app (2).py

import gradio as gr
from transformers import pipeline

# Load the model using the correct identifier
classifier = pipeline('text-classification', model='prabhaskenche/toxic-comment-classification-using-RoBERTa')

def classify(text):
    results = classifier(text)
    # Adjust the label names based on your model's label mapping
    non_toxic_score = next((item['score'] for item in results[0] if item['label'] == 'LABEL_0'), 0)
    toxic_score = next((item['score'] for item in results[0] if item['label'] == 'LABEL_1'), 0)
    return f"{non_toxic_score:.3f} non-toxic, {toxic_score:.3f} toxic"

# Create the Gradio interface
interface = gr.Interface(
    fn=classify,
    inputs=gr.Textbox(lines=2, placeholder="Enter text here..."),
    outputs="text"
)

# Launch the interface
interface.launch()