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 transformers 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)