Update app.py

app.py

@@ -1,34 +1,46 @@
 import streamlit as st
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+import torch
 
-
-st.title("Correct Grammar with Transformers 🦄")
+# Set up the Streamlit app
+st.title("Correct your Grammar with Transformers")
 st.write("")
 st.write("Input your text here!")
 
+# Create input text area
 default_value = "Mike and Anna is skiing"
-sent = st.text_area("Text", default_value, height = 50)
-num_return_sequences = st.sidebar.number_input('Number of Return Sequences', min_value=1, max_value=3, value=1, step=1)
+sent = st.text_area("Text", default_value, height=50)
 
-### Run Model
-from transformers import T5ForConditionalGeneration, T5Tokenizer
-import torch
-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=num_return_sequences):
-  batch = tokenizer([input_text],truncation=True,padding='max_length',max_length=len(input_text), return_tensors="pt").to(torch_device)
-  results = model.generate(**batch,max_length=len(input_text),num_beams=2, num_return_sequences=num_return_sequences, temperature=1.5)
-  #answer = tokenizer.batch_decode(results[0], skip_special_tokens=True)
-  return results
-  
-##Prompts
-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, skip_special_tokens=True)
-    generated_sequences.append(text)
-
-st.write(generated_sequences)
+# Create "Check Now" button
+if st.button("Check Now"):
+    # Run Model
+    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):
+        batch = tokenizer([input_text], truncation=True, padding='max_length', max_length=len(input_text), return_tensors="pt").to(torch_device)
+        results = model.generate(**batch, max_length=len(input_text), num_beams=2, num_return_sequences=num_return_sequences, temperature=1.5)
+        return results
+
+    # Prompts
+    results = correct_grammar(sent, num_return_sequences=1)
+
+    # Decode results
+    generated_sequences = []
+    for generated_sequence_idx, generated_sequence in enumerate(results):
+        text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True, skip_special_tokens=True)
+        generated_sequences.append(text)
+
+    # Check correctness
+    is_correct = sent == generated_sequences[0]
+
+    # Display correctness result
+    if is_correct:
+        st.write("Result: ", generated_sequences[0], " (Correct)", key="result_text", unsafe_allow_html=True)
+    else:
+        st.write("Result: ", generated_sequences[0], " (Wrong)", key="result_text", unsafe_allow_html=True)
+
+# Display correct grammar sentence in a box
+st.text("Correct Grammar Sentence:")
+st.code(generated_sequences[0])