Update app.py

app.py

@@ -1,10 +1,15 @@
-from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+from transformers import AutoModelForQuestionAnswering, AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
 import gradio as grad
 import ast
 
 mdl_name = "deepset/roberta-base-squad2"
 my_pipeline = pipeline('question-answering', model=mdl_name, tokenizer=mdl_name)
 
+model_translate_name = 'danhsf/m2m100_418M-finetuned-kde4-en-to-pt_BR'
+model_translate = AutoModelForSeq2SeqLM.from_pretrained(model_translate_name)
+model_translate_token = AutoTokenizer.from_pretrained(model_translate_name)
+translate_pipeline = ('translation', model=model_translate_name)
+
 def answer_question(question,context):
     text= "{"+"'question': '"+question+"','context': '"+context+"'}"
     di=ast.literal_eval(text)
@@ -12,4 +17,13 @@ def answer_question(question,context):
     print('response', response)
     return response
 
-grad.Interface(answer_question, inputs=["text","text"], outputs="text").launch()
+
+def translate(text):
+    inputs = model_translate_token(text, return_tensor='pt')
+    translate_output = model_translate.generate(**inputs)
+    response = model_translate_token(translate_output[0], skip_special_tokens=True)
+    #response = translate_pipeline(text)
+    return response
+
+#grad.Interface(answer_question, inputs=["text","text"], outputs="text").launch()
+grad.Interface(translate, inputs=['text',], outputs='text').launch()