Update app.py

app.py

@@ -122,10 +122,10 @@ def text2text(context,answer):
 # grad.Interface(text2text, inputs=[context,ans], outputs=out).launch()
 
 #T5 summaryzer
-from transformers import AutoTokenizer, AutoModelWithLMHead
-import gradio as grad
-text2text_tkn = AutoTokenizer.from_pretrained("deep-learning-analytics/wikihow-t5-small")
-mdl = AutoModelWithLMHead.from_pretrained("deep-learning-analytics/wikihow-t5-small")
+# from transformers import AutoTokenizer, AutoModelWithLMHead
+# import gradio as grad
+# text2text_tkn = AutoTokenizer.from_pretrained("deep-learning-analytics/wikihow-t5-small")
+# mdl = AutoModelWithLMHead.from_pretrained("deep-learning-analytics/wikihow-t5-small")
 def text2text_summary(para):
     initial_txt = para.strip().replace("\n","")
     tkn_text = text2text_tkn.encode(initial_txt, return_tensors="pt")
@@ -138,6 +138,21 @@ def text2text_summary(para):
         )
     response = text2text_tkn.decode(tkn_ids[0], skip_special_tokens=True)
     return response
-para=grad.Textbox(lines=10, label="Paragraph", placeholder="Copy paragraph")
-out=grad.Textbox(lines=1, label="Summary")
-grad.Interface(text2text_summary, inputs=para, outputs=out).launch()
+# para=grad.Textbox(lines=10, label="Paragraph", placeholder="Copy paragraph")
+# out=grad.Textbox(lines=1, label="Summary")
+# grad.Interface(text2text_summary, inputs=para, outputs=out).launch()
+
+# T5 Translate
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+import gradio as grad
+text2text_tkn= T5Tokenizer.from_pretrained("t5-small")
+mdl = T5ForConditionalGeneration.from_pretrained("t5-small")
+def text2text_translation(text):
+     inp = "translate English to Portuguese:: "+text
+     enc = text2text_tkn(inp, return_tensors="pt")
+     tokens = mdl.generate(**enc)
+     response=text2text_tkn.batch_decode(tokens)
+     return response
+para=grad.Textbox(lines=1, label="English Text", placeholder="Text in English")
+out=grad.Textbox(lines=1, label="Portuguese Translation")
+grad.Interface(text2text_translation, inputs=para, outputs=out).launch()