Update app.py

app.py

@@ -9,27 +9,6 @@ import config
 from transformers import pipeline, AutoTokenizer, AutoModel
 import gradio as gr
 
-# DEVICE = config.device
-
-# model = AutoModel.from_pretrained("thak123/bert-emoji-latvian-twitter-classifier")
-# 7 EPOCH Version
-# BERT_PATH = "FFZG-cleopatra/bert-emoji-latvian-twitter"
-
-# tokenizer = transformers.BertTokenizer.from_pretrained(
-#     BERT_PATH,
-#     do_lower_case=True
-# )
-#AutoTokenizer.from_pretrained("FFZG-cleopatra/bert-emoji-latvian-twitter")
-
-# classifier = pipeline("sentiment-analysis",
-#                       model= model, 
-#                       tokenizer = tokenizer)
-
-# MODEL = BERTBaseUncased()
-# MODEL.load_state_dict(torch.load(config.MODEL_PATH, map_location=torch.device(DEVICE)))
-# MODEL.eval()
-
-
 
 # T = tokenizer.TweetTokenizer(
 #     preserve_handles=True, preserve_hashes=True, preserve_case=False, preserve_url=False)
@@ -58,27 +37,27 @@ def preprocess(text):
 def sentence_prediction(sentence):
     # sentence = preprocess(sentence)
     
-    # model_path = config.MODEL_PATH
+    model_path = config.MODEL_PATH
 
-    # test_dataset = dataset.BERTDataset(
-    #     review=[sentence],
-    #     target=[0]
-    # )
+    test_dataset = dataset.BERTDataset(
+        review=[sentence],
+        target=[0]
+    )
 
-    # test_data_loader = torch.utils.data.DataLoader(
-    #     test_dataset,
-    #     batch_size=config.VALID_BATCH_SIZE,
-    #     num_workers=3
-    # )
+    test_data_loader = torch.utils.data.DataLoader(
+        test_dataset,
+        batch_size=config.VALID_BATCH_SIZE,
+        num_workers=3
+    )
 
-    # device = config.device
+    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)
+    outputs, [] = engine.predict_fn(test_data_loader, model, device)
 
     outputs =  classifier(sentence)