app.py

import streamlit as st
import numpy as np
from html import escape
import torch
from transformers import RobertaModel, AutoTokenizer


@st.experimental_memo
def load():
  tokenizer = AutoTokenizer.from_pretrained('SajjadAyoubi/clip-fa-text')
  text_encoder = RobertaModel.from_pretrained('SajjadAyoubi/clip-fa-text').eval()
  image_embeddings = torch.load('embedding.pt')
  links = np.load('data.npy', allow_pickle=True)
  return tokenizer, text_encoder, links, image_embeddings
  
tokenizer, text_encoder, links, image_embeddings = load()


@st.experimental_memo
def image_search(query, top_k=10):
    with torch.no_grad():
        text_embedding = text_encoder(**tokenizer(query, return_tensors='pt')).pooler_output
    _, indices = torch.cosine_similarity(image_embeddings, text_embedding).sort(descending=True)
    return [links[i] for i in indices[:top_k]]


def get_html(url_list):
    html = "<div style='margin-top: 50px; max-width: 1100px; display: flex; flex-wrap: wrap; justify-content: space-evenly'>"
    for url in url_list:
        html2 = f"<img style='height: 200px; margin: 2px' src='{escape(url)}'>"  
        html = html + html2
    html += "</div>"
    return html


description = '''
# Persian (fa) image search 
- Enter your query and hit enter

Built with [CLIP-fa](https://github.com/sajjjadayobi/CLIPfa) model and 25k images from [Unsplash](https://unsplash.com/)
'''


def main():
    st.markdown('''
              <style>
              .block-container{
                max-width: 1200px;
              }
              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('Search Box (type in fa)', value='گل صورتی')
    c.text("It'll take about 20s to load all new images")
    if len(query) > 0:
        results = image_search(query)
        st.markdown(get_html(results), unsafe_allow_html=True)


if __name__ == '__main__':
    main()