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import io
import os
import requests
import zipfile
import natsort
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():
return jnp.load("static/features/features.npy")
def app():
st.title("From Text to Image")
st.markdown(
"""
### 👋 Ciao!
Here you can search for images in the Unsplash 25k Photos dataset.
🤌 Italian mode on! 🤌
"""
)
if 'suggestion' not in st.session_state:
st.session_state.suggestion = ""
def update_query(value=""):
st.session_state.suggestion = value
col1, col2, col3, col4 = st.beta_columns(4)
with col1:
st.button('Un gatto', on_click=update_query, kwargs=dict(value='Un gatto'))
with col2:
st.button('Due gatti', on_click=update_query, kwargs=dict(value='Due gatti'))
with col3:
st.button('Un fiore giallo', on_click=update_query, kwargs=dict(value='Un fiore giallo'))
with col4:
st.button('Un fiore blu', on_click=update_query, kwargs=dict(value='Un fiore blu'))
query = st.text_input('Insert an italian query text here...', st.session_state.suggestion)
if query:
with st.spinner("Computing in progress..."):
model = get_model()
download_images()
image_features = get_image_features()
model = get_model()
tokenizer = get_tokenizer()
image_size = model.config.vision_config.image_size
val_preprocess = Compose(
[
Resize([image_size], interpolation=InterpolationMode.BICUBIC),
CenterCrop(image_size),
ToTensor(),
Normalize(
(0.48145466, 0.4578275, 0.40821073),
(0.26862954, 0.26130258, 0.27577711),
),
]
)
dataset = utils.CustomDataSet("photos/", transform=val_preprocess)
image_paths = utils.find_image(
query, model, dataset, tokenizer, image_features, n=2
)
st.image(image_paths)
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