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| import streamlit as st | |
| from nltk import ngrams | |
| import nltk | |
| # Download NLTK data | |
| nltk.download('punkt') | |
| def extract_ngrams(text, n): | |
| tokens = nltk.word_tokenize(text) | |
| n_grams = list(ngrams(tokens, n)) | |
| return n_grams | |
| def main(): | |
| # Set page title and configure page layout | |
| st.set_page_config(page_title="N-gram Extractor", layout="wide") | |
| # Page title and subtitle | |
| st.title("N-gram Generator") | |
| st.write("Enter a text passage and choose the value of n to extract n-grams.") | |
| # User input | |
| text_input = st.text_area("Enter a text passage:", "") | |
| # Choose n for n-grams | |
| n = st.slider("Select the value of n for n-grams:", min_value=1, max_value=5, value=2) | |
| # Extract button | |
| if st.button("Extract N-grams"): | |
| if not text_input: | |
| st.warning("Please enter a text passage.") | |
| else: | |
| # Extract and display n-grams | |
| n_grams_result = extract_ngrams(text_input, n) | |
| st.subheader(f"{n}-grams:") | |
| st.write(n_grams_result) | |
| if __name__ == "__main__": | |
| main() | |