app.py

import streamlit as st

# Use a pipeline as a high-level helper
from transformers import pipeline

toxic_model = pipeline("text-classification", model="Matt09Miao/GP5_tweet_toxic")  
    


st.set_page_config(page_title="Tweet Toxicity Analysis")

st.header("Please input your Tweet for Toxicity Analysis :performing_arts:")
input = st.text_area("Enter a Tweer for analysis")
result = toxic_model(input)

  # Display the classification result
    max_score = float('-inf')
    max_label = ''

    for result in results:
        if result['score'] > max_score:
            max_score = result['score']
            max_label = result['label']

    st.write("Tweet:", input)
    st.write("Label:", max_label)
    st.write("Score:", max_score)