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import requests |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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bio_tokenizer = AutoTokenizer.from_pretrained("microsoft/BioGPT-Large") |
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bio_model = AutoModelForCausalLM.from_pretrained("microsoft/BioGPT-Large").to(DEVICE) |
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def search_pubmed(query, max_results=5): |
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""" |
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البحث عن أبحاث في PubMed باستخدام اسم المرض |
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""" |
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url = f"https://api.ncbi.nlm.nih.gov/lit/ctxp/v1/pubmed/?term={query}&format=json" |
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response = requests.get(url) |
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if response.status_code != 200: |
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return [] |
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data = response.json() |
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articles = [] |
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for item in data.get("records", [])[:max_results]: |
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articles.append({ |
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"title": item.get("title", ""), |
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"link": item.get("url", ""), |
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"abstract": item.get("abstract", "") |
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}) |
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return articles |
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def summarize_text(text): |
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""" |
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تلخيص نص البحث باستخدام BioGPT |
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ملاحظة: BioGPT موديل causal، مش seq2seq، لذلك النتيجة توليد نصي وليست تلخيص مثالي |
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""" |
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inputs = bio_tokenizer(text, return_tensors="pt", truncation=True, max_length=1024).to(DEVICE) |
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summary_ids = bio_model.generate( |
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**inputs, |
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max_length=150, |
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min_length=40, |
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length_penalty=2.0, |
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num_beams=4 |
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) |
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summary = bio_tokenizer.decode(summary_ids[0], skip_special_tokens=True) |
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return summary |
