from functools import lru_cache

import gradio as gr
from transformers import pipeline

MODEL_NAME = "TheMohanad1/marbert-arabic-sentiment-analyzer"

# Load once when the Space starts
sentiment_pipeline = pipeline(
    task="text-classification",
    model=MODEL_NAME,
    tokenizer=MODEL_NAME,
)

@lru_cache(maxsize=1024)
def predict(text: str):
    """
    Cache repeated requests for identical inputs.
    """
    result = sentiment_pipeline(text)[0]

    return {
        "label": result["label"],
        "score": round(result["score"], 4),
    }


def infer(text):
    text = text.strip()
    if not text:
        return "", 0.0

    result = predict(text)
    return result["label"], result["score"]


demo = gr.Interface(
    fn=infer,
    inputs=gr.Textbox(lines=4, placeholder="اكتب نصاً عربياً..."),
    outputs=[
        gr.Label(label="Sentiment"),
        gr.Number(label="Confidence"),
    ],
    title="Arabic Sentiment Analysis",
)

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