update 1

app.py

@@ -78,16 +78,14 @@ label_to_class = requests.get(label_map_url).json()
 # Load your custom model from Hugging Face
 model = FineGrainedClassifier(num_classes=len(label_to_class))
 model_checkpoint = "Maverick98/EcommerceClassifier"
-model.load_state_dict(torch.hub.load_state_dict_from_url(f"https://huggingface.co/{model_checkpoint}/resolve/main/model_checkpoint.pth", map_location=torch.device('cpu')))
+checkpoint_url = f"https://huggingface.co/Maverick98/EcommerceClassifier/resolve/main/model_checkpoint.pth"
+checkpoint = torch.hub.load_state_dict_from_url(checkpoint_url, map_location=torch.device('cpu'))
+# Extract and load the model state_dict
+model.load_state_dict(checkpoint['model_state_dict'])
+
 # Load the tokenizer from Jina
 tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v2-base-en")
 
-# # Define image preprocessing
-# transform = transforms.Compose([
-#     transforms.Resize((224, 224)),
-#     transforms.ToTensor(),
-#     transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-# ])
 
 def load_image(image_path_or_url):
     """