switch to zero gpu

app.py

@@ -17,18 +17,27 @@ os.system('cp -rf /home/user/app/assets/conversions.py /home/user/.local/lib/pyt
 DEFAULT_MODEL='smpler_x_h32'
 OUT_FOLDER = '/home/user/app/demo_out'
 os.makedirs(OUT_FOLDER, exist_ok=True)
-num_gpus = 1 if torch.cuda.is_available() else -1
+# num_gpus = 1 if torch.cuda.is_available() else -1
 # print("!!!", torch.cuda.is_available())      
 # print(torch.cuda.device_count())   
 # print(torch.version.cuda)  
 # index = torch.cuda.current_device()
 # print(index)  
 # print(torch.cuda.get_device_name(index))
-from main.inference import Inferer
-inferer = Inferer(DEFAULT_MODEL, num_gpus, OUT_FOLDER)
+# from main.inference import Inferer
+# inferer = Inferer(DEFAULT_MODEL, num_gpus, OUT_FOLDER)
 
 @spaces.GPU(enable_queue=True)
 def infer(video_input, in_threshold=0.5, num_people="Single person", render_mesh=False):
+    num_gpus = 1 if torch.cuda.is_available() else -1
+    print("!!!", torch.cuda.is_available())      
+    print(torch.cuda.device_count())   
+    print(torch.version.cuda)  
+    index = torch.cuda.current_device()
+    print(index)  
+    print(torch.cuda.get_device_name(index))
+    from main.inference import Inferer
+    inferer = Inferer(DEFAULT_MODEL, num_gpus, OUT_FOLDER)
     os.system(f'rm -rf {OUT_FOLDER}/*')
     multi_person = False if (num_people == "Single person") else True
     cap = cv2.VideoCapture(video_input)