Update game3.py

game3.py

@@ -47,7 +47,8 @@ def func3(num_selected, human_predict, num1, num2, user_important):
     # Load model directly
     # Use a pipeline as a high-level helper
 
-    classifier = pipeline("text-classification", model="padmajabfrl/Gender-Classification")
+    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+    classifier = pipeline("text-classification", model="padmajabfrl/Gender-Classification", device=device)
     output = classifier([text['text']])
 
     print(output)
@@ -153,7 +154,8 @@ def func3_written(text_written, human_predict, lang_written):
     # tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
     # model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
 
-    classifier = pipeline("text-classification", model="padmajabfrl/Gender-Classification")
+    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+    classifier = pipeline("text-classification", model="padmajabfrl/Gender-Classification", device=device)
 
     output = classifier([text_written])
 
@@ -173,7 +175,7 @@ def func3_written(text_written, human_predict, lang_written):
 
     import shap
 
-    gender_classifier = pipeline("text-classification", model="padmajabfrl/Gender-Classification", return_all_scores=True)
+    gender_classifier = pipeline("text-classification", model="padmajabfrl/Gender-Classification", return_all_scores=True, device=device)
 
     explainer = shap.Explainer(gender_classifier)