Upload folder using huggingface_hub

app.py

@@ -1,35 +1,53 @@
 import os
+from typing import Any
+
 import gradio as gr
 import torch
 import numpy as np
 from transformers import pipeline
 from PIL import Image
 
-
+# Load the depth estimation model from Hugging Face Transformers
 depth_estimator = pipeline(task="depth-estimation", model="Intel/dpt-hybrid-midas")
 
+def launch(input_image: Image.Image) -> Image.Image:
+    """
+    Process an input image to estimate its depth map.
 
-def launch(input_image):
+    Args:
+    input_image: An image object as received from the Gradio interface.
+
+    Returns:
+    A PIL Image object representing the depth map.
+    """
+    # Generate depth estimation from the input image
     out = depth_estimator(input_image)
 
-    # resize the prediction
+    # Resize the prediction to match the input image size
     prediction = torch.nn.functional.interpolate(
         out["predicted_depth"].unsqueeze(1),
-        size=input_image.size[::-1],
+        size=input_image.size[::-1],  # PIL images use width x height, whereas torch uses height x width
         mode="bicubic",
         align_corners=False,
     )
 
-    # normalize the prediction
+    # Normalize the prediction to be in the range [0, 255]
     output = prediction.squeeze().numpy()
     formatted = (output * 255 / np.max(output)).astype("uint8")
+
+    # Convert the numpy array back to a PIL image
     depth = Image.fromarray(formatted)
     return depth
 
-
-iface = gr.Interface(launch, 
-                     inputs=gr.Image(type='pil'), 
-                     outputs=gr.Image(type='pil'))
-
-
-iface.launch(share=True)
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=launch,
+    inputs=gr.inputs.Image(type='pil'),
+    outputs=gr.outputs.Image(type='pil'),
+    title="Depth Estimation",
+    description="Upload an image to estimate its depth map."
+)
+
+# Launch the Gradio app with sharing option enabled
+if __name__ == "__main__":
+    iface.launch(share=True)