Update app.py

app.py

@@ -36,18 +36,20 @@ def sentiment_analysis(sentence, model_name):
         "CCCC/RoBERTa_English_FinancialNews_tuned":"roberta",
     }
 
-    tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-zh-en")
-    model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-zh-en")
-    
-    translated_tokens = model.generate(
-         **tokenizer(sentences, return_tensors="pt", padding=True)
-    )
-    sentences_list = []
-    for t in translated_tokens:
-        sentences_list.append(tokenizer.decode(t, skip_special_tokens=True))
+    if 'Chinese' in modelname:
+        tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-zh-en")
+        model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-zh-en")
+        
+        translated_tokens = model.generate(
+             **tokenizer(sentences, return_tensors="pt", padding=True)
+        )
+        sentences = []
+        for t in translated_tokens:
+            sentences.append(tokenizer.decode(t, skip_special_tokens=True))
+
 
     testdata = []
-    for i,sentence in enumerate(sentences_list):
+    for i,sentence in enumerate(sentences):
         testdata.append(InputExample(guid=i,text_a=sentence,label=0))
 
     plm, tokenizer, model_config, WrapperClass = load_plm(type_dic[model_name], model_name)