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import gradio as gr |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline |
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import graphviz |
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from PIL import Image |
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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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question_generator = pipeline("text2text-generation", model="valhalla/t5-small-e2e-qg") |
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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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def generate_questions(summary): |
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questions = question_generator(summary, max_length=64, num_return_sequences=5) |
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return [q['generated_text'] for q in questions] |
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def generate_concept_map(summary, questions): |
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dot = graphviz.Digraph(comment='Concept Map') |
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dot.node('A', summary) |
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for i, question in enumerate(questions): |
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dot.node(f'Q{i}', question) |
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dot.edge('A', f'Q{i}') |
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dot.render('concept_map', format='png') |
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return Image.open('concept_map.png') |
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def analyze_text(text, lang): |
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summary = summarize_text(text, lang) |
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questions = generate_questions(summary) |
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concept_map_image = generate_concept_map(summary, questions) |
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return summary, questions, concept_map_image |
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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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iface = gr.Interface( |
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fn=analyze_text, |
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inputs=[gr.Textbox(lines=10, placeholder="Enter text here........"), gr.Dropdown(["ar", "en"], label="Language")], |
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outputs=[gr.Textbox(label="Summary"), gr.Textbox(label="Questions"), gr.Image(label="Concept Map")], |
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examples=examples, |
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title="AI Study Assistant", |
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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." |
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) |
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if __name__ == "__main__": |
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iface.launch() |
