Update app.py

app.py

@@ -15,9 +15,12 @@ midas_transform = torch.hub.load("intel-isl/MiDaS", "transforms").default_transf
 
 def estimate_depth(image):
     image = image.convert("RGB")
-    img_tensor = midas_transform(image).to(device)
+    image_np = np.array(image) / 255.0  # Convert PIL image to NumPy and normalize
+    image_tensor = torch.tensor(image_np, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0).to(device)
+    
     with torch.no_grad():
-        depth = midas_model(img_tensor).squeeze().cpu().numpy()
+        depth = midas_model(image_tensor).squeeze().cpu().numpy()
+    
     depth = cv2.resize(depth, (image.size[0], image.size[1]))
     depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255
     return depth.astype(np.uint8)