app.py

import gradio as gr
from transformers import TextClassificationPipeline, AutoTokenizer, AutoModelForSequenceClassification

# Load a pre-trained text classification model 
model_name = "KoalaAI/Text-Moderation" 
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Create a TextClassificationPipeline
pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer)

# Define the classify_text function using the pipeline
def classify_text(text):
    prediction = pipe(text)[0]["label"]
    return prediction

# Create a Gradio interface
iface = gr.Interface(
    fn=classify_text,
    inputs=gradio.components.Textbox(label="Enter text"),
    outputs=gr.outputs.Label(label="Predicted classes"),
)

# Launch the Gradio app
iface.launch()