app.py

import streamlit as st
from keybert import KeyBERT

# Create a KeyBERT instance
kw_model = KeyBERT()

# Define the Streamlit app
def main():
 st.title("Keyword Extraction")
 st.write("Enter your document below:")

 # Get user input
 doc = st.text_area("Document")

 # Get user choice for stopwords removal (default checkbox)
 remove_stopwords = st.checkbox("Remove Stopwords", value=True)

 # Get user choice for MMR (default checkbox)
 apply_mmr = st.checkbox("Apply Maximal Marginal Relevance (MMR)", value=True)

 # Get user choice for number of results (slider)
 num_results = st.slider("Number of Results", min_value=1, max_value=30, value=5, step=1)

 # Get user choice for minimum n-gram value (default textbox)
 min_ngram = st.number_input("Minimum N-gram", value=1, min_value=1, max_value=10, step=1)

 # Get user choice for maximum n-gram value (default textbox)
 max_ngram = st.number_input("Maximum N-gram", value=3, min_value=1, max_value=10, step=1)

 # Get user choice for keyword diversity (MMR only)
 diversity = st.slider("Keyword Diversity (MMR)", min_value=0.0, max_value=1.0, value=0.2, step=0.1, format="%.1f")

 # Extract keywords
 if st.button("Extract Keywords"):
 if apply_mmr:
 keywords = kw_model.extract_keywords(doc,
 keyphrase_ngram_range=(min_ngram, max_ngram),
 stop_words='english' if remove_stopwords else None,
 use_mmr=True,
 diversity=diversity)
 else:
 keywords = kw_model.extract_keywords(doc,
 keyphrase_ngram_range=(min_ngram, max_ngram),
 stop_words='english' if remove_stopwords else None)

 selected_keywords = keywords[:num_results]

 st.write(f"Top {num_results} Keywords:")
 for keyword, score in selected_keywords:
 st.write(f"- {keyword} (Score: {score})")

# Run the app
if __name__ == "__main__":
 main()