Update app.py

app.py

@@ -12,9 +12,14 @@ import engine
 from model import BERTBaseUncased
 
 MODEL = None
-DEVICE = config.device
+
 T = tokenizer.TweetTokenizer(preserve_handles=True, preserve_hashes=True, preserve_case=False, preserve_url=False)
 
+device = config.device
+
+model = BERTBaseUncased()
+model.load_state_dict(torch.load(model_path, map_location=torch.device(device)))
+model.to(device)
 
 def preprocess(text):
     tokens = T.tokenize(text)
@@ -39,7 +44,7 @@ def preprocess(text):
 
 def sentence_prediction(sentence):
     sentence = preprocess(sentence)
-    model_path = config.MODEL_PATH
+   
 
     test_dataset = dataset.BERTDataset(
         review=[sentence],
@@ -52,23 +57,16 @@ def sentence_prediction(sentence):
         num_workers=3
     )
 
-    device = config.device
-
-    model = BERTBaseUncased()
-    model.load_state_dict(torch.load(
-        model_path, map_location=torch.device(device)))
-    model.to(device)
-
     outputs, [] = engine.predict_fn(test_data_loader, model, device)
     print(outputs)
-    return outputs[0]
+    return  label_full_decoder(outputs[0])
     
 interface = gr.Interface(
     fn=sentence_prediction,
     inputs='text',
     outputs=['text'],
     title='Sentiment Analysis',
-    description='Get the positive/negative sentiment for the given input.'
+    description='Get the positive/neutral/negative sentiment for the given input.'
 )