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Create app.py
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app.py
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import gradio as gr
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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import torch
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# تحميل النموذج والتوكنايزر
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model_id = "methodya/arabic-summarizer-philosophy-v2"
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model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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# نقل النموذج إلى GPU إذا كان متوفراً
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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model = model.to(device)
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model.eval()
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def summarize(text, max_length=200, num_beams=7, length_penalty=1.2):
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# تحويل القيم إلى النوع المناسب
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max_length = int(max_length)
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num_beams = int(num_beams)
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length_penalty = float(length_penalty)
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if not text.strip():
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return "الرجاء إدخال نص للتلخيص"
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# تجهيز البيانات للنموذج
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inputs = tokenizer(
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text,
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return_tensors="pt",
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max_length=2048,
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padding=True,
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truncation=True
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)
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inputs = {k: v.to(device) for k, v in inputs.items()}
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# توليد الملخص مع معاملات محسنة
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_length=max_length,
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min_length=30,
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num_beams=num_beams,
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length_penalty=length_penalty,
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repetition_penalty=2.5,
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do_sample=False,
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temperature=1.0,
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top_p=0.95,
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no_repeat_ngram_size=4,
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num_return_sequences=1,
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early_stopping=True
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)
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return tokenizer.decode(outputs[0], skip_special_tokens=True)
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# تعريف واجهة Gradio
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interface = gr.Interface(
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fn=summarize,
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inputs=[
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gr.Textbox(lines=8, label="النص", placeholder="أدخل النص العربي هنا..."),
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gr.Slider(100, 400, value=200, label="طول الملخص"),
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gr.Slider(1, 10, value=7, step=1, label="دقة التلخيص (num_beams)"),
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gr.Slider(0.5, 2.0, value=1.2, step=0.1, label="معامل الطول (length_penalty)")
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],
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outputs=gr.Textbox(label="الملخص"),
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title="نظام تلخيص النصوص الفلسفية العربية",
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description="نموذج متقدم لتلخيص النصوص الفلسفية باللغة العربية",
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submit_btn="تلخيص",
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clear_btn="مسح"
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)
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# تشغيل التطبيق
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interface.launch()
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