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(" Image Retrieval
", unsafe_allow_html=True)
st.markdown(" Text to Image
", unsafe_allow_html=True)
st.markdown(
"""
👋 Ciao! Here you can type Italian query and search from ~150k images in the Conceptual Captions (CC) dataset or 25k Photos in the Unsplash dataset.
Though these images were not used for training the model, you will see most queries make sense.
Rare errors might be due to 2 possibilities:
a)The model is answering in a wrong way or b) the image you are looking for are not in the dataset & the model is giving you the best answer it can get.
You can choose from one of the following examples :
"""
)
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