Create app.py

app.py

@@ -0,0 +1,45 @@
+from zoedepth.utils.config import get_config
+from zoedepth.models.builder import build_model
+
+import gradio as gr
+import torch
+
+from depth import depth_interface
+from mesh import mesh_interface
+
+
+css = """
+#img-display-container {
+    max-height: 50vh;
+    }
+#img-display-input {
+    max-height: 40vh;
+    }
+#img-display-output {
+    max-height: 40vh;
+    }
+    
+"""
+DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
+model = torch.hub.load('isl-org/ZoeDepth', "ZoeD_N", pretrained=True).to(DEVICE).eval()
+
+# config_mode="infer"
+# pretrained_resource = f"local::C:/Users/Charl/.cache/torch/hub/checkpoints/ZoeD_M12_N.pt"
+# config = get_config("zoedepth", config_mode, pretrained_resource=pretrained_resource)
+# model = build_model(config).to(DEVICE).eval()
+
+# title = "# ZoeDepth"
+# description = """Official demo for **ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth**."""
+
+with gr.Blocks(css=css) as API:
+    # gr.Markdown(title)
+    # gr.Markdown(description)
+    with gr.Tab("Depth Prediction"):
+        depth_interface(model)
+    with gr.Tab("Image to 3D"):
+        mesh_interface(model)
+    # with gr.Tab("360 Panorama to 3D"):
+    #     create_pano_to_3d_demo(model)
+
+if __name__ == '__main__':
+    API.launch()