Update app.py

app.py

@@ -224,7 +224,6 @@ nlp=lambda x:[(x[t["start"]:t["end"]],t["entity_group"]) for t in pipeline(x)]
 
 from transformers import AutoModelForSequenceClassification
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
-tokenizer = AutoTokenizer.from_pretrained('bert-base-multilingual-cased')
 
 def all_process(origin_essay):
     essay_sent =split_essay_to_sentence(origin_essay)
@@ -235,7 +234,7 @@ def all_process(origin_essay):
         row_dict[row] = answer
     emo2idx, idx2emo = get_sent_labeldata()
     tokenizer = AutoTokenizer.from_pretrained('bert-base-multilingual-cased')
-    cls_model = AutoModelForSequenceClassification.from_pretrain ed('seriouspark/bert-base-multilingual-cased-finetuning-sentimental-6label')
+    cls_model = AutoModelForSequenceClassification.from_pretrained('seriouspark/bert-base-multilingual-cased-finetuning-sentimental-6label')
     #cls_model = AutoModelForSequenceClassification.from_pretrained('bert-base-multilingual-cased', num_labels = 6)
     
     final_result, file_name_dt = get_word_emotion_pair(cls_model, essay_sent)