Update app.py

app.py

@@ -6,17 +6,14 @@ import numpy as np
 import networkx as nx
 from googletrans import Translator
 
-# Download NLTK resources
 nltk.download('punkt')
 nltk.download('stopwords')
 
-# Function to read and preprocess the article
 def read_article(article):
     sentences = nltk.sent_tokenize(article)
     sentences = [sentence for sentence in sentences if len(sentence) > 10]  # filter very short sentences
     return sentences
 
-# Function to compute sentence similarity based on cosine similarity
 def sentence_similarity(sent1, sent2, stopwords):
     words1 = nltk.word_tokenize(sent1)
     words2 = nltk.word_tokenize(sent2)
@@ -41,42 +38,34 @@ def sentence_similarity(sent1, sent2, stopwords):
 
     return 1 - cosine_distance(vector1, vector2)
 
-# Function to create a similarity matrix of sentences
 def build_similarity_matrix(sentences, stopwords):
     similarity_matrix = np.zeros((len(sentences), len(sentences)))
 
     for i in range(len(sentences)):
         for j in range(len(sentences)):
-            if i == j:  # Skip comparing a sentence to itself
+            if i == j:  
                 continue
             similarity_matrix[i][j] = sentence_similarity(sentences[i], sentences[j], stopwords)
 
     return similarity_matrix
 
-# Function to generate the article summary
 def generate_summary(article, top_n=5):
     sentences = read_article(article)
     stop_words = set(stopwords.words('english'))
     sentence_similarity_matrix = build_similarity_matrix(sentences, stop_words)
 
-    # Create a graph from the similarity matrix
     sentence_similarity_graph = nx.from_numpy_array(sentence_similarity_matrix)
 
-    # Use the PageRank algorithm to rank the sentences
     scores = nx.pagerank(sentence_similarity_graph)
 
-    # Sort the sentences by score
     ranked_sentences = sorted(((scores[i], sentence) for i, sentence in enumerate(sentences)), reverse=True)
 
-    # Get the top N sentences as the summary
     summary = " ".join([sentence for _, sentence in ranked_sentences[:top_n]])
     return summary
 
-# Streamlit web app with improved styling and icons
 st.set_page_config(page_title="Article Summarizer", page_icon="✍️")
 st.title("Article Summarizer")
 
-# Custom CSS to style the app with icons
 st.markdown(
     """
     <style>