Update model.py

model.py

@@ -1,8 +1,10 @@
 import os
 os.environ["TRANSFORMERS_CACHE"] = "/tmp/hf-cache"
 os.environ["HF_HOME"] = "/tmp/hf-home"
+
 import nltk
 nltk.download("punkt", download_dir="/tmp/nltk_data")
+
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.cluster import KMeans
 from sklearn.metrics.pairwise import cosine_similarity
@@ -10,26 +12,42 @@ from nltk.tokenize import sent_tokenize
 from transformers import pipeline
 import numpy as np
 
+# Load summarizer model
 summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
 
 def summarize_review(text):
+    """Standard transformer-based summarization"""
     return summarizer(text, max_length=60, min_length=10, do_sample=False)[0]["summary_text"]
 
 def smart_summarize(text, n_clusters=1):
+    """
+    Clustering + cosine similarity-based summarization
+    Selects most representative sentence(s) from each cluster
+    """
     sentences = sent_tokenize(text)
     if len(sentences) <= 1:
         return text
 
-    tfidf_matrix = TfidfVectorizer(stop_words="english").fit_transform(sentences)
+    tfidf = TfidfVectorizer(stop_words="english")
+    tfidf_matrix = tfidf.fit_transform(sentences)
+
     if len(sentences) <= n_clusters:
         return " ".join(sentences)
 
     kmeans = KMeans(n_clusters=n_clusters, random_state=42).fit(tfidf_matrix)
-    avg = []
+    summary_sentences = []
+
     for i in range(n_clusters):
         idx = np.where(kmeans.labels_ == i)[0]
-        if not len(idx): continue
-        avg_vector = tfidf_matrix[idx].mean(axis=0)
-        sim = cosine_similarity(avg_vector, tfidf_matrix[idx])
-        avg.append(sentences[idx[np.argmax(sim)]])
-    return " ".join(sorted(avg, key=sentences.index))
+        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)
+        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)
+
+    # Preserve original sentence order
+    return " ".join(sorted(summary_sentences, key=sentences.index))