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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)}%")
# 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)
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