revert back to cpu : (

interfaces/cap.py

@@ -83,9 +83,9 @@ def build_huggingface_path(language: str, domain: str):
     else:
         return "poltextlab/xlm-roberta-large-pooled-cap"
 
-@spaces.GPU
+#@spaces.GPU
 def predict(text, model_id, tokenizer_id):
-    device = torch.device("cuda:0")
+    device = torch.device("cpu")
     model = AutoModelForSequenceClassification.from_pretrained(model_id, low_cpu_mem_usage=True, device_map="auto", offload_folder="offload", token=HF_TOKEN).to(device)
     tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
 

interfaces/cap_minor_media.py

@@ -57,9 +57,9 @@ def check_huggingface_path(checkpoint_path: str):
 def build_huggingface_path(language: str, domain: str):
     return ("poltextlab/xlm-roberta-large-pooled-cap-media", "poltextlab/xlm-roberta-large-pooled-cap-minor-v3")
 
-@spaces.GPU(duration=30)
+#@spaces.GPU(duration=30)
 def predict(text, major_model_id, minor_model_id, tokenizer_id, HF_TOKEN=None):
-    device = torch.device("cuda:0")
+    device = torch.device("cpu")
 
     # Load major and minor models + tokenizer
     major_model = AutoModelForSequenceClassification.from_pretrained(