| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline | |
| model_id = "Nadasr/sentAnalysisModel" | |
| classifier = pipeline( | |
| "text-classification", | |
| model=model_id, | |
| tokenizer=model_id, | |
| return_all_scores=False | |
| ) | |
| label_map = { | |
| "LABEL_0": "سلبي", | |
| "LABEL_1": "إيجابي", | |
| "0": "سلبي", | |
| "1": "إيجابي", | |
| } | |
| def predict(text): | |
| text = text.strip() | |
| if not text: | |
| return "اكتب الجملة أولاً 🙂" | |
| result = classifier(text)[0] | |
| label = label_map.get(result["label"], result["label"]) | |
| score = round(float(result["score"]), 3) | |
| return f"{label} (score = {score})" | |
| demo = gr.Interface( | |
| fn=predict, | |
| inputs=gr.Textbox(lines=3, label="النص العربي"), | |
| outputs=gr.Textbox(label="نتيجة التحليل"), | |
| title="نموذج تحليل المشاعر بالعربية", | |
| description="أدخل الجملة وسيتم تصنيفها إلى إيجابي أو سلبي." | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |