trial

app.py

@@ -56,19 +56,20 @@ def predict(img, confidence):
 
     # Download model from GCS
     model_path = os.getenv('MODEL_PATH')
+    print(model_path)
     response = requests.get(model_path)
     time.sleep(20)
 
-    # Define the cache directory path
-    cache_dir = os.path.expanduser("~/.cache/torch/hub/checkpoints")
-    model_file_name = os.path.basename(model_path)
-    model_file_path = os.path.join(cache_dir, model_file_name)
-    print(model_file_path)
+    # # Define the cache directory path
+    # cache_dir = os.path.expanduser("~/.cache/torch/hub/checkpoints")
+    # model_file_name = os.path.basename(model_path)
+    # model_file_path = os.path.join(cache_dir, model_file_name)
+    # print(model_file_path)
 
-    with open(model_file_path, 'wb') as f:
+    with open('model.pt', 'wb') as f:
         f.write(response.content)
 
-    checkpoint = torch.load(model_file_path, map_location=device)
+    checkpoint = torch.load('model.pt', map_location=device)
     densenet.load_state_dict(checkpoint['model_state_dict'])
 
     densenet.eval()