Update app.py

app.py

@@ -1,5 +1,5 @@
 import gradio as gr
-from gradio_client import Client, file
+from gradio_client import Client, handle_file
 from PIL import Image
 import numpy as np
 import io
@@ -8,20 +8,24 @@ import tempfile
 def process_image(image):
     client = Client("prs-eth/marigold")
     
-    # Save the uploaded image temporarily
+    # Convert numpy array to PIL Image if needed
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image.astype('uint8'), 'RGB')
+
+    # Save the PIL Image to a temporary file
     with tempfile.NamedTemporaryFile(delete=False, suffix=".jpeg") as tmp:
         image.save(tmp, format='JPEG')
-        tmp_path = tmp.name
+        tmp_path = tmp.name  # Get the file path
 
     # Call the API with necessary parameters
     result = client.predict(
-        file(tmp_path),  # filepath for 'Input Image' Image component
+        handle_file(tmp_path),  # filepath for 'Input Image' Image component
         20,  # Ensemble size
         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)'
+        handle_file(tmp_path),  # Placeholder for 'Predicted depth (16-bit)'
+        handle_file(tmp_path),  # Placeholder for 'Predicted depth (32-bit)'
+        handle_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
@@ -32,7 +36,7 @@ def process_image(image):
 
     # Handle the returned file path for the depth image
     if result and 'depth_outputs' in result:
-        depth_image_path = result['depth_outputs'][0]  # Assuming the result is a local path to the image
+        depth_image_path = result['depth_outputs'][0]
         depth_image = Image.open(depth_image_path)
         depth_image.load()  # Ensure the image is loaded completely
         return depth_image