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app.py

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+import streamlit as st
+from transformers import pipeline
+from happytransformer import HappyTextToText, TTSettings
+
+# Initialize the spelling correction pipeline
+fix_spelling = pipeline("text2text-generation", model="oliverguhr/spelling-correction-english-base")
+
+# Initialize the grammar correction model
+happy_tt = HappyTextToText("T5", "vennify/t5-base-grammar-correction")
+args = TTSettings(num_beams=5, min_length=1)
+
+# Streamlit app
+def main():
+    st.title("Spelling and Grammar Checker")
+    
+    # Input text area
+    text_input = st.text_area("Enter your text here:")
+    
+    # Check button
+    if st.button("Check"):
+        # Spelling correction
+        corrected_spelling = fix_spelling(text_input)[0]['generated_text']
+        
+        # Grammar correction
+        result = happy_tt.generate_text(f"grammar: {text_input}", args=args)
+        corrected_grammar = result.text
+        
+        # Display corrected text
+        st.subheader("Corrected Text:")
+        st.write(corrected_grammar)  # Display grammar-corrected text
+        
+        # Display spelling correction if there's a difference
+        if corrected_spelling != corrected_grammar:
+            st.write(f"Spelling was also corrected to: {corrected_spelling}")
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+transformers
+happytransformer