app.py

import streamlit as st
import torch
from PIL import Image
from transformers import AutoFeatureExtractor, AutoModelForImageClassification

# 모델 및 설정 로드
@st.cache_resource
def load_model():
    feature_extractor = AutoFeatureExtractor.from_pretrained("xinyu1205/recognize-anything-plus-model")
    model = AutoModelForImageClassification.from_pretrained("xinyu1205/recognize-anything-plus-model")
    model.eval()
    return feature_extractor, model

# 예측 함수
def predict(image, feature_extractor, model):
    inputs = feature_extractor(images=image, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
    
    logits = outputs.logits
    # 상위 5개 태그 반환
    top_5 = torch.topk(logits, k=5)
    return [model.config.id2label[i.item()] for i in top_5.indices[0]]

# Streamlit 앱
st.title("RAM++ Image Tagging")

feature_extractor, model = load_model()

uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])

if uploaded_file is not None:
    image = Image.open(uploaded_file)
    st.image(image, caption='Uploaded Image', use_column_width=True)

    if st.button('Get Tags'):
        tags = predict(image, feature_extractor, model)
        st.write("Predicted Tags:")
        st.write(", ".join(tags))