Update app.py

app.py

@@ -13,20 +13,19 @@ model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
 def process_image(image_path):
     image_path = Path(image_path)
     image = Image.open(image_path)
-
+    
     # if wider than 512 pixels let's resample to keep it performant on phones etc
-    if (image.size[0] > 512):
-        image = image.resize((512,int(512*image.size[1]/image.size[0])),Image.Resampling.LANCZOS)
-
+    if image.size[0] > 512:
+        image = image.resize((512, int(512 * image.size[1] / image.size[0])), Image.Resampling.LANCZOS)
+    
     # prepare image for the model
-    # encoding = feature_extractor(image, return_tensors="pt")
-    inputs = processor(images=image, return_tensors="pt")
-
+    inputs = feature_extractor(images=image, return_tensors="pt")
+    
     # forward pass
     with torch.no_grad():
-        outputs = model(**encoding)
+        outputs = model(**inputs)
         predicted_depth = outputs.predicted_depth
-
+    
     # interpolate to original size
     prediction = torch.nn.functional.interpolate(
         predicted_depth.unsqueeze(1),
@@ -34,11 +33,11 @@ def process_image(image_path):
         mode="bicubic",
         align_corners=False,
     ).squeeze()
+    
     output = prediction.cpu().numpy()
     depth = (output * 255 / np.max(output)).astype('uint8')
-
-    h = depthviewer2html(image,depth)
-    return [h]
+    h = depthviewer2html(image, depth)
+    return h  # No need for list wrapper with single output
 
 title = "3d Visualization of Depth Maps Generated using MiDaS"
 description = "Improved 3D interactive depth viewer using Three.js embedded in a Gradio app. For more details see the <a href='https://colab.research.google.com/drive/1l2l8U7Vhq9RnvV2tHyfhrXKNuHfmb4IP?usp=sharing'>Colab Notebook.</a>"
@@ -54,4 +53,4 @@ iface = gr.Interface(fn=process_image,
                      cache_examples=False,
                      css='#depth-viewer: {height:300px;}')
 
-iface.launch(debug=True)
+iface.launch(server_name="0.0.0.0", debug=True)