app.py

from transformers import pipeline
import gradio as gr

# Load the model
pipe = pipeline("text-classification", model="mgbam/roberta-yelp-genomic-bottleneck")

def classify_text(text):
    results = pipe(text)
    # Extract and format results
    formatted_results = [
        f"Label: {result['label']}, Score: {result['score']:.2f}" for result in results
    ]
    return "\n".join(formatted_results)

# Gradio interface
interface = gr.Interface(
    fn=classify_text,
    inputs="text",
    outputs="text",
    title="Text Classification",
    description="Classify text using the RoBERTa-Yelp-Genomic-Bottleneck model."
)

if __name__ == "__main__":
    interface.launch()