Update app.py

app.py

@@ -28,9 +28,9 @@ 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")
+# Update the numpy version check at the top of the file
+if not hasattr(np, '__version__') or tuple(map(int, np.__version__.split('.'))) > (1, 24, 0):
+    print(f"Warning: Using numpy version {np.__version__}. Some features may not work properly.")
 
 # Configure logging
 logging.basicConfig(level=logging.INFO)
@@ -69,32 +69,34 @@ def load_sentence_transformer():
         cache_dir = Path('/cache')
         cache_dir.mkdir(parents=True, exist_ok=True)
         
-        # 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
+        # Import einops here to ensure it's available
+        try:
+            import einops
+        except ImportError:
+            raise ImportError("einops is required. Please install it with 'pip install einops'")
+        
+        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:
-            raise ImportError("Incompatible numpy version")
+            print(f"Warning: Model file not found at {model_path}")
+            
+        return model_embedding
     except Exception as e:
         print(f"Error loading model: {str(e)}")
         raise