# app.py
import gradio as gr
from transformers import pipeline

# Load once at startup
clf = pipeline(
    "text-classification",
    model="distilbert/distilbert-base-uncased-finetuned-sst-2-english"
)

def predict(text):
    if not text or not text.strip():
        return {"empty": 1.0}
    result = clf(text)[0]
    return {result["label"]: float(result["score"])}

demo = gr.Interface(
    fn=predict,
    inputs=gr.Textbox(lines=5, label="Input text"),
    outputs=gr.Label(label="Prediction"),
    title="Sentiment Classifier",
    description="Simple ML inference in a Hugging Face Space"
)

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