Update app.py

app.py

@@ -3,27 +3,22 @@ from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
 import graphviz
 from PIL import Image
 
-# تحميل موديل التلخيص
 model_name = "csebuetnlp/mT5_multilingual_XLSum"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 
-# تحميل موديل توليد الأسئلة
 question_generator = pipeline("text2text-generation", model="valhalla/t5-small-e2e-qg")
 
-# دالة تلخيص النص
 def summarize_text(text, src_lang):
     inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True)
     summary_ids = model.generate(inputs["input_ids"], max_length=150, min_length=30, length_penalty=2.0, num_beams=4, early_stopping=True)
     summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
     return summary
 
-# دالة توليد الأسئلة
 def generate_questions(summary):
     questions = question_generator(summary, max_length=64, num_return_sequences=5)
     return [q['generated_text'] for q in questions]
 
-# دالة توليد خريطة مفاهيم
 def generate_concept_map(summary, questions):
     dot = graphviz.Digraph(comment='Concept Map')
     dot.node('A', summary)
@@ -33,20 +28,18 @@ def generate_concept_map(summary, questions):
     dot.render('concept_map', format='png')
     return Image.open('concept_map.png')
 
-# دالة التحليل الكامل
 def analyze_text(text, lang):
     summary = summarize_text(text, lang)
     questions = generate_questions(summary)
     concept_map_image = generate_concept_map(summary, questions)
     return summary, questions, concept_map_image
 
-# أمثلة للنصوص
 examples = [
     ["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"],
     ["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"]
 ]
 
-# واجهة Gradio
+
 iface = gr.Interface(
     fn=analyze_text,
     inputs=[gr.Textbox(lines=10, placeholder="Enter text here........"), gr.Dropdown(["ar", "en"], label="Language")],
@@ -56,6 +49,5 @@ iface = gr.Interface(
     description="Enter a text in Arabic or English and the model will summarize it and generate various questions about it in addition to generating a concept map, or you can choose one of the examples."
 )
 
-# تشغيل التطبيق
 if __name__ == "__main__":
     iface.launch()