Create app.py

app.py

@@ -0,0 +1,46 @@
+import streamlit as st
+
+st.title("Correct Grammar with Transformers 🦄")
+st.write("")
+st.write("Input your text here!")
+
+default_value = "Urveesh and Raj is playing cricket"
+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)
+
+# 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):
+    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)
+
+# Decode generated sequences
+generated_sequences = [tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True, skip_special_tokens=True) for generated_sequence in results]
+
+# Add "Check Now" button
+if st.button("Check Now"):
+    st.write("### Results:")
+
+    # Check correctness and display in green or red
+    for generated_sequence in generated_sequences:
+        is_correct = generated_sequence == sent
+        color = "green" if is_correct else "red"
+        st.write(f"**Generated Sentence:**", generated_sequence, f" (Matches original: {is_correct})", unsafe_allow_html=True)
+
+        # If incorrect, display correct grammar sentence in a box
+        if not is_correct:
+            st.warning(f"**Correct Grammar:** {sent}")
+
+# Display original input
+st.write("### Original Input:")
+st.write(sent)