Update README: use snapshot_download for inference

README.md

@@ -106,27 +106,29 @@ This model uses a custom PyTorch architecture. The model code is included in the
 ### Installation
 
 ```bash
-pip install torch torchvision timm safetensors
+pip install torch torchvision timm safetensors huggingface_hub
 ```
 
 ### Inference
 
 ```python
+import sys
 import torch
+from huggingface_hub import snapshot_download
 from safetensors.torch import load_file
 from torchvision.transforms import functional as TF
 from PIL import Image
 
-# 1. Clone or download the repo files
-# git clone https://huggingface.co/andreribeiro87/unet3plus-efficientnet-kvasir-seg
-# cd unet3plus-efficientnet-kvasir-seg
+# 1. Download the full repo (weights + model code) — handles LFS automatically
+repo_dir = snapshot_download("andreribeiro87/unet3plus-efficientnet-kvasir-seg")
+sys.path.insert(0, repo_dir)
 
-# 2. Import the model
+# 2. Import and instantiate the model
 from models import create_model
-
-# 3. Instantiate and load weights
 model = create_model("unet3plus", backbone="efficientnet")
-state_dict = load_file("model.safetensors")
+
+# 3. Load weights
+state_dict = load_file(f"{repo_dir}/model.safetensors")
 model.load_state_dict(state_dict)
 model.eval()