Create app.py

app.py

@@ -0,0 +1,25 @@
+import gradio as gr
+from transformers import pipeline
+
+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()