Update product_recommender.py

product_recommender.py

@@ -1,19 +1,72 @@
-import pandas as pd
-from sentence_transformers import SentenceTransformer
-import faiss
-
-class ProductRecommender:
-    def __init__(self, product_data_path):
-        self.data = pd.read_csv(product_data_path)
-        self.model = SentenceTransformer('all-MiniLM-L6-v2')
-        self.embeddings = self.model.encode(self.data['product_description'].tolist())
-        self.index = faiss.IndexFlatL2(self.embeddings.shape[1])
-        self.index.add(self.embeddings)
-
-    def get_recommendations(self, query, top_n=5):
-        query_embedding = self.model.encode([query])
-        distances, indices = self.index.search(query_embedding, top_n)
-        recommendations = []
-        for i in indices[0]:
-            recommendations.append(self.data.iloc[i]['product_title'] + ": " + self.data.iloc[i]['product_description'])
-        return recommendations
+import pandas as pd
+from sentence_transformers import SentenceTransformer
+import faiss
+
+class ProductRecommender:
+    def __init__(self, product_data_path):
+        try:
+            # Attempt to load the product data CSV
+            self.data = pd.read_csv(product_data_path)
+            print("Product data loaded successfully.")
+        except Exception as e:
+            print(f"Error loading product data: {e}")
+            self.data = pd.DataFrame()  # Create an empty DataFrame if loading fails
+            return
+
+        try:
+            # Initialize the sentence transformer model
+            self.model = SentenceTransformer('all-MiniLM-L6-v2')
+            print("Model loaded successfully.")
+        except Exception as e:
+            print(f"Error loading SentenceTransformer model: {e}")
+            self.model = None  # Set model to None if loading fails
+            return
+
+        try:
+            # Check if 'product_description' column exists
+            if 'product_description' not in self.data.columns:
+                print("Error: 'product_description' column is missing in the data.")
+                return
+
+            # Generate embeddings for the product descriptions
+            self.embeddings = self.model.encode(self.data['product_description'].tolist())
+            print(f"Embeddings generated successfully. Shape: {self.embeddings.shape}")
+        except Exception as e:
+            print(f"Error generating embeddings: {e}")
+            self.embeddings = None  # Set embeddings to None if generation fails
+            return
+
+        try:
+            # Initialize FAISS index and add the embeddings
+            self.index = faiss.IndexFlatL2(self.embeddings.shape[1])
+            self.index.add(self.embeddings)
+            print("FAISS index created and embeddings added.")
+        except Exception as e:
+            print(f"Error creating FAISS index or adding embeddings: {e}")
+            self.index = None  # Set index to None if creation fails
+            return
+
+    def get_recommendations(self, query, top_n=5):
+        if self.model is None or self.index is None:
+            print("Error: Model or FAISS index not initialized. Cannot make recommendations.")
+            return []
+
+        try:
+            # Generate the embedding for the query
+            query_embedding = self.model.encode([query])
+            print(f"Query embedding generated. Shape: {query_embedding.shape}")
+        except Exception as e:
+            print(f"Error generating query embedding: {e}")
+            return []
+
+        try:
+            # Search for top_n recommendations
+            distances, indices = self.index.search(query_embedding, top_n)
+            recommendations = []
+            for i in indices[0]:
+                recommendations.append(self.data.iloc[i]['product_title'] + ": " + self.data.iloc[i]['product_description'])
+            print(f"Recommendations generated successfully: {recommendations}")
+            return recommendations
+        except Exception as e:
+            print(f"Error during recommendation search: {e}")
+            return []