# 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)}%")