toxic.py

import streamlit as st
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification


def app():
    st.title('Toxic Comment Classifier')
    st.write('This is the toxic comment classifier page.')
model_checkpoint = 'cointegrated/rubert-tiny-toxicity'
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)

if torch.cuda.is_available():
    model.cuda()

def text2toxicity(text, aggregate=True):
    """ Calculate toxicity of a text (if aggregate=True) or a vector of toxicity aspects (if aggregate=False)"""
    with torch.no_grad():
        inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True).to(model.device)
        proba = torch.sigmoid(model(**inputs).logits).cpu().numpy()
    if isinstance(text, str):
        proba = proba[0]
    if aggregate:
        return 1 - proba.T[0] * (1 - proba.T[-1])
    return proba

st.title("Toxicity Detector")

user_input = st.text_area("Enter text to check for toxicity:", "Капец ты гнида")
if st.button("Analyze"):
    toxicity_score = text2toxicity(user_input, True)
    st.write(f"Toxicity Score: {toxicity_score:.4f}")
    if toxicity_score > 0.5:
        st.write("Warning: The text seems to be toxic!")