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app.py

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+import gradio as gr
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+model_id = "methodya/arabic-summarizer-philosophy"
+model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+def summarize(text, max_length=150):
+    inputs = tokenizer(text, return_tensors="pt", max_length=1024, truncation=True)
+    outputs = model.generate(**inputs, max_length=max_length)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+interface = gr.Interface(
+    fn=summarize,
+    inputs=[
+        gr.Textbox(lines=8, label="النص"),
+        gr.Slider(50, 250, value=150, label="طول الملخص")
+    ],
+    outputs=gr.Textbox(label="الملخص"),
+    title="ملخص النصوص الفلسفية",
+    description="نموذج لتلخيص النصوص الفلسفية باللغة العربية"
+)
+
+interface.launch()

requirements.txt

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+gradio>=3.50.0
+torch
+transformers