selected = selected[['norm_clip_crop', 'norm_mcos_crop', 'norm_pop']]
|
480 |
+
selected = selected.to_numpy()
|
481 |
+
|
482 |
+
y = np.concatenate((np.full((len(selected), 1), -1), np.full((len(residual), 1), 1)), axis=0).ravel()
|
483 |
+
X = np.concatenate((selected, residual), axis=0)
|
484 |
+
|
485 |
+
# fit svm model, and get parameters for the hyperplane
|
486 |
+
clf = LinearSVC(random_state=0, C=1.0, fit_intercept=False, dual='auto')
|
487 |
+
clf.fit(X, y)
|
488 |
+
optimal_weight = clf.coef_[0].tolist()
|
489 |
+
print('optimal weight:', optimal_weight)
|
490 |
+
pass
|
491 |
+
|
492 |
+
elif method == 'Greedy':
|
493 |
+
for idx in selected.index:
|
494 |
+
# find which score is the highest, clip, mcos, or pop
|
495 |
+
clip_score = selected.loc[idx, 'norm_clip_crop']
|
496 |
+
mcos_score = selected.loc[idx, 'norm_mcos_crop']
|
497 |
+
pop_score = selected.loc[idx, 'norm_pop']
|
498 |
+
if clip_score >= mcos_score and clip_score >= pop_score:
|
499 |
+
optimal_weight[0] += 1
|
500 |
+
elif mcos_score >= clip_score and mcos_score >= pop_score:
|
501 |
+
optimal_weight[1] += 1
|
502 |
+
elif pop_score >= clip_score and pop_score >= mcos_score:
|
503 |
+
optimal_weight[2] += 1
|
504 |
+
|
505 |
+
# normalize optimal_weight
|
506 |
+
optimal_weight = [round(weight/len(selected), 2) for weight in optimal_weight]
|
507 |
+
print('optimal weight:', optimal_weight)
|
508 |
+
print('optimal weight:', optimal_weight)
|
509 |
+
|
510 |
+
st.session_state.score_weights[0: 3] = optimal_weight
|
511 |
+
|
512 |
+
|
513 |
+
def remove_ranking_states(self, prompt_id):
|
514 |
+
# for drag sort
|
515 |
+
try:
|
516 |
+
st.session_state.counter[prompt_id] = 0
|
517 |
+
st.session_state.ranking[prompt_id] = {}
|
518 |
+
print('remove ranking states')
|
519 |
+
except:
|
520 |
+
print('no sort ranking states to remove')
|
521 |
+
|
522 |
+
# for battles
|
523 |
+
try:
|
524 |
+
st.session_state.pointer[prompt_id] = {'left': 0, 'right': 1}
|
525 |
+
print('remove battles states')
|
526 |
+
except:
|
527 |
+
print('no battles states to remove')
|
528 |
+
|
529 |
+
# for page progress
|
530 |
+
try:
|
531 |
+
st.session_state.progress[prompt_id] = 'ranking'
|
532 |
+
print('reset page progress states')
|
533 |
+
except:
|
534 |
+
print('no page progress states to be reset')
|
535 |
+
|
536 |
+
|
537 |
+
# hist_data = pd.DataFrame(np.random.normal(42, 10, (200, 1)), columns=["x"])
|
538 |
+
@st.cache_resource
|
539 |
+
def altair_histogram(hist_data, sort_by, mini, maxi):
|
540 |
+
brushed = alt.selection_interval(encodings=['x'], name="brushed")
|
541 |
+
|
542 |
+
chart = (
|
543 |
+
alt.Chart(hist_data)
|
544 |
+
.mark_bar(opacity=0.7, cornerRadius=2)
|
545 |
+
.encode(alt.X(f"{sort_by}:Q", bin=alt.Bin(maxbins=25)), y="count()")
|
546 |
+
# .add_selection(brushed)
|
547 |
+
# .properties(width=800, height=300)
|
548 |
+
)
|
549 |
+
|
550 |
+
# Create a transparent rectangle for highlighting the range
|
551 |
+
highlight = (
|
552 |
+
alt.Chart(pd.DataFrame({'x1': [mini], 'x2': [maxi]}))
|
553 |
+
.mark_rect(opacity=0.3)
|
554 |
+
.encode(x='x1', x2='x2')
|
555 |
+
# .properties(width=800, height=300)
|
556 |
+
)
|
557 |
+
|
558 |
+
# Layer the chart and the highlight rectangle
|
559 |
+
layered_chart = alt.layer(chart, highlight)
|
560 |
+
|
561 |
+
return layered_chart
|
562 |
+
|
563 |
+
|
564 |
+
@st.cache_data
|
565 |
+
def load_hf_dataset():
|
566 |
+
# login to huggingface
|
567 |
+
login(token=os.environ.get("HF_TOKEN"))
|
568 |
+
|
569 |
+
# load from huggingface
|
570 |
+
roster = pd.DataFrame(load_dataset('MAPS-research/GEMRec-Roster', split='train'))
|
571 |
+
promptBook = pd.DataFrame(load_dataset('MAPS-research/GEMRec-Metadata', split='train'))
|
572 |
+
# images_ds = load_from_disk(os.path.join(os.getcwd(), 'data', 'promptbook'))
|
573 |
+
images_ds = None # set to None for now since we use s3 bucket to store images
|
574 |
+
|
575 |
+
# # process dataset
|
576 |
+
# roster = roster[['model_id', 'model_name', 'modelVersion_id', 'modelVersion_name',
|
577 |
+
# 'model_download_count']].drop_duplicates().reset_index(drop=True)
|
578 |
+
|
579 |
+
# add 'custom_score_weights' column to promptBook if not exist
|
580 |
+
if 'weighted_score_sum' not in promptBook.columns:
|
581 |
+
promptBook.loc[:, 'weighted_score_sum'] = 0
|
582 |
+
|
583 |
+
# merge roster and promptbook
|
584 |
+
promptBook = promptBook.merge(roster[['model_id', 'model_name', 'modelVersion_id', 'modelVersion_name', 'model_download_count']],
|
585 |
+
on=['model_id', 'modelVersion_id'], how='left')
|
586 |
+
|
587 |
+
# add column to record current row index
|
588 |
+
promptBook.loc[:, 'row_idx'] = promptBook.index
|
589 |
+
|
590 |
+
# apply a nsfw filter
|
591 |
+
promptBook = promptBook[promptBook['nsfw_score'] <= 0.84].reset_index(drop=True)
|
592 |
+
|
593 |
+
# add a column that adds up 'norm_clip', 'norm_mcos', and 'norm_pop'
|
594 |
+
score_weights = [1.0, 0.8, 0.2]
|
595 |
+
promptBook.loc[:, 'total_score'] = round(promptBook['norm_clip'] * score_weights[0] + promptBook['norm_mcos'] * score_weights[1] + promptBook['norm_pop'] * score_weights[2], 4)
|
596 |
+
|
597 |
+
return roster, promptBook, images_ds
|
598 |
+
|
599 |
+
@st.cache_data
|
600 |
+
def load_tsne_coordinates(items):
|
601 |
+
# load tsne coordinates
|
602 |
+
tsne_df = pd.read_parquet('./data/feats_tsne.parquet')
|
603 |
+
|
604 |
+
# print(tsne_df['modelVersion_id'].dtype)
|
605 |
+
|
606 |
+
print('before merge:', items)
|
607 |
+
items = items.merge(tsne_df, on=['modelVersion_id', 'prompt_id'], how='left')
|
608 |
+
print('after merge:', items)
|
609 |
+
return items
|
610 |
+
|
611 |
+
|
612 |
+
if __name__ == "__main__":
|
613 |
+
st.set_page_config(page_title="Model Coffer Gallery", page_icon="🖼️", layout="wide")
|
614 |
+
|
615 |
+
if 'user_id' not in st.session_state:
|
616 |
+
st.warning('Please log in first.')
|
617 |
+
home_btn = st.button('Go to Home Page')
|
618 |
+
if home_btn:
|
619 |
+
switch_page("home")
|
620 |
+
else:
|
621 |
+
# st.write('You have already logged in as ' + st.session_state.user_id[0])
|
622 |
+
roster, promptBook, images_ds = load_hf_dataset()
|
623 |
+
# print(promptBook.columns)
|
624 |
+
|
625 |
+
# initialize selected_dict
|
626 |
+
if 'selected_dict' not in st.session_state:
|
627 |
+
st.session_state['selected_dict'] = {}
|
628 |
+
|
629 |
+
app = GalleryApp(promptBook=promptBook, images_ds=images_ds)
|
630 |
+
app.app()
|
631 |
+
|
632 |
+
# components.html(
|
633 |
+
# """
|
634 |
+
# <script>
|
635 |
+
# var iframe = window.parent.document.querySelector('[title="streamlit_agraph.agraph"]');
|
636 |
+
# console.log(iframe);
|
637 |
+
# var targetElement = iframe.contentDocument.querySelector('div.vis-network div.vis-navigation div.vis-button.vis-zoomExtends');
|
638 |
+
# console.log(targetElement);
|
639 |
+
# targetElement.style.background-image = "url(https://www.flaticon.com/free-icon-font/menu-burger_3917215?related_id=3917215#)";
|
640 |
+
# </script>
|
641 |
+
# """,
|
642 |
+
# # unsafe_allow_html=True,
|
643 |
+
# )
|
Archive/agraphTest.py
DELETED
@@ -1,170 +0,0 @@
|
|
1 |
-
import os
|
2 |
-
|
3 |
-
import streamlit as st
|
4 |
-
import torch
|
5 |
-
import pandas as pd
|
6 |
-
import numpy as np
|
7 |
-
|
8 |
-
from datasets import load_dataset, Dataset, load_from_disk
|
9 |
-
from huggingface_hub import login
|
10 |
-
from streamlit_agraph import agraph, Node, Edge, Config
|
11 |
-
from sklearn.manifold import TSNE
|
12 |
-
|
13 |
-
|
14 |
-
@st.cache_data
|
15 |
-
def load_hf_dataset():
|
16 |
-
# login to huggingface
|
17 |
-
login(token=os.environ.get("HF_TOKEN"))
|
18 |
-
|
19 |
-
# load from huggingface
|
20 |
-
roster = pd.DataFrame(load_dataset('MAPS-research/GEMRec-Roster', split='train'))
|
21 |
-
promptBook = pd.DataFrame(load_dataset('MAPS-research/GEMRec-Metadata', split='train'))
|
22 |
-
|
23 |
-
# process dataset
|
24 |
-
roster = roster[['model_id', 'model_name', 'modelVersion_id', 'modelVersion_name',
|
25 |
-
'model_download_count']].drop_duplicates().reset_index(drop=True)
|
26 |
-
|
27 |
-
# add 'custom_score_weights' column to promptBook if not exist
|
28 |
-
if 'weighted_score_sum' not in promptBook.columns:
|
29 |
-
promptBook.loc[:, 'weighted_score_sum'] = 0
|
30 |
-
|
31 |
-
# merge roster and promptbook
|
32 |
-
promptBook = promptBook.merge(roster[['model_id', 'model_name', 'modelVersion_id', 'modelVersion_name', 'model_download_count']],
|
33 |
-
on=['model_id', 'modelVersion_id'], how='left')
|
34 |
-
|
35 |
-
# add column to record current row index
|
36 |
-
promptBook.loc[:, 'row_idx'] = promptBook.index
|
37 |
-
|
38 |
-
return roster, promptBook
|
39 |
-
|
40 |
-
|
41 |
-
@st.cache_data
|
42 |
-
def calc_tsne(prompt_id):
|
43 |
-
print('==> loading feats')
|
44 |
-
feats = {}
|
45 |
-
for pt in os.listdir('../data/feats'):
|
46 |
-
if pt.split('.')[-1] == 'pt' and pt.split('.')[0].isdigit():
|
47 |
-
feats[pt.split('.')[0]] = torch.load(os.path.join('../data/feats', pt))
|
48 |
-
|
49 |
-
print('==> applying t-SNE')
|
50 |
-
# apply t-SNE to entries in each feat in feats to get 2D coordinates
|
51 |
-
tsne = TSNE(n_components=2, random_state=0)
|
52 |
-
# for k, v in tqdm(feats.items()):
|
53 |
-
# feats[k]['tsne'] = tsne.fit_transform(v['all'].numpy())
|
54 |
-
# prompt_id = '90'
|
55 |
-
feats[prompt_id]['tsne'] = tsne.fit_transform(feats[prompt_id]['all'].numpy())
|
56 |
-
|
57 |
-
feats_df = pd.DataFrame(feats[prompt_id]['tsne'], columns=['x', 'y'])
|
58 |
-
feats_df['prompt_id'] = prompt_id
|
59 |
-
|
60 |
-
keys = []
|
61 |
-
for k in feats[prompt_id].keys():
|
62 |
-
if k != 'all' and k != 'tsne':
|
63 |
-
keys.append(int(k.item()))
|
64 |
-
|
65 |
-
feats_df['modelVersion_id'] = keys
|
66 |
-
|
67 |
-
|
68 |
-
return feats_df
|
69 |
-
|
70 |
-
# print(feats[prompt_id]['tsne'])
|
71 |
-
|
72 |
-
|
73 |
-
if __name__ == '__main__':
|
74 |
-
st.set_page_config(layout="wide")
|
75 |
-
|
76 |
-
# load dataset
|
77 |
-
roster, promptBook = load_hf_dataset()
|
78 |
-
# prompt_id = '20'
|
79 |
-
|
80 |
-
with st.sidebar:
|
81 |
-
st.write('## Select Prompt')
|
82 |
-
prompts = promptBook['prompt_id'].unique().tolist()
|
83 |
-
# sort prompts by prompt_id
|
84 |
-
prompts.sort()
|
85 |
-
prompt_id = st.selectbox('Select Prompt', prompts, index=0)
|
86 |
-
physics = st.checkbox('Enable Physics')
|
87 |
-
|
88 |
-
feats_df = calc_tsne(str(prompt_id))
|
89 |
-
|
90 |
-
# keys = []
|
91 |
-
# for k in feats[prompt_id].keys():
|
92 |
-
# if k != 'all' and k != 'tsne':
|
93 |
-
# keys.append(int(k.item()))
|
94 |
-
|
95 |
-
# print(keys)
|
96 |
-
|
97 |
-
data = []
|
98 |
-
for idx in feats_df.index:
|
99 |
-
modelVersion_id = feats_df.loc[idx, 'modelVersion_id']
|
100 |
-
image_id = promptBook[(promptBook['modelVersion_id'] == modelVersion_id) & (
|
101 |
-
promptBook['prompt_id'] == int(prompt_id))].reset_index(drop=True).loc[0, 'image_id']
|
102 |
-
image_url = f"https://modelcofferbucket.s3-accelerate.amazonaws.com/{image_id}.png"
|
103 |
-
scale = 50
|
104 |
-
data.append((feats_df.loc[idx, 'x'] * scale, feats_df.loc[idx, 'y'] * scale, image_url))
|
105 |
-
|
106 |
-
image_size = promptBook[(promptBook['image_id'] == image_id)].reset_index(drop=True).loc[0, 'size'].split('x')
|
107 |
-
|
108 |
-
nodes = []
|
109 |
-
edges = []
|
110 |
-
|
111 |
-
for d in data:
|
112 |
-
nodes.append( Node(id=d[2],
|
113 |
-
# label=str(items.loc[idx, 'model_name']),
|
114 |
-
size=20,
|
115 |
-
shape="image",
|
116 |
-
image=d[2],
|
117 |
-
x=[d[0]],
|
118 |
-
y=[d[1]],
|
119 |
-
fixed=False if physics else True,
|
120 |
-
color={'background': '#00000', 'border': '#ffffff'},
|
121 |
-
shadow={'enabled': True, 'color': 'rgba(0,0,0,0.4)', 'size': 10, 'x': 1, 'y': 1},
|
122 |
-
# borderWidth=1,
|
123 |
-
# shapeProperties={'useBorderWithImage': True},
|
124 |
-
)
|
125 |
-
)
|
126 |
-
|
127 |
-
|
128 |
-
# nodes.append( Node(id="Spiderman",
|
129 |
-
# label="Peter Parker",
|
130 |
-
# size=25,
|
131 |
-
# shape="circularImage",
|
132 |
-
# image="http://marvel-force-chart.surge.sh/marvel_force_chart_img/top_spiderman.png")
|
133 |
-
# ) # includes **kwargs
|
134 |
-
# nodes.append( Node(id="Captain_Marvel",
|
135 |
-
# label="Carol Danvers",
|
136 |
-
# fixed=True,
|
137 |
-
# size=25,
|
138 |
-
# shape="circularImage",
|
139 |
-
# image="http://marvel-force-chart.surge.sh/marvel_force_chart_img/top_captainmarvel.png")
|
140 |
-
# )
|
141 |
-
# edges.append( Edge(source="Captain_Marvel",
|
142 |
-
# label="friend_of",
|
143 |
-
# target="Spiderman",
|
144 |
-
# length=200,
|
145 |
-
# # **kwargs
|
146 |
-
# )
|
147 |
-
# )
|
148 |
-
#
|
149 |
-
config = Config(width='100%',
|
150 |
-
height=800,
|
151 |
-
directed=True,
|
152 |
-
physics=physics,
|
153 |
-
hierarchical=False,
|
154 |
-
# **kwargs
|
155 |
-
)
|
156 |
-
|
157 |
-
cols = st.columns([3, 1], gap='large')
|
158 |
-
|
159 |
-
with cols[0]:
|
160 |
-
return_value = agraph(nodes=nodes,
|
161 |
-
edges=edges,
|
162 |
-
config=config)
|
163 |
-
|
164 |
-
# st.write(return_value)
|
165 |
-
|
166 |
-
with cols[1]:
|
167 |
-
try:
|
168 |
-
st.image(return_value, use_column_width=True)
|
169 |
-
except:
|
170 |
-
st.write('No image selected')
|
|
|
|
|
|
|
|
|
|
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|
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|
Archive/bokehTest.py
DELETED
@@ -1,182 +0,0 @@
|
|
1 |
-
import os
|
2 |
-
|
3 |
-
import streamlit as st
|
4 |
-
import torch
|
5 |
-
import pandas as pd
|
6 |
-
import numpy as np
|
7 |
-
import requests
|
8 |
-
|
9 |
-
from bokeh.plotting import figure, show
|
10 |
-
from bokeh.models import HoverTool, ColumnDataSource, CustomJSHover
|
11 |
-
from bokeh.embed import file_html
|
12 |
-
from bokeh.resources import CDN # Import CDN here
|
13 |
-
from datasets import load_dataset, Dataset, load_from_disk
|
14 |
-
from huggingface_hub import login
|
15 |
-
from sklearn.manifold import TSNE
|
16 |
-
from tqdm import tqdm
|
17 |
-
|
18 |
-
|
19 |
-
@st.cache_data
|
20 |
-
def load_hf_dataset():
|
21 |
-
# login to huggingface
|
22 |
-
login(token=os.environ.get("HF_TOKEN"))
|
23 |
-
|
24 |
-
# load from huggingface
|
25 |
-
roster = pd.DataFrame(load_dataset('MAPS-research/GEMRec-Roster', split='train'))
|
26 |
-
promptBook = pd.DataFrame(load_dataset('MAPS-research/GEMRec-Metadata', split='train'))
|
27 |
-
|
28 |
-
# process dataset
|
29 |
-
roster = roster[['model_id', 'model_name', 'modelVersion_id', 'modelVersion_name',
|
30 |
-
'model_download_count']].drop_duplicates().reset_index(drop=True)
|
31 |
-
|
32 |
-
# add 'custom_score_weights' column to promptBook if not exist
|
33 |
-
if 'weighted_score_sum' not in promptBook.columns:
|
34 |
-
promptBook.loc[:, 'weighted_score_sum'] = 0
|
35 |
-
|
36 |
-
# merge roster and promptbook
|
37 |
-
promptBook = promptBook.merge(roster[['model_id', 'model_name', 'modelVersion_id', 'modelVersion_name', 'model_download_count']],
|
38 |
-
on=['model_id', 'modelVersion_id'], how='left')
|
39 |
-
|
40 |
-
# add column to record current row index
|
41 |
-
promptBook.loc[:, 'row_idx'] = promptBook.index
|
42 |
-
|
43 |
-
return roster, promptBook
|
44 |
-
|
45 |
-
def show_with_bokeh(data, streamlit=False):
|
46 |
-
# Extract x, y coordinates and image URLs
|
47 |
-
x_coords, y_coords, image_urls = zip(*data)
|
48 |
-
|
49 |
-
# Create a ColumnDataSource
|
50 |
-
source = ColumnDataSource(data=dict(x=x_coords, y=y_coords, image=image_urls))
|
51 |
-
|
52 |
-
# Create a figure
|
53 |
-
p = figure(width=800, height=600)
|
54 |
-
|
55 |
-
# Add scatter plot
|
56 |
-
scatter = p.scatter(x='x', y='y', size=20, source=source)
|
57 |
-
|
58 |
-
# Define hover tool
|
59 |
-
hover = HoverTool()
|
60 |
-
# hover.tooltips = """
|
61 |
-
# <div>
|
62 |
-
# <iframe src="@image" width="512" height="512"></iframe>
|
63 |
-
# </div>
|
64 |
-
# """
|
65 |
-
# hover.formatters = {'@image': CustomJSHover(code="""
|
66 |
-
# const index = cb_data.index;
|
67 |
-
# const url = cb_data.source.data['image'][index];
|
68 |
-
# return '<iframe src="' + url + '" width="512" height="512"></iframe>';
|
69 |
-
# """)}
|
70 |
-
|
71 |
-
hover.tooltips = """
|
72 |
-
<div>
|
73 |
-
<img src="@image" style='object-fit: contain'; height=100%">
|
74 |
-
</div>
|
75 |
-
"""
|
76 |
-
hover.formatters = {'@image': CustomJSHover(code="""
|
77 |
-
const index = cb_data.index;
|
78 |
-
const url = cb_data.source.data['image'][index];
|
79 |
-
return '<img src="' + url + '">';
|
80 |
-
""")}
|
81 |
-
|
82 |
-
p.add_tools(hover)
|
83 |
-
|
84 |
-
# Generate HTML with the plot
|
85 |
-
html = file_html(p, CDN, "Interactive Scatter Plot with Hover Images")
|
86 |
-
|
87 |
-
# Save the HTML file or show it
|
88 |
-
# with open("scatter_plot_with_hover_images.html", "w") as f:
|
89 |
-
# f.write(html)
|
90 |
-
|
91 |
-
if streamlit:
|
92 |
-
st.bokeh_chart(p, use_container_width=True)
|
93 |
-
else:
|
94 |
-
show(p)
|
95 |
-
|
96 |
-
|
97 |
-
def show_with_bokeh_2(data, image_size=[40, 40], streamlit=False):
|
98 |
-
# Extract x, y coordinates and image URLs
|
99 |
-
x_coords, y_coords, image_urls = zip(*data)
|
100 |
-
|
101 |
-
# Create a ColumnDataSource
|
102 |
-
source = ColumnDataSource(data=dict(x=x_coords, y=y_coords, image=image_urls))
|
103 |
-
|
104 |
-
# Create a figure
|
105 |
-
p = figure(width=800, height=600, aspect_ratio=1.0)
|
106 |
-
|
107 |
-
# Add image glyphs
|
108 |
-
# image_size = 40 # Adjust this size as needed
|
109 |
-
scale = 0.1
|
110 |
-
image_size = [int(image_size[0])*scale, int(image_size[1])*scale]
|
111 |
-
print(image_size)
|
112 |
-
p.image_url(url='image', x='x', y='y', source=source, w=image_size[0], h=image_size[1], anchor="center")
|
113 |
-
|
114 |
-
# Define hover tool
|
115 |
-
hover = HoverTool()
|
116 |
-
hover.tooltips = """
|
117 |
-
<div>
|
118 |
-
<img src="@image" style='object-fit: contain'; height=100%'">
|
119 |
-
</div>
|
120 |
-
"""
|
121 |
-
p.add_tools(hover)
|
122 |
-
|
123 |
-
# Generate HTML with the plot
|
124 |
-
html = file_html(p, CDN, "Scatter Plot with Images")
|
125 |
-
|
126 |
-
# Save the HTML file or show it
|
127 |
-
# with open("scatter_plot_with_images.html", "w") as f:
|
128 |
-
# f.write(html)
|
129 |
-
|
130 |
-
if streamlit:
|
131 |
-
st.bokeh_chart(p, use_container_width=True)
|
132 |
-
else:
|
133 |
-
show(p)
|
134 |
-
|
135 |
-
|
136 |
-
if __name__ == '__main__':
|
137 |
-
# load dataset
|
138 |
-
roster, promptBook = load_hf_dataset()
|
139 |
-
|
140 |
-
print('==> loading feats')
|
141 |
-
feats = {}
|
142 |
-
for pt in os.listdir('../data/feats'):
|
143 |
-
if pt.split('.')[-1] == 'pt' and pt.split('.')[0].isdigit():
|
144 |
-
feats[pt.split('.')[0]] = torch.load(os.path.join('../data/feats', pt))
|
145 |
-
|
146 |
-
print('==> applying t-SNE')
|
147 |
-
# apply t-SNE to entries in each feat in feats to get 2D coordinates
|
148 |
-
tsne = TSNE(n_components=2, random_state=0)
|
149 |
-
# for k, v in tqdm(feats.items()):
|
150 |
-
# feats[k]['tsne'] = tsne.fit_transform(v['all'].numpy())
|
151 |
-
prompt_id = '49'
|
152 |
-
feats[prompt_id]['tsne'] = tsne.fit_transform(feats[prompt_id]['all'].numpy())
|
153 |
-
|
154 |
-
print(feats[prompt_id]['tsne'])
|
155 |
-
|
156 |
-
keys = []
|
157 |
-
for k in feats[prompt_id].keys():
|
158 |
-
if k != 'all' and k != 'tsne':
|
159 |
-
keys.append(int(k.item()))
|
160 |
-
|
161 |
-
print(keys)
|
162 |
-
|
163 |
-
data = []
|
164 |
-
for idx in range(len(keys)):
|
165 |
-
modelVersion_id = keys[idx]
|
166 |
-
image_id = promptBook[(promptBook['modelVersion_id'] == modelVersion_id) & (promptBook['prompt_id'] == int(prompt_id))].reset_index(drop=True).loc[0, 'image_id']
|
167 |
-
image_url = f"https://modelcofferbucket.s3-accelerate.amazonaws.com/{image_id}.png"
|
168 |
-
scale = 50
|
169 |
-
data.append((feats[prompt_id]['tsne'][idx][0]*scale, feats[prompt_id]['tsne'][idx][1]*scale, image_url))
|
170 |
-
|
171 |
-
image_size = promptBook[(promptBook['image_id'] == image_id)].reset_index(drop=True).loc[0, 'size'].split('x')
|
172 |
-
|
173 |
-
# # Sample data: (x, y) coordinates and corresponding image URLs
|
174 |
-
# data = [
|
175 |
-
# (2, 5, "https://www.crunchyroll.com/imgsrv/display/thumbnail/480x720/catalog/crunchyroll/669dae5dbea3d93bb5f1012078501976.jpeg"),
|
176 |
-
# (4, 8, "https://i.pinimg.com/originals/40/6d/38/406d38957bc4fd12f34c5dfa3d73b86d.jpg"),
|
177 |
-
# (7, 3, "https://i.pinimg.com/550x/76/27/d2/7627d227adc6fb5fb6662ebfb9d82d7e.jpg"),
|
178 |
-
# # Add more data points and image URLs
|
179 |
-
# ]
|
180 |
-
|
181 |
-
# show_with_bokeh(data, streamlit=True)
|
182 |
-
show_with_bokeh_2(data, image_size=image_size, streamlit=True)
|
|
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|
Archive/optimization.py
DELETED
@@ -1,37 +0,0 @@
|
|
1 |
-
import numpy as np
|
2 |
-
from scipy.optimize import minimize, differential_evolution
|
3 |
-
|
4 |
-
|
5 |
-
# Define the function y = x_1*w_1 + x_2*w_2 + x_3*w_3
|
6 |
-
def objective_function(w_indices):
|
7 |
-
x_1 = x_1_values[int(w_indices[0])]
|
8 |
-
x_2 = x_2_values[int(w_indices[1])]
|
9 |
-
x_3 = x_3_values[int(w_indices[2])]
|
10 |
-
return - (x_1 * w_indices[3] + x_2 * w_indices[4] + x_3 * w_indices[5]) # Use w_indices to get w_1, w_2, w_3
|
11 |
-
|
12 |
-
|
13 |
-
if __name__ == '__main__':
|
14 |
-
# Given sets of discrete values for x_1, x_2, and x_3
|
15 |
-
x_1_values = [1, 2, 3, 5, 6]
|
16 |
-
x_2_values = [0, 5, 7, 2, 1]
|
17 |
-
x_3_values = [3, 7, 4, 5, 2]
|
18 |
-
|
19 |
-
# Perform differential evolution optimization with integer variables
|
20 |
-
# bounds = [(0, len(x_1_values) - 2), (0, len(x_2_values) - 1), (0, len(x_3_values) - 1), (-1, 1), (-1, 1), (-1, 1)]
|
21 |
-
bounds = [(3, 4), (3, 4), (3, 4), (-1, 1), (-1, 1), (-1, 1)]
|
22 |
-
result = differential_evolution(objective_function, bounds)
|
23 |
-
|
24 |
-
# Get the optimal indices of x_1, x_2, and x_3
|
25 |
-
x_1_index, x_2_index, x_3_index, w_1_opt, w_2_opt, w_3_opt = result.x
|
26 |
-
|
27 |
-
# Calculate the peak point (x_1, x_2, x_3) corresponding to the optimal indices
|
28 |
-
x_1_peak = x_1_values[int(x_1_index)]
|
29 |
-
x_2_peak = x_2_values[int(x_2_index)]
|
30 |
-
x_3_peak = x_3_values[int(x_3_index)]
|
31 |
-
|
32 |
-
# Print the results
|
33 |
-
print("Optimal w_1:", w_1_opt)
|
34 |
-
print("Optimal w_2:", w_2_opt)
|
35 |
-
print("Optimal w_3:", w_3_opt)
|
36 |
-
print("Peak Point (x_1, x_2, x_3):", (x_1_peak, x_2_peak, x_3_peak))
|
37 |
-
print("Maximum Value of y:", -result.fun) # Use negative sign as we previously used to maximize
|
|
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|
Archive/optimization2.py
DELETED
@@ -1,40 +0,0 @@
|
|
1 |
-
import numpy as np
|
2 |
-
from scipy.optimize import minimize
|
3 |
-
|
4 |
-
if __name__ == '__main__':
|
5 |
-
|
6 |
-
# Given subset of m values for x_1, x_2, and x_3
|
7 |
-
x1_subset = [2, 3, 4]
|
8 |
-
x2_subset = [0, 1]
|
9 |
-
x3_subset = [5, 6, 7]
|
10 |
-
|
11 |
-
# Full set of possible values for x_1, x_2, and x_3
|
12 |
-
x1_full = [1, 2, 3, 4, 5]
|
13 |
-
x2_full = [0, 1, 2, 3, 4, 5]
|
14 |
-
x3_full = [3, 5, 7]
|
15 |
-
|
16 |
-
# Define the objective function for quantile-based ranking
|
17 |
-
def objective_function(w):
|
18 |
-
y_subset = [x1 * w[0] + x2 * w[1] + x3 * w[2] for x1, x2, x3 in zip(x1_subset, x2_subset, x3_subset)]
|
19 |
-
y_full_set = [x1 * w[0] + x2 * w[1] + x3 * w[2] for x1 in x1_full for x2 in x2_full for x3 in x3_full]
|
20 |
-
|
21 |
-
# Calculate the 90th percentile of y values for the full set
|
22 |
-
y_full_set_90th_percentile = np.percentile(y_full_set, 90)
|
23 |
-
|
24 |
-
# Maximize the difference between the 90th percentile of the subset and the 90th percentile of the full set
|
25 |
-
return - min(y_subset) + y_full_set_90th_percentile
|
26 |
-
|
27 |
-
|
28 |
-
# Bounds for w_1, w_2, and w_3 (-1 to 1)
|
29 |
-
bounds = [(-1, 1), (-1, 1), (-1, 1)]
|
30 |
-
|
31 |
-
# Perform bounded optimization to find the values of w_1, w_2, and w_3 that maximize the objective function
|
32 |
-
result = minimize(objective_function, np.zeros(3), method='TNC', bounds=bounds)
|
33 |
-
|
34 |
-
# Get the optimal values of w_1, w_2, and w_3
|
35 |
-
w_1_opt, w_2_opt, w_3_opt = result.x
|
36 |
-
|
37 |
-
# Print the results
|
38 |
-
print("Optimal w_1:", w_1_opt)
|
39 |
-
print("Optimal w_2:", w_2_opt)
|
40 |
-
print("Optimal w_3:", w_3_opt)
|
|
|
|
|
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|
pages/Gallery.py
CHANGED
@@ -157,29 +157,29 @@ class GalleryApp:
|
|
157 |
# save latest threshold
|
158 |
st.session_state.score_weights[3] = nsfw_threshold
|
159 |
|
160 |
-
# draw a distribution histogram
|
161 |
-
if sort_type == 'Scores':
|
162 |
-
|
163 |
-
|
164 |
-
|
165 |
-
|
166 |
-
|
167 |
-
|
168 |
-
|
169 |
-
|
170 |
-
|
171 |
-
|
172 |
-
|
173 |
-
|
174 |
-
|
175 |
-
|
176 |
-
|
177 |
-
|
178 |
-
|
179 |
-
|
180 |
-
|
181 |
-
|
182 |
-
|
183 |
|
184 |
display_options = st.columns([1, 4])
|
185 |
|
@@ -208,25 +208,24 @@ class GalleryApp:
|
|
208 |
|
209 |
return items, info, col_num
|
210 |
|
211 |
-
def sidebar(self):
|
212 |
with st.sidebar:
|
213 |
-
prompt_tags = self.promptBook['tag'].unique()
|
214 |
-
# sort tags by alphabetical order
|
215 |
-
prompt_tags = np.sort(prompt_tags)[::1]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
216 |
|
217 |
-
|
218 |
|
219 |
-
items =
|
220 |
|
221 |
-
prompts = np.sort(items['prompt'].unique())[::1]
|
222 |
-
|
223 |
-
selected_prompt = st.selectbox('Select prompt', prompts, index=3)
|
224 |
-
|
225 |
-
mode = st.radio('Select a mode', ['Gallery', 'Graph'], horizontal=True, index=1)
|
226 |
-
|
227 |
-
items = items[items['prompt'] == selected_prompt].reset_index(drop=True)
|
228 |
-
prompt_id = items['prompt_id'].unique()[0]
|
229 |
-
note = items['note'].unique()[0]
|
230 |
|
231 |
# show source
|
232 |
if isinstance(note, str):
|
@@ -260,49 +259,66 @@ class GalleryApp:
|
|
260 |
except:
|
261 |
pass
|
262 |
|
263 |
-
return prompt_tags, tag, prompt_id, items
|
264 |
|
265 |
-
def app(self):
|
266 |
-
st.title('Model Visualization and Retrieval')
|
267 |
-
st.write('This is a gallery of images generated by the models')
|
268 |
-
|
269 |
-
prompt_tags, tag, prompt_id, items, mode = self.sidebar()
|
270 |
-
# items, info, col_num = self.selection_panel(items)
|
271 |
-
|
272 |
-
# subset = st.radio('Select a subset', ['All', 'Selected Only'], index=0, horizontal=True)
|
273 |
-
# try:
|
274 |
-
# if subset == 'Selected Only':
|
275 |
-
# items = items[items['modelVersion_id'].isin(st.session_state.selected_dict[prompt_id])].reset_index(drop=True)
|
276 |
-
# except:
|
277 |
-
# pass
|
278 |
-
|
279 |
-
# add safety check for some prompts
|
280 |
-
safety_check = True
|
281 |
-
unsafe_prompts = {}
|
282 |
-
# initialize unsafe prompts
|
283 |
-
for prompt_tag in prompt_tags:
|
284 |
-
unsafe_prompts[prompt_tag] = []
|
285 |
-
# manually add unsafe prompts
|
286 |
-
unsafe_prompts['world knowledge'] = [83]
|
287 |
-
unsafe_prompts['abstract'] = [1, 3]
|
288 |
-
|
289 |
-
if int(prompt_id.item()) in unsafe_prompts[tag]:
|
290 |
-
st.warning('This prompt may contain unsafe content. They might be offensive, depressing, or sexual.')
|
291 |
-
safety_check = st.checkbox('I understand that this prompt may contain unsafe content. Show these images anyway.', key=f'safety_{prompt_id}')
|
292 |
-
|
293 |
-
if safety_check:
|
294 |
-
if mode == 'Gallery':
|
295 |
-
self.gallery_mode(prompt_id, items)
|
296 |
-
elif mode == 'Graph':
|
297 |
-
self.graph_mode(prompt_id, items)
|
298 |
|
|
|
|
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|
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|
|
299 |
|
300 |
def graph_mode(self, prompt_id, items):
|
301 |
graph_cols = st.columns([3, 1])
|
302 |
-
prompt = st.chat_input(f"Selected model version ids: {str(st.session_state.selected_dict.get(prompt_id, []))}",
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303 |
-
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304 |
-
if prompt:
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305 |
-
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306 |
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307 |
with graph_cols[0]:
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308 |
graph_space = st.empty()
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@@ -366,20 +382,20 @@ class GalleryApp:
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366 |
def gallery_mode(self, prompt_id, items):
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367 |
items, info, col_num = self.selection_panel(items)
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368 |
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369 |
-
if 'selected_dict' in st.session_state:
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-
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371 |
-
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372 |
-
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-
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-
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prompt = st.chat_input(f"Selected model version ids: {str(st.session_state.selected_dict.get(prompt_id, []))}", disabled=False, key=f'{prompt_id}')
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if prompt:
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@@ -387,32 +403,28 @@ class GalleryApp:
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387 |
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with st.form(key=f'{prompt_id}'):
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# buttons = st.columns([1, 1, 1])
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-
buttons_space = st.columns([1, 1, 1
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391 |
gallery_space = st.empty()
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with buttons_space[0]:
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-
continue_btn = st.form_submit_button('
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if continue_btn:
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-
self.submit_actions('Continue', prompt_id)
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397 |
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with buttons_space[1]:
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-
select_btn = st.form_submit_button('Select All', use_container_width=True)
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-
if select_btn:
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-
self.submit_actions('Select', prompt_id)
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-
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403 |
-
with buttons_space[2]:
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deselect_btn = st.form_submit_button('Deselect All', use_container_width=True)
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if deselect_btn:
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406 |
self.submit_actions('Deselect', prompt_id)
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408 |
-
with buttons_space[
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409 |
refresh_btn = st.form_submit_button('Refresh', on_click=gallery_space.empty, use_container_width=True)
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with gallery_space.container():
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with st.spinner('Loading images...'):
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self.gallery_standard(items, col_num, info)
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st.info("Don't forget to scroll back to top and click the 'Confirm Selection' button to save your selection!!!")
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# save latest threshold
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st.session_state.score_weights[3] = nsfw_threshold
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+
# # draw a distribution histogram
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+
# if sort_type == 'Scores':
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+
# try:
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+
# with st.expander('Show score distribution histogram and select score range'):
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+
# st.write('**Score distribution histogram**')
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+
# chart_space = st.container()
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+
# # st.write('Select the range of scores to show')
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+
# hist_data = pd.DataFrame(items[sort_by])
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+
# mini = hist_data[sort_by].min().item()
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+
# mini = mini//0.1 * 0.1
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+
# maxi = hist_data[sort_by].max().item()
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+
# maxi = maxi//0.1 * 0.1 + 0.1
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+
# st.write('**Select the range of scores to show**')
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+
# r = st.slider('Select the range of scores to show', min_value=mini, max_value=maxi, value=(mini, maxi), step=0.05, label_visibility='collapsed')
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+
# with chart_space:
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+
# st.altair_chart(altair_histogram(hist_data, sort_by, r[0], r[1]), use_container_width=True)
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+
# # event_dict = altair_component(altair_chart=altair_histogram(hist_data, sort_by))
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177 |
+
# # r = event_dict.get(sort_by)
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+
# if r:
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179 |
+
# items = items[(items[sort_by] >= r[0]) & (items[sort_by] <= r[1])].reset_index(drop=True)
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180 |
+
# # st.write(r)
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181 |
+
# except:
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182 |
+
# pass
|
183 |
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184 |
display_options = st.columns([1, 4])
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185 |
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|
208 |
|
209 |
return items, info, col_num
|
210 |
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211 |
+
def sidebar(self, items, prompt_id, note):
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212 |
with st.sidebar:
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213 |
+
# prompt_tags = self.promptBook['tag'].unique()
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214 |
+
# # sort tags by alphabetical order
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215 |
+
# prompt_tags = np.sort(prompt_tags)[::1]
|
216 |
+
#
|
217 |
+
# tag = st.selectbox('Select a tag', prompt_tags, index=5)
|
218 |
+
#
|
219 |
+
# items = self.promptBook[self.promptBook['tag'] == tag].reset_index(drop=True)
|
220 |
+
#
|
221 |
+
# prompts = np.sort(items['prompt'].unique())[::1]
|
222 |
+
#
|
223 |
+
# selected_prompt = st.selectbox('Select prompt', prompts, index=3)
|
224 |
|
225 |
+
# mode = st.radio('Select a mode', ['Gallery', 'Graph'], horizontal=True, index=1)
|
226 |
|
227 |
+
# items = items[items['prompt'] == selected_prompt].reset_index(drop=True)
|
228 |
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|
229 |
|
230 |
# show source
|
231 |
if isinstance(note, str):
|
|
|
259 |
except:
|
260 |
pass
|
261 |
|
262 |
+
# return prompt_tags, tag, prompt_id, items
|
263 |
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
264 |
|
265 |
+
def app(self):
|
266 |
+
# st.title('Model Visualization and Retrieval')
|
267 |
+
# st.write('This is a gallery of images generated by the models')
|
268 |
+
|
269 |
+
# build the tabular view
|
270 |
+
prompt_tags = self.promptBook['tag'].unique()
|
271 |
+
# sort tags by alphabetical order
|
272 |
+
prompt_tags = np.sort(prompt_tags)[::1].tolist()
|
273 |
+
|
274 |
+
tabs = st.tabs(prompt_tags)
|
275 |
+
with st.spinner('Loading...'):
|
276 |
+
for i in range(len(prompt_tags)):
|
277 |
+
with tabs[i]:
|
278 |
+
tag = prompt_tags[i]
|
279 |
+
items = self.promptBook[self.promptBook['tag'] == tag].reset_index(drop=True)
|
280 |
+
|
281 |
+
prompts = np.sort(items['prompt'].unique())[::1]
|
282 |
+
|
283 |
+
subset_selector = st.columns([3, 1])
|
284 |
+
with subset_selector[0]:
|
285 |
+
selected_prompt = st.selectbox('Select prompt', prompts, index=3)
|
286 |
+
with subset_selector[1]:
|
287 |
+
subset = st.selectbox('Select a subset', ['All', 'Selected Only'], index=0, key=f'subset_{selected_prompt}')
|
288 |
+
items = items[items['prompt'] == selected_prompt].reset_index(drop=True)
|
289 |
+
prompt_id = items['prompt_id'].unique()[0]
|
290 |
+
note = items['note'].unique()[0]
|
291 |
+
|
292 |
+
# add safety check for some prompts
|
293 |
+
safety_check = True
|
294 |
+
unsafe_prompts = {}
|
295 |
+
# initialize unsafe prompts
|
296 |
+
for prompt_tag in prompt_tags:
|
297 |
+
unsafe_prompts[prompt_tag] = []
|
298 |
+
# manually add unsafe prompts
|
299 |
+
unsafe_prompts['world knowledge'] = [83]
|
300 |
+
unsafe_prompts['abstract'] = [1, 3]
|
301 |
+
|
302 |
+
if int(prompt_id.item()) in unsafe_prompts[tag]:
|
303 |
+
st.warning('This prompt may contain unsafe content. They might be offensive, depressing, or sexual.')
|
304 |
+
safety_check = st.checkbox('I understand that this prompt may contain unsafe content. Show these images anyway.', key=f'safety_{prompt_id}')
|
305 |
+
|
306 |
+
if safety_check:
|
307 |
+
|
308 |
+
# if subset == 'Selected Only' and 'selected_dict' in st.session_state:
|
309 |
+
# items = items[items['modelVersion_id'].isin(st.session_state.selected_dict[prompt_id])].reset_index(drop=True)
|
310 |
+
# self.gallery_mode(prompt_id, items)
|
311 |
+
# else:
|
312 |
+
self.graph_mode(prompt_id, items)
|
313 |
+
|
314 |
+
self.sidebar(items, prompt_id, note)
|
315 |
|
316 |
def graph_mode(self, prompt_id, items):
|
317 |
graph_cols = st.columns([3, 1])
|
318 |
+
# prompt = st.chat_input(f"Selected model version ids: {str(st.session_state.selected_dict.get(prompt_id, []))}",
|
319 |
+
# disabled=False, key=f'{prompt_id}')
|
320 |
+
# if prompt:
|
321 |
+
# switch_page("ranking")
|
322 |
|
323 |
with graph_cols[0]:
|
324 |
graph_space = st.empty()
|
|
|
382 |
def gallery_mode(self, prompt_id, items):
|
383 |
items, info, col_num = self.selection_panel(items)
|
384 |
|
385 |
+
# if 'selected_dict' in st.session_state:
|
386 |
+
# # st.write('checked: ', str(st.session_state.selected_dict.get(prompt_id, [])))
|
387 |
+
# dynamic_weight_options = ['Grid Search', 'SVM', 'Greedy']
|
388 |
+
# dynamic_weight_panel = st.columns(len(dynamic_weight_options))
|
389 |
+
#
|
390 |
+
# if len(st.session_state.selected_dict.get(prompt_id, [])) > 0:
|
391 |
+
# btn_disable = False
|
392 |
+
# else:
|
393 |
+
# btn_disable = True
|
394 |
+
#
|
395 |
+
# for i in range(len(dynamic_weight_options)):
|
396 |
+
# method = dynamic_weight_options[i]
|
397 |
+
# with dynamic_weight_panel[i]:
|
398 |
+
# btn = st.button(method, use_container_width=True, disabled=btn_disable, on_click=self.dynamic_weight, args=(prompt_id, items, method))
|
399 |
|
400 |
prompt = st.chat_input(f"Selected model version ids: {str(st.session_state.selected_dict.get(prompt_id, []))}", disabled=False, key=f'{prompt_id}')
|
401 |
if prompt:
|
|
|
403 |
|
404 |
with st.form(key=f'{prompt_id}'):
|
405 |
# buttons = st.columns([1, 1, 1])
|
406 |
+
buttons_space = st.columns([1, 1, 1])
|
407 |
gallery_space = st.empty()
|
408 |
|
409 |
with buttons_space[0]:
|
410 |
+
continue_btn = st.form_submit_button('Proceed selections to ranking', use_container_width=True, type='primary')
|
411 |
if continue_btn:
|
412 |
+
# self.submit_actions('Continue', prompt_id)
|
413 |
+
switch_page("ranking")
|
414 |
|
415 |
with buttons_space[1]:
|
|
|
|
|
|
|
|
|
|
|
416 |
deselect_btn = st.form_submit_button('Deselect All', use_container_width=True)
|
417 |
if deselect_btn:
|
418 |
self.submit_actions('Deselect', prompt_id)
|
419 |
|
420 |
+
with buttons_space[2]:
|
421 |
refresh_btn = st.form_submit_button('Refresh', on_click=gallery_space.empty, use_container_width=True)
|
422 |
|
423 |
with gallery_space.container():
|
424 |
with st.spinner('Loading images...'):
|
425 |
self.gallery_standard(items, col_num, info)
|
426 |
|
427 |
+
# st.info("Don't forget to scroll back to top and click the 'Confirm Selection' button to save your selection!!!")
|
428 |
|
429 |
|
430 |
|