Update app.py

app.py

@@ -1,10 +1,29 @@
 import streamlit as st
-#from transformers import T5ForConditionalGeneration, T5Tokenizer
+from transformers import T5ForConditionalGeneration, T5Tokenizer
 
 sentence = st.text_area("enter some text")
 
 if sentence:
-    st.write(sentence)
-    
+    from transformers import T5ForConditionalGeneration, T5Tokenizer
+
+    model = T5ForConditionalGeneration.from_pretrained("Unbabel/gec-t5_small")
+    tokenizer = T5Tokenizer.from_pretrained('t5-small')
+
+    sentence = "I like to swimming"
+    tokenized_sentence = tokenizer('gec: ' + sentence, max_length=128, truncation=True, padding='max_length', return_tensors='pt')
+    corrected_sentence = tokenizer.decode(
+        model.generate(
+            input_ids = tokenized_sentence.input_ids,
+            attention_mask = tokenized_sentence.attention_mask, 
+            max_length=128,
+            num_beams=5,
+            early_stopping=True,
+        )[0],
+        skip_special_tokens=True, 
+        clean_up_tokenization_spaces=True
+    )
+    st.write(corrected_sentence) 
+
+