| 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 |