Update app.py

app.py

@@ -13,22 +13,27 @@ import os
 st.set_page_config(page_title="Volume Estimator", layout="wide")
 st.title("Volume Estimation using SAM Segmentation + MiDaS Depth")
 
-# Load SAM and MiDaS models
 @st.cache_resource
 def load_models():
-    # Download SAM checkpoint from Hugging Face
+    import requests
+    import os
+
+    # ✅ Use Hugging Face public model file URL
     checkpoint_url = "https://huggingface.co/HCMUE-Research/SAM-vit-h/resolve/main/sam_vit_h_4b8939.pth"
     checkpoint_path = "sam_vit_h_4b8939.pth"
+
+    # Download only if not already present
     if not os.path.exists(checkpoint_path):
+        st.info("Downloading SAM model checkpoint...")
+        response = requests.get(checkpoint_url)
         with open(checkpoint_path, "wb") as f:
-            f.write(requests.get(checkpoint_url).content)
+            f.write(response.content)
 
-    # Load SAM
     device = "cuda" if torch.cuda.is_available() else "cpu"
     sam = sam_model_registry["vit_h"](checkpoint=checkpoint_path).to(device)
     predictor = SamPredictor(sam)
 
-    # Load MiDaS
+    # Load MiDaS model
     midas = torch.hub.load("intel-isl/MiDaS", "DPT_Large")
     midas.eval()
     midas_transform = Compose([
@@ -38,6 +43,7 @@ def load_models():
     ])
     return predictor, midas, midas_transform
 
+
 predictor, midas_model, midas_transform = load_models()
 
 # Input source selection