Update pipeline.py

pipeline.py

@@ -5,7 +5,7 @@ from nltk import sent_tokenize
 import nltk
 
 class PreTrainedPipeline():
-    model_type="t5"
+
     def __init__(self, path=""):
         # IMPLEMENT_THIS
         # Preload all the elements you are going to need at inference.
@@ -14,6 +14,10 @@ class PreTrainedPipeline():
         nltk.download('punkt')
         self.model = AutoModelForSeq2SeqLM.from_pretrained(path)        
         self.tokenizer = AutoTokenizer.from_pretrained(path)
+        
+        self.model_type="t5"
+#        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.device =  "cpu"
 
 
     def __call__(self, inputs: str):