Update game1.py

game1.py

@@ -114,7 +114,8 @@ def func1(lang_selected, num_selected, human_predict, num1, num2, user_important
     # Use a pipeline as a high-level helper
     from transformers import pipeline
 
-    classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+    classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer, device=device)
     output = classifier([text['text']])
 
     star2num = {
@@ -322,7 +323,8 @@ def func1_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("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
+    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+    classifier = pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment", device=device)
     
     output = classifier([text_written])
 
@@ -353,10 +355,12 @@ def func1_written(text_written, human_predict, lang_written):
     import shap
 
     # sentiment_classifier = pipeline("text-classification", return_all_scores=True)
+    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+
     if lang_written == "Dutch":
-        sentiment_classifier = pipeline("text-classification", model='DTAI-KULeuven/robbert-v2-dutch-sentiment', return_all_scores=True)
+        sentiment_classifier = pipeline("text-classification", model='DTAI-KULeuven/robbert-v2-dutch-sentiment', return_all_scores=True, device=device)
     else:
-        sentiment_classifier = pipeline("text-classification", model='distilbert-base-uncased-finetuned-sst-2-english', return_all_scores=True)
+        sentiment_classifier = pipeline("text-classification", model='distilbert-base-uncased-finetuned-sst-2-english', return_all_scores=True, device=device)
 
     explainer = shap.Explainer(sentiment_classifier)