Update app.py

app.py

@@ -13,7 +13,7 @@ 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 out very short sentences
+    sentences = [sentence for sentence in sentences if len(sentence) > 10]  # Filter out very short sentences
     return sentences
 
 # Function to compute sentence similarity based on cosine similarity
@@ -43,7 +43,7 @@ def sentence_similarity(sent1, sent2, stopwords):
 
 # Function to create a similarity matrix of sentences
 def build_similarity_matrix(sentences, stopwords):
-    similarity_matrix = np.zeros((len(sentences), len(sentences))
+    similarity_matrix = np.zeros((len(sentences), len(sentences)))
 
     for i in range(len(sentences)):
         for j in range(len(sentences)):
@@ -66,7 +66,7 @@ def generate_summary(article, top_n=5):
     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)
+    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]])