import streamlit as st import pandas as pd import numpy as np from html import escape import os import torch import transformers from transformers import RobertaModel, AutoTokenizer #@st.cache(hash_funcs={transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast: hash}, suppress_st_warning=True, allow_output_mutation=True) @st.cache(show_spinner=False, hash_funcs={RobertaModel: lambda _: None, AutoTokenizer: lambda _: None, dict: lambda _: None}) def load(): text_encoder = RobertaModel.from_pretrained('SajjadAyoubi/clip-fa-text') tokenizer = AutoTokenizer.from_pretrained('SajjadAyoubi/clip-fa-text') image_embeddings = torch.load('embedding.pt') links = np.load('data.npy', allow_pickle=True) return text_encoder, tokenizer, links, image_embeddings text_encoder, tokenizer, links, image_embeddings = load() def get_html(url_list, height=224): html = "
" for url in url_list: html2 = f"" html = html + html2 html += "
" return html def compute_embeddings(query): 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(''' ''', unsafe_allow_html=True) st.sidebar.markdown(description) _, c, _ = st.columns((1, 3, 1)) query = c.text_input('', value='غروب خورشید') if len(query) > 0: results = image_search(query) st.markdown(get_html(results), unsafe_allow_html=True) if __name__ == '__main__': main()