Update model.py

model.py

@@ -1,3 +1,4 @@
+
 import os
 os.environ["TRANSFORMERS_CACHE"] = "/tmp/hf-cache"
 os.environ["HF_HOME"] = "/tmp/hf-home"
@@ -42,10 +43,9 @@ def smart_summarize(text, n_clusters=1):
         if not len(idx):
             continue
         # Average vector from cluster, converted to ndarray
-        avg_vector = np.asarray(tfidf_matrix[idx].mean(axis=0))
-        # Similarity computation (ensure both inputs are arrays)
+        avg_vector = tfidf_matrix[idx].mean(axis=0).A  # Convert matrix to ndarray
+        # Compute cosine similarity between avg_vector and tfidf vectors in cluster
         sim = cosine_similarity(avg_vector, tfidf_matrix[idx].toarray())
-        # Select sentence with highest similarity to avg
         most_representative = sentences[idx[np.argmax(sim)]]
         summary_sentences.append(most_representative)