Update app.py

app.py

@@ -85,17 +85,26 @@ async def load_models():
         global_models.bio_tokenizer = AutoTokenizer.from_pretrained("blaze999/Medical-NER")
         global_models.bio_model = AutoModelForTokenClassification.from_pretrained("blaze999/Medical-NER")
 
-        # Load embeddings data
-        with open('embeddings.pkl', 'rb') as file:
-            global_models.embeddings_data = pickle.load(file)
-
+        # Load embeddings data with better error handling
+        try:
+            with open('embeddings.pkl', 'rb') as file:
+                global_models.embeddings_data = pickle.load(file)
+        except (FileNotFoundError, pickle.UnpicklingError) as e:
+            print(f"Error loading embeddings data: {e}")
+            raise HTTPException(status_code=500, detail="Failed to load embeddings data.")
+        
         # Load URL mapping data
-        df = pd.read_excel('finalcleaned_excel_file.xlsx')
-        global_models.file_name_to_url = {f"article_{index}.html": url for index, url in enumerate(df['Unnamed: 0'])}
-
+        try:
+            df = pd.read_excel('finalcleaned_excel_file.xlsx')
+            global_models.file_name_to_url = {f"article_{index}.html": url for index, url in enumerate(df['Unnamed: 0'])}
+        except Exception as e:
+            print(f"Error loading URL mapping data: {e}")
+            raise HTTPException(status_code=500, detail="Failed to load URL mapping data.")
+        
     except Exception as e:
         print(f"Error loading models: {e}")
-        raise
+        raise HTTPException(status_code=500, detail="Failed to load models.")
+
 
 def translate_ar_to_en(text):
     try: