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)}%")


# import streamlit as st
# from tranformers import pipeline


# pipe = pipeline(task="sentiment-analysis")
# st.title("Toxic Tweets Analyzer")

# text = st.text_area("Enter your tweet here, or submit to test the default tweets")

# if text == "Enter your tweet here, or submit to test the default tweets":
    
#     data = [
#         "PICKLE YE",
#         "I'm nice at ping pong"
#         "My eyes are now wide open and now realize I've been used to spread messages I don't believe in. I am distancing myself from politics and completely focusing on being creative !!!",
#         "There are so many lonely emojis",
#     ]
    
#     st.json([pipe(d) for d in data])
    
# else:
#     out = pipe(text)
#     st.json(out)