Create app.py
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app.py
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import gradio as gr
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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# تحميل موديل التلخيص
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model_name = "csebuetnlp/mT5_multilingual_XLSum"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
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# دالة تلخيص النص
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def summarize_text(text, src_lang):
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inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True)
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summary_ids = model.generate(inputs["input_ids"], max_length=150, min_length=30, length_penalty=2.0, num_beams=4, early_stopping=True)
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summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
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return summary
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# أمثلة للنصوص
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examples = [
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["الذكاء الاصطناعي هو فرع من علوم الكمبيوتر يهدف إلى إنشاء آلات ذكية تعمل وتتفاعل مثل البشر. بعض الأنشطة التي صممت أجهزة الكمبيوتر الذكية للقيام بها تشمل: التعرف على الصوت، التعلم، التخطيط، وحل المشاكل.", "ar"],
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["Artificial intelligence is a branch of computer science that aims to create intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include: Speech recognition, learning, planning, and problem-solving.", "en"]
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]
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# واجهة Gradio
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iface = gr.Interface(
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fn=summarize_text,
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inputs=[gr.Textbox(lines=10, placeholder="Enter the text here....."), gr.Dropdown(["ar", "en"], label="Language")],
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outputs="text",
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examples=examples,
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title=" AI Study Summary ",
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description="Enter a text in Arabic or English and the model will summarize it, you can also choose one of the available examples."
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)
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# تشغيل التطبيق
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if __name__ == "__main__":
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iface.launch()
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