Create app.py

app.py

@@ -0,0 +1,59 @@
+import gradio as gr
+from huggingface_hub import hf_hub_download, HfFolder
+from PIL import Image
+import requests, torch
+import numpy as np
+from io import BytesIO
+import plotly.graph_objects as go
+
+def load_ScanNet_sample(data_path):
+    
+    all_data = torch.load(data_path)
+    
+    point = np.array(all_data['coord'])
+    color = np.array(all_data['color'])
+ 
+    point = point - point.min(axis=0)
+    point = point / point.max(axis=0)
+    color = color / 255.
+    return point, color
+
+def show_logo():
+    repo_id = "ZiyuG/Cache"
+    filename = "scene0000_00.pth"
+    token = HfFolder.get_token()  # Ensure you are logged in via huggingface-cli login
+
+    try:
+        file_path = hf_hub_download(repo_id=repo_id, filename=filename, use_auth_token=token, repo_type='dataset')
+        point, color = load_ScanNet_sample(file_path)
+        if point.shape[0] > 100000:
+            indices = np.random.choice(point.shape[0], 100000, replace=False)
+            point = point[indices]
+            color = color[indices]
+    except Exception as e:
+        print(e)
+        point = np.random.rand(8000, 3)
+        color = np.random.rand(8000, 3)
+    
+    fig = go.Figure(
+        data=[
+            go.Scatter3d(
+                x=point[:,0], y=point[:,1], z=point[:,2],
+                mode='markers',
+                marker=dict(size=1, color=color, opacity=0.5),
+            )
+        ],
+        layout=dict(
+            scene=dict(
+                xaxis=dict(visible=False),
+                yaxis=dict(visible=False),
+                zaxis=dict(visible=False),
+                aspectratio=dict(x=1, y=1, z=1), 
+                camera=dict(eye=dict(x=1.5, y=1.5, z=1.5))
+            )
+        )
+    )
+    return fig
+
+iface = gr.Interface(fn=show_logo, inputs=[], outputs=gr.Plot(), title="Display Logo")
+iface.launch(share=True)