Update app.py

app.py

@@ -79,6 +79,31 @@ class BookRecommender:
             logger.error(f"Error loading model: {str(e)}", exc_info=True)
             return False
 
+    def recommend_books(self, user_query, top_n=5, include_description=True):
+        if self.model is None or self.book_embeddings is None or self.df is None:
+            return []
+        try:
+            processed_query = self.preprocess_text(user_query)
+            user_embedding = self.model.encode([processed_query])
+            similarities = cosine_similarity(user_embedding, self.book_embeddings)[0]
+            similar_books_idx = np.argsort(similarities)[-top_n:][::-1]
+            recommendations = []
+            for i, idx in enumerate(similar_books_idx):
+                book_data = {
+                    'title': self.df.iloc[idx].get('Title', ''),
+                    'author': self.df.iloc[idx].get('Authors', ''),
+                    'category': self.df.iloc[idx].get('Category', ''),
+                    'year': self.df.iloc[idx].get('Publish Date (Year)', ''),
+                    'description': self.df.iloc[idx].get('Description', '')[:197] + "..." if include_description and 'Description' in self.df.columns else '',
+                    'relevance_score': float(similarities[idx]),
+                    'rank': i + 1
+                }
+                recommendations.append(book_data)
+            return recommendations
+        except Exception as e:
+            logger.error(f"Error generating recommendations: {str(e)}", exc_info=True)
+            return []
+
 def load_ood_thresholds(model_path):
     threshold_path = os.path.join(model_path, "ood_thresholds.json")
     if os.path.exists(threshold_path):