Update app.py

app.py

@@ -17,16 +17,29 @@ summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 # Extractive summarization using TextRank
 def extractive_summarization(text, num_sentences=3):
     sentences = sent_tokenize(text)
+
+    # Handle cases where input text is too short
     if len(sentences) <= num_sentences:
-        return text  # If text is short, return as is
-    
-    vectorizer = TfidfVectorizer(stop_words="english")
-    sentence_vectors = vectorizer.fit_transform(sentences)
-    similarity_matrix = cosine_similarity(sentence_vectors)
-    graph = nx.from_numpy_array(similarity_matrix)
-    scores = nx.pagerank(graph)
-    ranked_sentences = sorted(((scores[i], s) for i, s in enumerate(sentences)), reverse=True)
-    return " ".join([s for _, s in ranked_sentences[:num_sentences]])
+        return "Text is too short for extractive summarization."
+
+    try:
+        vectorizer = TfidfVectorizer(stop_words="english")
+        sentence_vectors = vectorizer.fit_transform(sentences)
+
+        # Handle cases where vectorization fails due to low variation in text
+        if sentence_vectors.shape[0] < num_sentences:
+            return "Insufficient unique content for extractive summarization."
+
+        similarity_matrix = cosine_similarity(sentence_vectors)
+        graph = nx.from_numpy_array(similarity_matrix)
+        scores = nx.pagerank(graph)
+
+        ranked_sentences = sorted(((scores[i], s) for i, s in enumerate(sentences)), reverse=True)
+        return " ".join([s for _, s in ranked_sentences[:num_sentences]])
+
+    except Exception as e:
+        return f"Error in extractive summarization: {str(e)}"
+
 
 # Abstractive summarization using BART
 def abstractive_summarization(text, length):