Update app.py

app.py

@@ -11,12 +11,11 @@ models = {
 tokenizers = {}
 model_instances = {}
 
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
 for name, path in models.items():
     tokenizers[name] = AutoTokenizer.from_pretrained(path)
-    model_instances[name] = AutoModelForSequenceClassification.from_pretrained(path)
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-model_instances["RUSpam/spamNS_v1"] = model_instances["RUSpam/spamNS_v1"].to(device).eval()
+    model_instances[name] = AutoModelForSequenceClassification.from_pretrained(path).to(device).eval()
 
 def clean_text(text):
     text = re.sub(r'http\S+', '', text)
@@ -29,6 +28,8 @@ def predict_spam_deberta(text):
     model = model_instances["RUSpam/spam_deberta_v4"]
 
     inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
+    inputs = {key: val.to(device) for key, val in inputs.items()}  # Move inputs to the device
+
     with torch.no_grad():
         outputs = model(**inputs)
         logits = outputs.logits