Update app.py

app.py

@@ -1,5 +1,5 @@
 import gradio as gr
-from gradio_client import Client, handle_file
+from gradio_client import Client, file
 from PIL import Image
 import numpy as np
 import io
@@ -8,27 +8,33 @@ import tempfile
 def process_image(image):
     client = Client("prs-eth/marigold")
     
-    # Convert the PIL Image or numpy array to bytes and save temporarily
-    if isinstance(image, np.ndarray):
-        image = Image.fromarray(image.astype('uint8'), 'RGB')
+    # Save the uploaded image temporarily
     with tempfile.NamedTemporaryFile(delete=False, suffix=".jpeg") as tmp:
         image.save(tmp, format='JPEG')
-        tmp_path = tmp.name  # Get the path of the saved file
+        tmp_path = tmp.name
 
-    # Call the API with necessary parameters using the file path
+    # Call the API with necessary parameters
     result = client.predict(
-        handle_file(tmp_path),  # Pass the file path
+        file(tmp_path),  # filepath for 'Input Image' Image component
         20,  # Ensemble size
-        10,  # Denoising steps
-        "recommended",  # Processing resolution
+        10,  # Number of denoising steps
+        "0",  # Processing resolution
+        file(tmp_path),  # Placeholder for 'Predicted depth (16-bit)'
+        file(tmp_path),  # Placeholder for 'Predicted depth (32-bit)'
+        file(tmp_path),  # Placeholder for 'Predicted depth (red-near, blue-far)'
+        0,  # Relative position of the near plane
+        0,  # Relative position of the far plane
+        0,  # Embossing level
+        1,  # Size of the smoothing filter
+        -100,  # Frame's near plane offset
         api_name="/submit_depth_fn"
     )
 
     # Handle the returned file path for the depth image
     if result and 'depth_outputs' in result:
-        depth_image_url = result['depth_outputs'][0]  # Assuming the result is a URL to the image
-        response = requests.get(depth_image_url)  # Download the image
-        depth_image = Image.open(io.BytesIO(response.content))
+        depth_image_path = result['depth_outputs'][0]  # Assuming the result is a local path to the image
+        depth_image = Image.open(depth_image_path)
+        depth_image.load()  # Ensure the image is loaded completely
         return depth_image
     else:
         return "No depth output received or error in processing"