Create app.py

app.py

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+from transformers import pipeline
+depth_estimator = pipeline(task="depth-estimation",
+                        model="Intel/dpt-hybrid-midas")
+import os
+from PIL import Image
+import torch
+import numpy as np
+import gradio as gr
+def launch(input_image):
+    out = depth_estimator(input_image)
+
+    # resize the prediction
+    prediction = torch.nn.functional.interpolate(
+        out["predicted_depth"].unsqueeze(1),
+        size=input_image.size[::-1],
+        mode="bicubic",
+        align_corners=False,
+    )
+    # normalize the prediction
+    output = prediction.squeeze().numpy()
+    formatted = (output * 255 / np.max(output)).astype("uint8")
+    depth = Image.fromarray(formatted)
+    return depth
+iface = gr.Interface(launch,
+                     inputs=gr.Image(type='pil'),
+                     outputs=gr.Image(type='pil'))
+iface.launch()