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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 | |
def load(): | |
text_encoder = RobertaModel.from_pretrained('SajjadAyoubi/clip-fa-text') | |
tokenizer = AutoTokenizer.from_pretrained('SajjadAyoubi/clip-fa-text') | |
link_df = pd.read_csv('links.csv') | |
image_embeddings = torch.load('embeddings.pt') | |
return text_encoder, tokenizer, link_df, image_embeddings | |
text_encoder, tokenizer, link_df, 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, link in url_list: | |
html2 = f"<img style='height: {height}px; margin: 5px' src='{escape(url)}'>" | |
if len(link) > 0: | |
html2 = f"<a href='{escape(link)}' target='_blank'>" + \ | |
html2 + "</a>" | |
html = html + html2 | |
html += "</div>" | |
return html | |
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 [(link_df.iloc[i]['path'], link_df.iloc[i]['link']) 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() |