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| import streamlit as st | |
| from transformers import pipeline | |
| from PIL import Image | |
| pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog") | |
| st.title("Hot Dog? Or Not?") | |
| file_name = st.file_uploader("Upload a hot dog candidate image") | |
| if file_name is not None: | |
| col1, col2 = st.columns(2) | |
| image = Image.open(file_name) | |
| col1.image(image, use_column_width=True) | |
| predictions = pipeline(image) | |
| col2.header("Probabilities") | |
| for p in predictions: | |
| col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%") | |