Create app.py

app.py

@@ -0,0 +1,31 @@
+import streamlit as st
+from transformers import T5ForConditionalGeneration, AutoTokenizer
+
+st.title("NeuralSpellChecker-Lite")
+st.markdown('NeuralSpellChecker-Lite is a fine-tuned version of **pre-trained t5-small model** modelled on randomly selected 30000 sentences modified by imputing random noises 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('./')
+
+form = st.form("NSC form")
+input_text = form.text_input(label='Enter a random sentence')
+submit = form.form_submit_button("Submit")
+
+if submit:
+  input_ids = ttokenizer.encode('seq: '+ 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=0.98, 
+    num_return_sequences=3
+  )
+  
+  st.subheader("Suggested sentences: ")
+  
+  i = 0
+  for x in outputs:
+      out_text = ttokenizer.decode(x, skip_special_tokens=True)
+      i = i + 1
+      st.success(str(i) + '. ' + out_text)