app.py

import streamlit as st
import pandas as pd
from transformers import AutoTokenizer
from transformers import (
    TFAutoModelForSequenceClassification as AutoModelForSequenceClassification,
)
from transformers import pipeline

st.title("Toxic Tweet Classifier")

demo = """Your words are like poison. They seep into my mind and make me feel worthless."""
text = st.text_area("Input text", demo, height=275)

submit = False
model_name = ""

with st.container():
    model_name = st.selectbox(
        "Select Model",
        ("RobCaamano/toxicity",),
    )
    submit = st.button("Submit", type="primary")

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
clf = pipeline(
    "sentiment-analysis", model=model, tokenizer=tokenizer, return_all_scores=True
)

input = tokenizer(text, return_tensors="tf")

if submit:
    results = dict(d.values() for d in clf(text)[0])
    classes = {k: results[k] for k in results.keys() if not k == "toxic"}

    max_class = max(classes, key=classes.get)
    probability = classes[max_class]

    if results['toxic'] >= 0.5:
        result_df = pd.DataFrame({
            'Toxic': ['Yes'],
            'Toxicity Class': [max_class],
            'Probability': [probability]
        })
    else:
        result_df = pd.DataFrame({
            'Toxic': ['No'],
            'Toxicity Class': 'This text is not toxic',
        })

    st.table(result_df)

    expander = st.expander("View Raw output")
    expander.write(results)