app.py

import gradio as gr
from transformers import pipeline

sentiment_pipeline = pipeline(
    "text-classification",
    model="Nadasr/sentAnalysisModel",
    tokenizer="Nadasr/sentAnalysisModel",
    return_all_scores=False  # يرجع أفضل لابل فقط
)

def predict_sentiment(text):
    text = text.strip()
    if not text:
        return "رجاءً أدخل نصاً للتصنيف 🙂"

    result = sentiment_pipeline(text)[0]
    label = result["label"]
    score = round(result["score"], 3)

    if label in ["LABEL_1", "POSITIVE", "positive"]:
        label_ar = "إيجابي 👍"
    elif label in ["LABEL_0", "NEGATIVE", "negative"]:
        label_ar = "سلبي 👎"
    else:
        label_ar = label

    return f"{label_ar} (الاحتمال = {score})"

demo = gr.Interface(
    fn=predict_sentiment,
    inputs=gr.Textbox(lines=4, label="أدخل النص العربي هنا"),
    outputs=gr.Textbox(label="نتيجة التصنيف"),
    title="Arabic Sentiment Analysis",
    description="نموذج لتحليل المشاعر للنصوص العربية (إيجابي / سلبي)."
)

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