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import streamlit as st
import pandas as pd
from html import escape
import os
import torch
from transformers import RobertaModel, AutoTokenizer
@st.cache(show_spinner=False)
def load():
text_encoder = RobertaModel.from_pretrained('SajjadAyoubi/clip-fa-text')
tokenizer = AutoTokenizer.from_pretrained('SajjadAyoubi/clip-fa-text')
links = np.load('link.npy', allow_pickle=True)
image_embeddings = torch.load('embeddings.pt')
return text_encoder, tokenizer, links, image_embeddings
text_encoder, tokenizer, links, image_embeddings = load()
def get_html(url_list, height=224):
html = "<div style='margin-top: 20px; max-width: 1200px; display: flex; flex-wrap: wrap; justify-content: space-evenly'>"
for url in url_list:
html2 = f"<img style='height: {height}px; margin: 5px' src='{escape(url)}'>"
html = html + html2
html += "</div>"
return html
@st.cache(show_spinner=False)
def image_search(query, top_k=8):
with torch.no_grad():
text_embedding = text_encoder(**tokenizer(query, return_tensors='pt')).pooler_output
values, indices = torch.cosine_similarity(text_embedding, image_embeddings).sort(descending=True)
return [links[i] for i in indices[:top_k]]
description = '''
# Semantic image search :)
'''
def main():
st.markdown('''
<style>
.block-container{
max-width: 1200px;
}
div.row-widget.stRadio > div{
flex-direction:row;
display: flex;
justify-content: center;
}
div.row-widget.stRadio > div > label{
margin-left: 5px;
margin-right: 5px;
}
section.main>div:first-child {
padding-top: 0px;
}
section:not(.main)>div:first-child {
padding-top: 30px;
}
div.reportview-container > section:first-child{
max-width: 320px;
}
#MainMenu {
visibility: hidden;
}
footer {
visibility: hidden;
}
</style>''',
unsafe_allow_html=True)
st.sidebar.markdown(description)
_, c, _ = st.columns((1, 3, 1))
query = c.text_input('', value='clouds at sunset')
if len(query) > 0:
results = image_search(query)
st.markdown(get_html(results), unsafe_allow_html=True)
if __name__ == '__main__':
main() |