Update app.py

app.py

@@ -1,56 +1,31 @@
 import gradio as gr
-from gradio_client import Client, file
+from gradio_client import Client, handle_file
 
 def predict_depth(image):
-    client = Client("prs-eth/marigold")  # Adjust the username/modelname as needed
-
-    # Assuming 'image' is the input image file from the user
-    response = client.predict(
-        file(image),  # Image file path
-        1,  # Ensemble size (example value)
-        10,  # Number of denoising steps (example value)
-        "0",  # Processing resolution (example value)
-        file('path_to_sample_file_1.pdf'),  # Sample file path for depth (16-bit)
-        file('path_to_sample_file_2.pdf'),  # Sample file path for depth (32-bit)
-        file('path_to_sample_file_3.pdf'),  # Sample file path for depth (color)
-        0.5,  # Relative position of the near plane
-        0.9,  # Relative position of the far plane
-        10,  # Embossing level
-        2,  # Smoothing filter size
-        -50,  # Frame's near plane offset
+    client = Client("prs-eth/marigold")
+    
+    # Prepare the API call with the necessary parameters
+    result = client.predict(
+        handle_file(image),  # Image file path
+        1,  # Example value for 'Ensemble size'
+        10,  # Example value for 'Number of denoising steps'
+        "0",  # Processing resolution choice
         api_name="/submit_depth_fn"
     )
-    # Assuming the API returns a filepath or you save the result to a file
-    output_file_path = save_image_to_file(response['image'])  # Implement this function based on your API's response
-    return output_file_path
+    
+    # Process the API response assuming it directly returns a file path for the depth map
+    if result and result[0]:  # Assuming the API returns the image path as the first item in a tuple
+        output_file_path = result[0]  # Directly use the returned file path
+        return output_file_path  # Return the path to the output file for download
+    return "No depth output available"
 
 # Gradio Interface
 iface = gr.Interface(
     fn=predict_depth,
     inputs=gr.Image(type='filepath', label="Upload your image"),
-    outputs=gr.Image(type='filepath', label="Depth Map Image"),
+    outputs=gr.File(label="Download Depth Map"),
     title="Depth Map Generator",
     description="Upload an image to receive a depth map file."
 )
 
 iface.launch()
-
-def save_image_to_file(image_data):
-    # Assuming 'image_data' is the image data in a compatible format
-    # You would save the image to a file and return the path
-    import matplotlib.pyplot as plt
-    import matplotlib.image as mpimg
-    import numpy as np
-    import tempfile
-    import os
-
-    # Create a temporary file
-    fd, path = tempfile.mkstemp(suffix=".png")
-    try:
-        # Assume image_data is a numpy array
-        img = np.array(image_data).astype(np.uint8)
-        plt.imsave(path, img)
-    finally:
-        os.close(fd)
-
-    return path