fix: resolve meta tensor issues by forcing CPU device mapping

Updated model loading in app.py to explicitly use CPU device with torch_dtype=torch.float32 and device_map=None to prevent meta tensor issues during inference. The model is now forcibly moved to CPU device after loading to ensure consistent behavior across different environments.

app.py

@@ -7,9 +7,18 @@ from io import BytesIO
 
 fashion_items = ['top', 'trousers', 'jumper']
 
-# Load model and processor
+# Load model and processor with CPU device to avoid meta tensor issues
 model_name = 'Marqo/marqo-fashionSigLIP'
-model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
+
+# Force CPU usage to avoid device mapping issues
+device = torch.device('cpu')
+model = AutoModel.from_pretrained(
+    model_name,
+    trust_remote_code=True,
+    torch_dtype=torch.float32,
+    device_map=None  # This forces the model to stay on CPU
+).to(device)
+
 processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
 
 # Preprocess and normalize text data