Update app.py

app.py

@@ -1,3 +1,6 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
 import gradio as gr
 import torch
 import numpy as np
@@ -10,6 +13,10 @@ from scipy.ndimage import binary_opening, binary_closing, sobel, binary_dilation
 import zipfile
 import io
 import os
+import tempfile
+import shutil
+import warnings
+warnings.filterwarnings('ignore')
 
 # グローバル変数
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -342,23 +349,26 @@ def process_image(image, max_size, tile_size, tile_overlap, min_area, mesh_res,
     output_data = {'metadata': metadata, 'meshes': meshes}
     json_str = json.dumps(output_data, ensure_ascii=False, indent=2)
     
-    # ZIPファイル作成
-    zip_buffer = io.BytesIO()
-    with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zip_file:
+    # ZIPファイル作成（一時ファイルとして保存）
+    import tempfile
+    import shutil
+    
+    temp_dir = tempfile.mkdtemp()
+    zip_path = os.path.join(temp_dir, 'city_3d_output.zip')
+    
+    with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zip_file:
         zip_file.writestr('city_3d_model.json', json_str)
         
         # セグメンテーション画像
         _, buffer = cv2.imencode('.png', cv2.cvtColor(colored_seg, cv2.COLOR_RGB2BGR))
         zip_file.writestr('segmentation_result.png', buffer.tobytes())
     
-    zip_buffer.seek(0)
-    
     # 統計情報
     stats = f"総セグメント数: {len(meshes)}\n\n"
     for cat, info in metadata['categories'].items():
         stats += f"{info['label']}: {info['count']}個\n"
     
-    return colored_seg, overlay, stats, zip_buffer.getvalue()
+    return colored_seg, overlay, stats, zip_path
 
 # Gradio UI
 with gr.Blocks(title="3D City Map Generator") as demo: