update app.py

app.py

@@ -85,6 +85,9 @@ justification_tokenizer = BartTokenizer.from_pretrained('facebook/bart-large', a
 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)
+
+print("veracity_model_device_0:{}".format(veracity_model.device))
+print("justification_model_device_0:{}".format(justification_model.device))
 # ---------------------------------------------------------------------------
 
 # ----------------------------------------------------------------------------
@@ -280,6 +283,7 @@ def veracity_prediction(claim, evidence):
     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)
+    print("veracity_model_device_1:{}".format(veracity_model.device))
 
     has_unanswerable = False
     has_true = False
@@ -342,6 +346,7 @@ 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)
+    print("justification_model_device_1:{}".format(justification_model.device))
 
     return pred_justification.strip()
 
@@ -363,8 +368,8 @@ def log_on_azure(file, logs, azure_share_client):
     file_client.upload_file(logs)
 
 
+# @spaces.GPU
 @app.post("/predict/")
-@spaces.GPU
 def fact_checking(item: Item):
     claim = item['claim']
     source = item['source']