-
Notifications
You must be signed in to change notification settings - Fork 4
/
Copy pathapp.py
322 lines (286 loc) · 16.2 KB
/
app.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
import random
import requests
import streamlit as st
from clip_model import ClipModel
from PIL import Image
IMAGES_LINKS = ["https://cdn.pixabay.com/photo/2014/10/13/21/34/clipper-487503_960_720.jpg",
"https://cdn.pixabay.com/photo/2019/09/06/04/25/beach-4455433_960_720.jpg",
"https://cdn.pixabay.com/photo/2019/11/11/14/30/zebra-4618513_960_720.jpg",
"https://cdn.pixabay.com/photo/2020/11/04/15/29/coffee-beans-5712780_960_720.jpg",
"https://cdn.pixabay.com/photo/2020/03/24/20/42/namibia-4965457_960_720.jpg",
"https://cdn.pixabay.com/photo/2020/08/27/07/31/restaurant-5521372_960_720.jpg",
"https://cdn.pixabay.com/photo/2020/08/24/21/41/couple-5515141_960_720.jpg",
"https://cdn.pixabay.com/photo/2020/01/31/07/10/billboards-4807268_960_720.jpg",
"https://cdn.pixabay.com/photo/2017/07/31/20/48/shell-2560930_960_720.jpg",
"https://cdn.pixabay.com/photo/2020/08/13/01/29/koala-5483931_960_720.jpg",
]
@st.cache # Cache this so that it doesn't change every time something changes in the page
def load_default_dataset():
return [load_image_from_url(url) for url in IMAGES_LINKS]
def load_image_from_url(url: str) -> Image.Image:
return Image.open(requests.get(url, stream=True).raw)
@st.cache
def load_model(model_architecture: str) -> ClipModel:
return ClipModel(model_architecture)
def init_state():
if "images" not in st.session_state:
st.session_state.images = None
if "prompts" not in st.session_state:
st.session_state.prompts = None
if "predictions" not in st.session_state:
st.session_state.predictions = None
if "default_text_input" not in st.session_state:
st.session_state.default_text_input = None
if "model_architecture" not in st.session_state:
st.session_state.model_architecture = "RN50"
def limit_number_images():
"""When moving between tasks sometimes the state of images can have too many samples"""
if st.session_state.images is not None and len(st.session_state.images) > 1:
st.session_state.images = [st.session_state.images[0]]
def limit_number_prompts():
"""When moving between tasks sometimes the state of prompts can have too many samples"""
if st.session_state.prompts is not None and len(st.session_state.prompts) > 1:
st.session_state.prompts = [st.session_state.prompts[0]]
def is_valid_prediction_state() -> bool:
if st.session_state.images is None or len(st.session_state.images) < 1:
st.error("Choose at least one image before predicting")
return False
if st.session_state.prompts is None or len(st.session_state.prompts) < 1:
st.error("Write at least one prompt before predicting")
return False
return True
def preprocess_image(image: Image.Image, max_size: int = 1200) -> Image.Image:
"""Set up a max size because otherwise the API sometimes breaks"""
width_0, height_0 = image.size
if max((width_0, height_0)) <= max_size:
return image
if width_0 > height_0:
aspect_ratio = max_size / float(width_0)
new_height = int(float(height_0) * float(aspect_ratio))
image = image.resize((max_size, new_height), Image.ANTIALIAS)
return image
else:
aspect_ratio = max_size / float(height_0)
new_width = int(float(width_0) * float(aspect_ratio))
image = image.resize((max_size, new_width), Image.ANTIALIAS)
return image
class Sections:
@staticmethod
def header():
st.markdown('<link rel="stylesheet" '
'href="https://fonts.googleapis.com/css?family=Merriweather+Sans">'
'<style> '
'h1 {font-family: "Merriweather Sans", sans-serif; font-size: 48px; color: #f57c70}'
'a {color: #e6746a !important}'
'.stButton>button {'
' color: white;'
' background: #e6746a;'
' display:inline-block;'
' width: 100%;'
' border-width: 0px;'
' font-weight: 500;'
' padding-top: 10px;'
' padding-bottom: 10px;'
'}'
'</style>', unsafe_allow_html=True)
st.markdown("# CLIP Playground")
st.markdown("### Try OpenAI's CLIP model in your browser")
st.markdown(" ")
st.markdown(" ")
with st.expander("What is CLIP?"):
st.markdown("CLIP is a machine learning model that computes similarity between text "
"(also called prompts) and images. It has been trained on a dataset with millions of diverse"
" image-prompt pairs, which allows it to generalize to unseen examples."
" <br /> Check out [OpenAI's blogpost](https://openai.com/blog/clip/) for more details",
unsafe_allow_html=True)
col1, col2 = st.columns(2)
col1.image("https://openaiassets.blob.core.windows.net/$web/clip/draft/20210104b/overview-a.svg")
col2.image("https://openaiassets.blob.core.windows.net/$web/clip/draft/20210104b/overview-b.svg")
with st.expander("What can CLIP do?"):
st.markdown("#### Prompt ranking")
st.markdown("Given different prompts and an image CLIP will rank the different prompts based on how well they describe the image")
st.markdown("#### Image ranking")
st.markdown("Given different images and a prompt CLIP will rank the different images based on how well they fit the description")
st.markdown("#### Image classification")
st.markdown("Similar to prompt ranking, given a set of classes CLIP can classify an image between them. "
"Think of [Hotdog/ Not hotdog](https://www.youtube.com/watch?v=pqTntG1RXSY&ab_channel=tvpromos) witho
8000
ut any training.")
st.markdown(" ")
st.markdown(" ")
@staticmethod
def image_uploader(accept_multiple_files: bool):
uploaded_images = st.file_uploader("Upload image", type=[".png", ".jpg", ".jpeg"],
accept_multiple_files=accept_multiple_files)
if (not accept_multiple_files and uploaded_images is not None) or (accept_multiple_files and len(uploaded_images) >= 1):
images = []
if not accept_multiple_files:
uploaded_images = [uploaded_images]
for uploaded_image in uploaded_images:
pil_image = Image.open(uploaded_image)
pil_image = preprocess_image(pil_image)
images.append(pil_image)
st.session_state.images = images
@staticmethod
def image_picker(default_text_input: str):
col1, col2, col3 = st.columns(3)
with col1:
default_image_1 = load_image_from_url("https://cdn.pixabay.com/photo/2014/10/13/21/34/clipper-487503_960_720.jpg")
st.image(default_image_1, use_column_width=True)
if st.button("Select image 1"):
st.session_state.images = [default_image_1]
st.session_state.default_text_input = default_text_input
with col2:
default_image_2 = load_image_from_url("https://cdn.pixabay.com/photo/2019/11/11/14/30/zebra-4618513_960_720.jpg")
st.image(default_image_2, use_column_width=True)
if st.button("Select image 2"):
st.session_state.images = [default_image_2]
st.session_state.default_text_input = default_text_input
with col3:
default_image_3 = load_image_from_url("https://cdn.pixabay.com/photo/2016/11/15/16/24/banana-1826760_960_720.jpg")
st.image(default_image_3, use_column_width=True)
if st.button("Select image 3"):
st.session_state.images = [default_image_3]
st.session_state.default_text_input = default_text_input
@staticmethod
def dataset_picker():
columns = st.columns(5)
st.session_state.dataset = load_default_dataset()
image_idx = 0
for col in columns:
col.image(st.session_state.dataset[image_idx])
image_idx += 1
col.image(st.session_state.dataset[image_idx])
image_idx += 1
if st.button("Select random dataset"):
st.session_state.images = st.session_state.dataset
st.session_state.default_text_input = "A sign that says 'SLOW DOWN'"
@staticmethod
def prompts_input(input_label: str, prompt_prefix: str = ''):
raw_text_input = st.text_input(input_label,
value=st.session_state.default_text_input if st.session_state.default_text_input is not None else "")
st.session_state.is_default_text_input = raw_text_input == st.session_state.default_text_input
if raw_text_input:
st.session_state.prompts = [prompt_prefix + class_name for class_name in raw_text_input.split(";") if len(class_name) > 1]
@staticmethod
def single_image_input_preview():
st.markdown("### Preview")
col1, col2 = st.columns([1, 2])
with col1:
st.markdown("Image to classify")
if st.session_state.images is not None:
st.image(st.session_state.images[0], use_column_width=True)
else:
st.warning("Select an image")
with col2:
st.markdown("Labels to choose from")
if st.session_state.prompts is not None:
for prompt in st.session_state.prompts:
st.markdown(f"* {prompt}")
if len(st.session_state.prompts) < 2:
st.warning("At least two prompts/classes are needed")
else:
st.warning("Enter the prompts/classes to classify from")
@staticmethod
def multiple_images_input_preview():
st.markdown("### Preview")
st.markdown("Images to classify")
col1, col2, col3 = st.columns(3)
if st.session_state.images is not None:
for idx, image in enumerate(st.session_state.images):
if idx < len(st.session_state.images) / 2:
col1.image(st.session_state.images[idx], use_column_width=True)
else:
col2.image(st.session_state.images[idx], use_column_width=True)
if len(st.session_state.images) < 2:
col2.warning("At least 2 images required")
else:
col1.warning("Select an image")
with col3:
st.markdown("Query prompt")
if st.session_state.prompts is not None:
for prompt in st.session_state.prompts:
st.write(prompt)
else:
st.warning("Enter the prompt to classify")
@staticmethod
def classification_output(model: ClipModel):
if st.button("Predict") and is_valid_prediction_state():
with st.spinner("Predicting..."):
st.markdown("### Results")
if len(st.session_state.images) == 1:
scores = model.compute_prompts_probabilities(st.session_state.images[0], st.session_state.prompts)
scored_prompts = [(prompt, score) for prompt, score in zip(st.session_state.prompts, scores)]
sorted_scored_prompts = sorted(scored_prompts, key=lambda x: x[1], reverse=True)
for prompt, probability in sorted_scored_prompts:
percentage_prob = int(probability * 100)
st.markdown(
f"###  {prompt}")
elif len(st.session_state.prompts) == 1:
st.markdown(f"### {st.session_state.prompts[0]}")
scores = model.compute_images_probabilities(st.session_state.images, st.session_state.prompts[0])
scored_images = [(image, score) for image, score in zip(st.session_state.images, scores)]
sorted_scored_images = sorted(scored_images, key=lambda x: x[1], reverse=True)
for image, probability in sorted_scored_images[:5]:
col1, col2 = st.columns([1, 3])
col1.image(image, use_column_width=True)
percentage_prob = int(probability * 100)
col2.markdown(f"### ")
else:
raise ValueError("Invalid state")
# is_default_image = isinstance(state.images[0], str)
# is_default_prediction = is_default_image and state.is_default_text_input
# if is_default_prediction:
# st.markdown("<br>:information_source: Try writing your own prompts and using your own pictures!",
# unsafe_allow_html=True)
# elif is_default_image:
# st.markdown("<br>:information_source: You can also use your own pictures!",
# unsafe_allow_html=True)
# elif state.is_default_text_input:
# st.markdown("<br>:information_source: Try writing your own prompts!"
# " It can be whatever you can think of",
# unsafe_allow_html=True)
if __name__ == "__main__":
Sections.header()
col1, col2 = st.columns([1, 2])
col1.markdown(" "); col1.markdown(" ")
col1.markdown("#### Task selection")
task_name: str = col2.selectbox("", options=["Prompt ranking", "Image ranking", "Image classification"])
st.markdown("<br>", unsafe_allow_html=True)
init_state()
model = load_model(st.session_state.model_architecture)
if task_name == "Image classification":
Sections.image_uploader(accept_multiple_files=False)
if st.session_state.images is None:
st.markdown("or choose one from")
Sections.image_picker(default_text_input="banana; boat; bird")
input_label = "Enter the classes to chose from separated by a semi-colon. (f.x. `banana; boat; honesty; apple`)"
Sections.prompts_input(input_label, prompt_prefix='A picture of a ')
limit_number_images()
Sections.single_image_input_preview()
Sections.classification_output(model)
elif task_name == "Prompt ranking":
Sections.image_uploader(accept_multiple_files=False)
if st.session_state.images is None:
st.markdown("or choose one from")
Sections.image_picker(default_text_input="A calm afternoon in the Mediterranean; "
"A beautiful creature;"
" Something that grows in tropical regions")
input_label = "Enter the prompts to choose from separated by a semi-colon. " \
"(f.x. `An image that inspires; A feeling of loneliness; joyful and young; apple`)"
Sections.prompts_input(input_label)
limit_number_images()
Sections.single_image_input_preview()
Sections.classification_output(model)
elif task_name == "Image ranking":
Sections.image_uploader(accept_multiple_files=True)
if st.session_state.images is None or len(st.session_state.images) < 2:
st.markdown("or use this random dataset")
Sections.dataset_picker()
Sections.prompts_input("Enter the prompt to query the images by")
limit_number_prompts()
Sections.multiple_images_input_preview()
Sections.classification_output(model)
with st.expander("Advanced settings"):
st.session_state.model_architecture = st.selectbox("Model architecture", options=['RN50', 'RN101', 'RN50x4', 'RN50x16', 'RN50x64', 'ViT-B/32',
'ViT-B/16', 'ViT-L/14', 'ViT-L/14@336px'], index=0)
st.markdown("<br><br><br><br>Made by [@JavierFnts](https://twitter.com/JavierFnts) | [How was CLIP Playground built?](https://twitter.com/JavierFnts/status/1363522529072214019)"
"", unsafe_allow_html=True)