Update app.py

app.py

@@ -136,7 +136,7 @@ def get_word_emotion_pair(cls_model, origin_essay_sentence, idx2emo):
 
 
 def get_essay_base_analysis(file_made_dt_str):
-    essay1 = pd.read_csv(f"./result/{file_name_dt}/essay_result.csv")
+    essay1 = pd.read_csv(f"./result/{file_made_dt_str}/essay_result.csv")
     essay1['noun_list_len'] = essay1['noun_list'].apply(lambda x : len(x))
     essay1['noun_list_uniqlen'] = essay1['noun_list'].apply(lambda x : len(set(x)))
     essay1['adj_list_len'] = essay1['adj_list'].apply(lambda x : len(x))
@@ -235,8 +235,8 @@ 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('bert-base-multilingual-cased', num_labels = 6)
+    cls_model = AutoModelForSequenceClassification.from_pretrain ed('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)
     all_result, adj_\result, noun_result, essay_summary, file_made_dt_str = get_essay_base_analysis(file_name_dt)