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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 |