app.py

import gradio as gr
from transformers import pipeline

MODEL_IDS = {
    "RoBERTa-base (best, 89.1%)": "heican/sentiment-roberta-base",
    "BERT-base (87.5%)": "heican/sentiment-bert-base",
}
DEFAULT_MODEL = "RoBERTa-base (best, 89.1%)"

_cache = {}
def get_model(name):
    if name not in _cache:
        _cache[name] = pipeline("sentiment-analysis", model=MODEL_IDS[name])
    return _cache[name]

def predict(text, model_choice):
    clf = get_model(model_choice or DEFAULT_MODEL)
    result = clf(text)[0]
    label = "Positive" if result["label"] == "LABEL_1" else "Negative"
    return f"{label} ({result['score']:.2%})"

demo = gr.Interface(
    fn=predict,
    inputs=[
        gr.Textbox(label="Tweet"),
        gr.Radio(list(MODEL_IDS.keys()), label="Model", value=DEFAULT_MODEL),
    ],
    outputs=gr.Textbox(label="Prediction"),
    title="Sentiment Analysis Demo",
    description="Compare two fine-tuned transformer models.",
)

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