app.py

import streamlit as st
import torch
import plotly.express as px
from transformers import AutoTokenizer, AutoModelForSequenceClassification

deftxt = "I hate you cancerous insects so much"
txt = st.text_area('Text to analyze', deftxt)

# load tokenizer and model weights
tokenizer = AutoTokenizer.from_pretrained("s-nlp/roberta_toxicity_classifier")
model = AutoModelForSequenceClassification.from_pretrained("s-nlp/roberta_toxicity_classifier")

# prepare the input
batch = tokenizer.encode('txt', return_tensors='pt')

# inference
result = model(batch)
print([d["text"] for d in result])
["Toxic", "Non-toxic"]

#fig = px.bar(result, x="", y="", orientation='h')
#fig.show()