app.py

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)}%")

# Second app
# import streamlit as st
# from transformers import pipeline
# pipe = pipeline ( 'sentiment-analysis')
# text = st.text_area('Enter some text ')
# if text :
#     out = pipe(text)
#     st.json(out)

# First app
# x = st.slider('Select a value')
# st.write(x, 'squared is', x * x)