Upload app.py

app.py

@@ -286,8 +286,8 @@ def visualize_predictions(image: Image.Image, predictions, threshold=0.45):
 
 # 定数
 REPO_ID = "cella110n/cl_tagger"
-MODEL_FILENAME = "cl_eva02_tagger_v1_250426/model_optimized.onnx"
-# MODEL_FILENAME = "cl_eva02_tagger_v1_250426/model.onnx" # Use non-optimized if needed
+# MODEL_FILENAME = "cl_eva02_tagger_v1_250426/model_optimized.onnx"
+MODEL_FILENAME = "cl_eva02_tagger_v1_250426/model.onnx" # Use non-optimized if needed
 TAG_MAPPING_FILENAME = "cl_eva02_tagger_v1_250426/tag_mapping.json"
 CACHE_DIR = "./model_cache"
 
@@ -323,6 +323,35 @@ def initialize_model():
     if onnx_session is None:
         model_path, tag_mapping_path = download_model_files()
         print("Loading model and labels...")
+
+        # --- Added Logging ---
+        print("--- Environment Check ---")
+        try:
+            import torch
+            print(f"PyTorch version: {torch.__version__}")
+            if torch.cuda.is_available():
+                print(f"PyTorch CUDA available: True")
+                print(f"PyTorch CUDA version: {torch.version.cuda}")
+                print(f"Detected GPU: {torch.cuda.get_device_name(0)}")
+                if torch.backends.cudnn.is_available():
+                    print(f"PyTorch cuDNN available: True")
+                    print(f"PyTorch cuDNN version: {torch.backends.cudnn.version()}")
+                else:
+                    print("PyTorch cuDNN available: False")
+            else:
+                print("PyTorch CUDA available: False")
+        except ImportError:
+            print("PyTorch not found.")
+        except Exception as e:
+            print(f"Error during PyTorch check: {e}")
+
+        try:
+             print(f"ONNX Runtime build info: {ort.get_buildinfo()}")
+        except Exception as e:
+             print(f"Error getting ONNX Runtime build info: {e}")
+        print("-------------------------")
+        # --- End Added Logging ---
+
         # ONNXセッションの初期化 (GPU優先)
         available_providers = ort.get_available_providers()
         print(f"Available ONNX Runtime providers: {available_providers}")