Update app.py

app.py

@@ -1,4 +1,10 @@
-import gradio as gr
+import streamlit as st
+
+default_value = "Mike and Anna is skiing"
+sent = st.text_area("Text", default_value, height = 275)
+num_return_sequences = st.sidebar.number_input('Number of Return Sequences', min_value=1, max_value=3, value=1, step=1)
+
+@st.cache
 ### Run Model
 from transformers import T5ForConditionalGeneration, T5Tokenizer
 import torch
@@ -6,11 +12,20 @@ torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
 tokenizer = T5Tokenizer.from_pretrained('deep-learning-analytics/GrammarCorrector')
 model = T5ForConditionalGeneration.from_pretrained('deep-learning-analytics/GrammarCorrector').to(torch_device)
 
-def correct_grammar(input_text,num_return_sequences=1):
+def correct_grammar(input_text,num_return_sequences=num_return_sequences):
   batch = tokenizer([input_text],truncation=True,padding='max_length',max_length=64, return_tensors="pt").to(torch_device)
   results = model.generate(**batch,max_length=64,num_beams=2, num_return_sequences=num_return_sequences, temperature=1.5)
-  answer = tokenizer.batch_decode(results[0], skip_special_tokens=True)
-  return answer
+  #answer = tokenizer.batch_decode(results[0], skip_special_tokens=True)
+  return results
   
-iface = gr.Interface(fn=correct_grammar, inputs=[gr.inputs.Textbox(lines=5)], outputs=["text"])
-iface.launch(inline=False, share=True)
+##Prompts
+st.title("Correct Grammar with Transformers 🦄")
+results = correct_grammar(sent, num_return_sequences)
+
+generated_sequences = []
+for generated_sequence_idx, generated_sequence in enumerate(results):
+    # Decode text
+    text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
+    generated_sequences.append(generated_sequence)
+
+st.write(generated_sequences)