update app.py

app.py

@@ -75,16 +75,20 @@ LABEL = [
 ]
 
 # Veracity
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
+# device = "cuda:0" if torch.cuda.is_available() else "cpu"
 veracity_tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
 bert_model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=4, problem_type="single_label_classification")
 veracity_checkpoint_path = os.getcwd() + "/averitec/pretrained_models/bert_veracity.ckpt"
-veracity_model = SequenceClassificationModule.load_from_checkpoint(veracity_checkpoint_path,tokenizer=veracity_tokenizer, model=bert_model).to(device)
+veracity_model = SequenceClassificationModule.load_from_checkpoint(veracity_checkpoint_path,tokenizer=veracity_tokenizer, model=bert_model)
+# veracity_model = SequenceClassificationModule.load_from_checkpoint(veracity_checkpoint_path,tokenizer=veracity_tokenizer, model=bert_model).to(device)
+
 # Justification
 justification_tokenizer = BartTokenizer.from_pretrained('facebook/bart-large', add_prefix_space=True)
 bart_model = BartForConditionalGeneration.from_pretrained("facebook/bart-large")
 best_checkpoint = os.getcwd()+ '/averitec/pretrained_models/bart_justifications_verdict-epoch=13-val_loss=2.03-val_meteor=0.28.ckpt'
-justification_model = JustificationGenerationModule.load_from_checkpoint(best_checkpoint, tokenizer=justification_tokenizer, model=bart_model).to(device)
+justification_model = JustificationGenerationModule.load_from_checkpoint(best_checkpoint, tokenizer=justification_tokenizer, model=bart_model)
+# justification_model = JustificationGenerationModule.load_from_checkpoint(best_checkpoint, tokenizer=justification_tokenizer, model=bart_model).to(device)
+
 
 print("veracity_model_device_0:{}".format(veracity_model.device))
 print("justification_model_device_0:{}".format(justification_model.device))
@@ -281,8 +285,8 @@ def veracity_prediction(claim, evidence):
         return pred_label
 
     tokenized_strings, attention_mask = dataLoader.tokenize_strings(evidence_strings)
-    example_support = torch.argmax(
-        veracity_model(tokenized_strings.to(device), attention_mask=attention_mask.to(device)).logits, axis=1)
+    example_support = torch.argmax(veracity_model(tokenized_strings, attention_mask=attention_mask).logits, axis=1)
+    # example_support = torch.argmax(veracity_model(tokenized_strings.to(device), attention_mask=attention_mask.to(device)).logits, axis=1)
     print("veracity_model_device_1:{}".format(veracity_model.device))
 
     has_unanswerable = False
@@ -345,7 +349,8 @@ def justification_generation(claim, evidence, verdict_label):
     #
     claim_str = extract_claim_str(claim, evidence, verdict_label)
     claim_str.strip()
-    pred_justification = justification_model.generate(claim_str, device=device)
+    pred_justification = justification_model.generate(claim_str)
+    # pred_justification = justification_model.generate(claim_str, device=device)
     print("justification_model_device_1:{}".format(justification_model.device))
 
     return pred_justification.strip()