Rename pubmed_module.py to diagnosis_module.py

diagnosis_module.py

@@ -0,0 +1,33 @@
+# diagnosis_module.py
+import json
+from difflib import SequenceMatcher
+
+def load_diseases(path="diseases.json"):
+    """
+    تحميل قاعدة بيانات الأمراض والأعراض
+    """
+    with open(path, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+def diagnose_symptoms(user_input):
+    """
+    تحليل الأعراض وإعطاء الأمراض المحتملة
+    """
+    diseases = load_diseases()
+    results = []
+    user_symptoms = [s.strip() for s in user_input.split(",")]
+    for disease, info in diseases.items():
+        match_score = sum(
+            SequenceMatcher(None, sym.lower(), us.lower()).ratio()
+            for sym in info["symptoms"]
+            for us in user_symptoms
+        ) / (len(info["symptoms"]) * len(user_symptoms))
+        if match_score > 0.2:
+            results.append({
+                "disease": disease,
+                "score": match_score,
+                "source": info.get("source", ""),
+                "brief": info.get("brief", "")
+            })
+    results.sort(key=lambda x: x["score"], reverse=True)
+    return results[:5]

pubmed_module.py

@@ -1,44 +0,0 @@
-# pubmed_module.py
-import requests
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-import torch
-
-# الجهاز: GPU لو متاح
-DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# تحميل BioGPT للتلخيص
-bio_tokenizer = AutoTokenizer.from_pretrained("microsoft/BioGPT-Large")
-bio_model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/BioGPT-Large").to(DEVICE)
-
-def search_pubmed(query, max_results=5):
-    """
-    البحث عن أبحاث في PubMed باستخدام اسم المرض
-    """
-    url = f"https://api.ncbi.nlm.nih.gov/lit/ctxp/v1/pubmed/?term={query}&format=json"
-    response = requests.get(url)
-    if response.status_code != 200:
-        return []
-    data = response.json()
-    articles = []
-    for item in data.get("records", [])[:max_results]:
-        articles.append({
-            "title": item.get("title", ""),
-            "link": item.get("url", ""),
-            "abstract": item.get("abstract", "")
-        })
-    return articles
-
-def summarize_text(text):
-    """
-    تلخيص نص البحث باستخدام BioGPT
-    """
-    inputs = bio_tokenizer(text, return_tensors="pt", truncation=True, max_length=1024).to(DEVICE)
-    summary_ids = bio_model.generate(
-        **inputs,
-        max_length=150,
-        min_length=40,
-        length_penalty=2.0,
-        num_beams=4
-    )
-    summary = bio_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-    return summary