Spaces:
Running
Running
File size: 6,771 Bytes
ce649db 6668c84 6101644 ce649db c5ad46a ce649db c5ad46a ce649db c5ad46a ce649db c5ad46a ce649db c5ad46a 71b659e c5ad46a 71b659e c5ad46a ce649db a01e989 6101644 bca94f8 ce649db 3a77c4a bca94f8 ce649db 6101644 6c67d85 bca94f8 ce649db 01c6c72 bca94f8 ce649db 01c6c72 ab4fce6 01c6c72 0789e97 fa00cfe 0789e97 01c6c72 0789e97 01c6c72 0789e97 01c6c72 0789e97 01c6c72 ab4fce6 0789e97 c5ad46a 01c6c72 c5ad46a 6101644 0789e97 6668c84 01c6c72 ce649db c5ad46a ce649db c5ad46a ce649db c5ad46a a01e989 c5ad46a ce649db 6101644 f920e44 6101644 ce649db 6101644 29302cd 624b826 6101644 3a77c4a 6668c84 |
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 |
import io
import os
import requests
import zipfile
import natsort
import gc
from PIL import Image
from PIL import UnidentifiedImageError
os.environ["TOKENIZERS_PARALLELISM"] = "false"
from stqdm import stqdm
import streamlit as st
from jax import numpy as jnp
import transformers
from transformers import AutoTokenizer
from torchvision.transforms import Compose, CenterCrop, Normalize, Resize, ToTensor
from torchvision.transforms.functional import InterpolationMode
from modeling_hybrid_clip import FlaxHybridCLIP
import utils
@st.cache(hash_funcs={FlaxHybridCLIP: lambda _: None})
def get_model():
return FlaxHybridCLIP.from_pretrained("clip-italian/clip-italian")
@st.cache(
hash_funcs={
transformers.models.bert.tokenization_bert_fast.BertTokenizerFast: lambda _: None
}
)
def get_tokenizer():
return AutoTokenizer.from_pretrained(
"dbmdz/bert-base-italian-xxl-uncased", cache_dir="./", use_fast=True
)
@st.cache(suppress_st_warning=True)
def download_images():
# from sentence_transformers import SentenceTransformer, util
img_folder = "photos/"
if not os.path.exists(img_folder) or len(os.listdir(img_folder)) == 0:
os.makedirs(img_folder, exist_ok=True)
photo_filename = "unsplash-25k-photos.zip"
if not os.path.exists(photo_filename): # Download dataset if does not exist
print(f"Downloading {photo_filename}...")
response = requests.get(
f"http://sbert.net/datasets/{photo_filename}", stream=True
)
total_size_in_bytes = int(response.headers.get("content-length", 0))
block_size = 1024 # 1 Kb
progress_bar = stqdm(
total=total_size_in_bytes
) # , unit='iB', unit_scale=True
content = io.BytesIO()
for data in response.iter_content(block_size):
progress_bar.update(len(data))
content.write(data)
progress_bar.close()
z = zipfile.ZipFile(content)
# content.close()
print("Extracting the dataset...")
z.extractall(path=img_folder)
print("Done.")
@st.cache()
def get_image_features(dataset_name):
if dataset_name == "Unsplash":
return jnp.load("static/features/features.npy")
else:
return jnp.load("static/features/CC_embeddings.npy")
@st.cache()
def load_urls(dataset_name):
if dataset_name == "CC":
with open("static/CC_urls.txt") as fp:
urls = [l.strip() for l in fp.readlines()]
return urls
else:
ValueError(f"{dataset_name} not supported here")
def get_image_transform(image_size):
return Compose(
[
Resize([image_size], interpolation=InterpolationMode.BICUBIC),
CenterCrop(image_size),
ToTensor(),
Normalize(
(0.48145466, 0.4578275, 0.40821073),
(0.26862954, 0.26130258, 0.27577711),
),
]
)
headers = {
#'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36',
'User-Agent': 'Googlebot-Image/1.0', # Pretend to be googlebot
'X-Forwarded-For': '64.18.15.200'
}
def app():
#st.title("From Text to Image")
st.markdown("<h1 style='text-align: center; color: #CD212A;'>Image Retrieval</h1>", unsafe_allow_html=True)
st.markdown("<h2 style='text-align: center; color: #008C45;font-weight:bold;'>Text to Image</h2>", unsafe_allow_html=True)
st.markdown(
"""
### 👋 Ciao!
Here you can search for ~150.000 images in the Conceptual Captions dataset (CC) or in the Unsplash 25k Photos dataset.
Even though we did not train on any of these images you will see most queries make sense. When you see errors, there might be two possibilities:
the model is answering in a wrong way or the image you are looking for are not in the dataset and the model is giving you the best answer it can get.
🤌 Italian mode on! 🤌
You can choose one of our examples down below...
"""
)
suggestions = [
"Un gatto",
"Due gatti",
"Un fiore giallo",
"Un fiore blu",
"Una coppia in montagna",
"Una coppia al tramonto"
]
sugg_idx = -1
col1, col2, col3, col4, col5, col6 = st.beta_columns([1, 1, 1.2, 1.2, 1.4, 1.4])
with col1:
if st.button(suggestions[0]):
sugg_idx = 0
with col2:
if st.button(suggestions[1]):
sugg_idx = 1
with col3:
if st.button(suggestions[2]):
sugg_idx = 2
with col4:
if st.button(suggestions[3]):
sugg_idx = 3
with col5:
if st.button(suggestions[4]):
sugg_idx = 4
with col6:
if st.button(suggestions[5]):
sugg_idx = 5
col1, col2 = st.beta_columns([3, 1])
with col1:
query = st.text_input("... or insert an Italian query text")
with col2:
dataset_name = st.selectbox("IR dataset", ["CC", "Unsplash"])
query = suggestions[sugg_idx] if sugg_idx > -1 else query if query else ""
if query:
with st.spinner("Computing..."):
if dataset_name == "Unsplash":
download_images()
image_features = get_image_features(dataset_name)
model = get_model()
tokenizer = get_tokenizer()
if dataset_name == "Unsplash":
image_size = model.config.vision_config.image_size
dataset = utils.CustomDataSet(
"photos/", transform=get_image_transform(image_size)
)
elif dataset_name == "CC":
dataset = load_urls(dataset_name)
else:
raise ValueError()
N = 3
image_paths = utils.find_image(
query, model, dataset, tokenizer, image_features, N, dataset_name
)
for i, image_url in enumerate(image_paths):
try:
if dataset_name == "Unsplash":
st.image(image_url)
elif dataset_name == "CC":
image_raw = requests.get(image_url, stream=True, allow_redirects=True, headers=headers).raw
image = Image.open(image_raw).convert("RGB")
st.image(image, use_column_width=True)
break
except (UnidentifiedImageError) as e:
if i == N - 1:
st.text(f'Tried to show {N} different image URLS but none of them were reachabele.\
Maybe try a different query?')
gc.collect()
sugg_idx = -1
|