classification.py

from glob import glob
import pandas as pd
import streamlit as st
from streamlit_image_annotation import classification

label_list = ['deer', 'human', 'dog', 'penguin', 'framingo', 'teddy bear']
image_path_list = glob('image/*.jpg')
if 'result_df' not in st.session_state:
    st.session_state['result_df'] = pd.DataFrame.from_dict({'image': image_path_list, 'label': [0]*len(image_path_list)}).copy()

num_page = st.slider('page', 0, len(image_path_list)-1, 0)

label = classification(image_path_list[num_page], 
                        label_list=label_list, 
                        default_label_index=int(st.session_state['result_df'].loc[num_page, 'label']))

if label is not None and label['label'] != st.session_state['result_df'].loc[num_page, 'label']:
    st.session_state['result_df'].loc[num_page, 'label'] = label_list.index(label['label'])
st.table(st.session_state['result_df'])