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 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(label="Upload image", type="pil")], 
+                     outputs=[gr.Image(label="Depth Map", type="pil")],
+                     title="DepthSense",
+                     description="Dive into the unseen depths of your images! Simply upload and let DepthSense reveal a whole new dimension of your visuals, instantly" )
+
+iface.launch()