removed to(device)

interfaces/cap.py

@@ -85,7 +85,6 @@ def build_huggingface_path(language: str, domain: str):
 
 @spaces.GPU(duration=5)
 def predict(text, model_id, tokenizer_id):
-    device = torch.device("cpu")
     model = AutoModelForSequenceClassification.from_pretrained(model_id, low_cpu_mem_usage=True, device_map="auto", offload_folder="offload", token=HF_TOKEN)
     tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
 
@@ -93,7 +92,7 @@ def predict(text, model_id, tokenizer_id):
                        max_length=256,
                        truncation=True,
                        padding="do_not_pad",
-                       return_tensors="pt").to(device)
+                       return_tensors="pt")
     model.eval()
 
     with torch.no_grad():

interfaces/cap_minor_media.py

@@ -59,8 +59,6 @@ def build_huggingface_path(language: str, domain: str):
 
 @spaces.GPU(duration=5)
 def predict(text, major_model_id, minor_model_id, tokenizer_id, HF_TOKEN=None):
-    device = torch.device("cpu")
-
     # Load major and minor models + tokenizer
     major_model = AutoModelForSequenceClassification.from_pretrained(
         major_model_id,
@@ -68,7 +66,7 @@ def predict(text, major_model_id, minor_model_id, tokenizer_id, HF_TOKEN=None):
         device_map="auto",
         offload_folder="offload",
         token=HF_TOKEN
-    ).to(device)
+    )
 
     minor_model = AutoModelForSequenceClassification.from_pretrained(
         minor_model_id,
@@ -76,12 +74,12 @@ def predict(text, major_model_id, minor_model_id, tokenizer_id, HF_TOKEN=None):
         device_map="auto",
         offload_folder="offload",
         token=HF_TOKEN
-    ).to(device)
+    )
 
     tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
 
     # Tokenize input
-    inputs = tokenizer(text, max_length=256, truncation=True, padding="do_not_pad", return_tensors="pt").to(device)
+    inputs = tokenizer(text, max_length=256, truncation=True, padding="do_not_pad", return_tensors="pt")
 
     # Predict major topic
     major_model.eval()