app.py

import gradio as gr
from transformers import pipeline

pipe = pipeline("text-classification", model="lewtun/xlm-roberta-base-finetuned-marc-500-samples")

def predict(text):
  label2star = {"LABEL_0": "⭐", "LABEL_1": "⭐⭐", "LABEL_2": "⭐⭐⭐", "LABEL_3": "⭐⭐⭐⭐", "LABEL_4": "⭐⭐⭐⭐⭐"}
  preds = pipe(text)[0]
  return label2star[preds["label"]], round(preds["score"], 5)
  
  gradio_ui = gr.Interface(
    fn=predict,
    title="Review analysis",
    description="Enter some review text about an Amazon product and check what the model predicts for it's star rating.",
    inputs=[
        gr.inputs.Textbox(lines=5, label="Paste some text here"),
    ],
    outputs=[
        gr.outputs.Textbox(label="Label"),
        gr.outputs.Textbox(label="Score"),
    ],
    examples=[
        ["I love this book!", "The Hunger Games is the worst book ever"],
    ],
)

gradio_ui.launch()