Update app.py

app.py

@@ -1,12 +1,28 @@
 import streamlit as st
-from transformers import pipeline
+from transformers import T5ForConditionalGeneration, T5Tokenizer
 
-pipe = pipeline("sentiment-analysis")
-text = st.text_area("enter some text")
+sentence = st.text_area("enter some text")
 
-if text:
-    out = pipe(text)
-    st.json(out)
+model = T5ForConditionalGeneration.from_pretrained("Unbabel/gec-t5_small")
+tokenizer = T5Tokenizer.from_pretrained('t5-small')
+
+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
+)
+print(corrected_sentence) # -> I like swimming.
+
+
+#if text:
+    #st.json(corrected_sentence)