app.py

import pickle

import numpy as np
import pandas as pd
import streamlit as st
from sentence_transformers.util import semantic_search
from transformers import VisionTextDualEncoderModel, VisionTextDualEncoderProcessor

st.title("VitB32 Bert Ko Small Clip Test")
st.markdown("Unsplash data에서 입력 텍스트와 가장 유사한 이미지를 검색합니다.")

with st.spinner("Loading model..."):
    model = VisionTextDualEncoderModel.from_pretrained(
        "Bingsu/vitB32_bert_ko_small_clip"
    )
    processor = VisionTextDualEncoderProcessor.from_pretrained(
        "Bingsu/vitB32_bert_ko_small_clip"
    )

info = pd.read_csv("info.csv")
with open("img_id.pkl", "rb") as f:
    img_id = pickle.load(f)
img_emb = np.load("img_emb.npy")

text = st.text_input("Input Text", value="검은 고양이")
tokens = processor(text=text, return_tensors="pt")

with st.spinner("Predicting..."):
    text_emb = model.get_text_features(**tokens)

result = semantic_search(text_emb, img_emb, top_k=6)[0]

columns = st.columns(3) + st.columns(3)
for i, col in enumerate(columns):
    photo_id = img_id[result[i]["corpus_id"]]
    target_series = info.loc[info["photo_id"] == photo_id, "photo_image_url"]
    if len(target_series) == 0:
        continue
    img_url = target_series.iloc[0]
    col.image(img_url, use_column_width=True)