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import streamlit as st
import pandas as pd
from backend.utils import load_dataset, use_container_width_percentage
st.title('ImageNet-1k')
st.markdown('This page shows the summary of 50,000 images in the validation set of [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k)')
# SCREEN_WIDTH, SCREEN_HEIGHT = 2560, 1664
with st.spinner("Loading dataset..."):
dataset_dict = {}
for data_index in range(5):
dataset_dict[data_index] = load_dataset(data_index)
imagenet_df = pd.read_csv('./data/ImageNet_metadata.csv')
class_labels = imagenet_df.ClassLabel.unique().tolist()
class_labels.sort()
selected_classes = st.multiselect('Class filter: ', options=['All'] + class_labels)
if not ('All' in selected_classes or len(selected_classes) == 0):
imagenet_df = imagenet_df[imagenet_df['ClassLabel'].isin(selected_classes)]
# st.write(class_labels)
col1, col2 = st.columns([2, 1])
with col1:
st.dataframe(imagenet_df)
use_container_width_percentage(100)
with col2:
st.text_area('Type anything here to copy later :)')
image = None
with st.form("display image"):
img_index = st.text_input('Image ID to display')
try:
img_index = int(img_index)
except:
pass
submitted = st.form_submit_button('Display this image')
if submitted:
image = dataset_dict[img_index//10_000][img_index%10_000]['image']
class_label = dataset_dict[img_index//10_000][img_index%10_000]['label']
class_id = dataset_dict[img_index//10_000][img_index%10_000]['id']
if image != None:
st.image(image)
st.write('**Class label:** ', class_label)
st.write('\n**Class id:** ', str(class_id))
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