| import spacy | |
| import streamlit as st | |
| def article_summarizer(article_text, num_sentences=3): | |
| nlp = spacy.load("en_core_web_sm") | |
| doc = nlp(article_text) | |
| sentence_importance = {} | |
| for sentence in doc.sents: | |
| sentence_tokens = [token for token in sentence if not token.is_stop] | |
| sentence_rank = sum(token.rank for token in sentence_tokens) | |
| sentence_importance[sentence] = sentence_rank | |
| sorted_sentences = sorted(sentence_importance, key=lambda x: sentence_importance[x], reverse=True) | |
| summary = " ".join(str(sentence) for sentence in sorted_sentences[:num_sentences]) | |
| return summary | |
| st.title("Article Summarizer") | |
| article = st.text_area("Enter your article here:") | |
| num_sentences = st.slider("Select the number of sentences for the summary:", 1, 10, 3) | |
| if st.button("Summarize"): | |
| if article: | |
| summary = article_summarizer(article, num_sentences) | |
| st.subheader("Summary:") | |
| st.write(summary) | |
| else: | |
| st.warning("Please enter an article to summarize.") |