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# import streamlit as st | |
# text = st.text_area("Prefix", value="DM: You enter the room.") | |
# batch = st.number_input("Variants", value=5) | |
# st.markdown(f"{text} {batch}") | |
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)}%") |