Update app.py

app.py

@@ -28,6 +28,10 @@ import numpy as np
 import tempfile
 from pathlib import Path
 
+# Add at the top of app.py after imports
+if not hasattr(np, '__version__') or tuple(map(int, np.__version__.split('.'))) < (1, 25, 0):
+    raise ImportError("This application requires numpy >= 1.25.0")
+
 # Configure logging
 logging.basicConfig(level=logging.INFO)
 
@@ -65,29 +69,32 @@ def load_sentence_transformer():
         cache_dir = Path('/cache')
         cache_dir.mkdir(parents=True, exist_ok=True)
         
-        model_embedding = SentenceTransformer(
-            "jinaai/jina-embeddings-v3",
-            trust_remote_code=True,
-            cache_folder=str(cache_dir)
-        ).to(device)
-
-        # Load and optimize model state dict
-        if os.path.exists(model_path):
-            state_dict = torch.load(model_path, map_location=device)
-
-            # Dequantize if needed
-            for key, tensor in state_dict.items():
-                if hasattr(tensor, 'dequantize'):
-                    state_dict[key] = tensor.dequantize().to(dtype=torch.float32)
-                elif tensor.dtype == torch.bfloat16:
-                    state_dict[key] = tensor.to(dtype=torch.float32)
-
-            model_embedding.load_state_dict(state_dict)
-            print("SentenceTransformer model loaded successfully.")
+        # Ensure numpy version compatibility
+        if tuple(map(int, np.__version__.split('.'))) >= (1, 25, 0):
+            model_embedding = SentenceTransformer(
+                "jinaai/jina-embeddings-v3",
+                trust_remote_code=True,
+                cache_folder=str(cache_dir)
+            ).to(device)
+
+            if os.path.exists(model_path):
+                state_dict = torch.load(model_path, map_location=device)
+                
+                # Handle tensor types
+                for key, tensor in state_dict.items():
+                    if hasattr(tensor, 'dequantize'):
+                        state_dict[key] = tensor.dequantize().to(dtype=torch.float32)
+                    elif tensor.dtype == torch.bfloat16:
+                        state_dict[key] = tensor.to(dtype=torch.float32)
+
+                model_embedding.load_state_dict(state_dict)
+                print("SentenceTransformer model loaded successfully.")
+            else:
+                print(f"Warning: Model file not found at {model_path}")
+                
+            return model_embedding
         else:
-            print(f"Warning: Model file not found at {model_path}")
-            
-        return model_embedding
+            raise ImportError("Incompatible numpy version")
     except Exception as e:
         print(f"Error loading model: {str(e)}")
         raise