Update app.py

app.py

@@ -39,38 +39,6 @@ def get_sent_labeldata():
     idx2emo = {v : k[1] for k, v in emo2idx.items()}
     return emo2idx, idx2emo
 
-# def load_model():
-    
-#     class BertClassifier(nn.Module):
-
-#         def __init__(self, dropout = 0.3):
-#             super(BertClassifier, self).__init__()
-
-#             self.bert= BertModel.from_pretrained('bert-base-multilingual-cased')
-#             self.dropout = nn.Dropout(dropout)
-#             self.linear = nn.Linear(768, 6)
-#             self.relu = nn.ReLU()
-
-#         def forward(self, input_id, mask):
-#             _, pooled_output = self.bert(input_ids = input_id, attention_mask = mask, return_dict = False)
-#             dropout_output = self.dropout(pooled_output)
-#             linear_output = self.linear(dropout_output)
-#             final_layer= self.relu(linear_output)
-
-#             return final_layer
-
-
-#     tokenizer = AutoTokenizer.from_pretrained('bert-base-multilingual-cased')
-#     device = 'cuda' if torch.cuda.is_available() else 'cpu'
-#     cls_model = BertClassifier()
-#     criterion = nn.CrossEntropyLoss()
-#     model_name = 'bert-base-multilingual-cased'
-#     PATH = './model' + '/' + model_name + '_' + '2023102410'
-#     print(PATH)
-#     cls_model = torch.load(PATH)
-#     #cls_model.load_state_dict(torch.load(PATH))
-#     return tokenizer, cls_model
-
 
 class myDataset_for_infer(torch.utils.data.Dataset):
     def __init__(self, X):
@@ -254,17 +222,13 @@ def all_process(origin_essay):
 import gradio as gr
 outputs = [gr.Dataframe(row_count = (6, "dynamic"),
                         col_count=(2, "dynamic"),
-                        label="Essay Summary based on Words")
+                        label="Essay Summary based on Words"),
+                        title = 'MooGeulMooGeul'
+                   
                         #headers=['type','word','슬픔', '분노', '기쁨', '불안', '상처', '당황', 'total'])
     
                         ]
-    
-    
-                        #row_count = (10, "dynamic"),
-                        #col_count=(9, "dynamic"),
-                        #label="Results",
-                        #headers=['type','word','슬픔', '분노', '기쁨', '불안', '상처', '당황', 'total'])
-          #]
+
         
 iface = gr.Interface(
    fn=all_process,