Update game3.py

game3.py

@@ -4,6 +4,8 @@ import time
 import pandas as pd
 import gradio as gr
 import numpy as np
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import pipeline
 
 def read3(num_selected_former):
     fname = 'data3_convai2_inferred.txt'
@@ -41,10 +43,8 @@ def read3(num_selected_former):
 def func3(lang_selected, num_selected, human_predict, num1, num2, user_important):
     chatbot = []
     # num1: Human score; num2: AI score
-    if lang_selected in ['en']:
-        fname = 'data1_en.txt'
-    else:
-        fname = 'data1_nl_10.txt'
+    fname = 'data3_convai2_inferred.txt'
+
     with open(fname) as f:
         content = f.readlines()
         text = eval(content[int(num_selected*2)])
@@ -223,10 +223,7 @@ def func3(lang_selected, num_selected, human_predict, num1, num2, user_important
     return ai_predict, chatbot, num1, num2, tot_scores
 
 def interpre3(lang_selected, num_selected):
-    if lang_selected in ['en']:
-        fname = 'data1_en.txt'
-    else:
-        fname = 'data1_nl_10.txt'
+    fname = 'data3_convai2_inferred.txt'
     with open(fname) as f:
         content = f.readlines()
         text = eval(content[int(num_selected*2)])
@@ -291,9 +288,6 @@ def func3_written(text_written, human_predict, lang_written):
     ''' 
 
     # (START) off-the-shelf version
-    from transformers import AutoTokenizer, AutoModelForSequenceClassification
-    from transformers import pipeline
-
 
     # tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
     # model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")