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 = "