Update app.py

app.py

@@ -1,16 +1,14 @@
 import streamlit as st
-from transformers import T5ForConditionalGeneration, AutoTokenizer
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
 st.title("SpellCorrectorT5")
 st.markdown('SpellCorrectorT5 is a fine-tuned version of **pre-trained t5-small model** modelled on randomly selected 50000 sentences modified by [imputing random noises/errors](./random_noiser.py) and trained using transformers. It not only looks for _spelling errors but also looks for the semantics_ in the sentence and suggest other possible words for the incorrect word.')
-ttokenizer = AutoTokenizer.from_pretrained("./")
-tmodel = T5ForConditionalGeneration.from_pretrained('./')
-
+ttokenizer = AutoTokenizer.from_pretrained("vishnun/tinygram")
+tmodel = AutoModelForSeq2SeqLM.from_pretrained("vishnun/tinygram")
 form = st.form("T5-form")
 
 examples = ["I will return it to yu once it is donr",
-            "Iu is going to rain",
-            "Feel free to raach out to me",
+            "Iu is going to rain",,
             "Wheir do you live?",
             "It wis great mieting with you all"]
 
@@ -21,16 +19,16 @@ input_text = form.text_input(label='Enter your own sentence', value=input_text)
 submit = form.form_submit_button("Submit")
 
 if submit:
-  input_ids = ttokenizer.encode('seq: '+ input_text, return_tensors='pt')
+  input_ids = ttokenizer.encode(input_text, return_tensors='pt')
   
   # generate text until the output length (which includes the context length) reaches 50
   outputs = tmodel.generate(
     input_ids,
     do_sample=True, 
     max_length=50,
-    top_p=0.99, 
-    top_k=50,
-    num_return_sequences=3
+    top_p=0.999, 
+    top_k=45,
+    num_return_sequences=2
   )
   
   st.subheader("Most probable: ")