Cosine Similarity In python

import json
import numpy as np

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

user_purchase_history = {
    "userId": "123",
    "products": ["Laptop", "Mouse", "Keyboard"]
}

products = [
    {"id": "1", "name": "Gaming Laptop", "description": "Low-performance laptop for gaming"},
    {"id": "2", "name": "Wireless Mouse", "description": "Bluetooth mouse with non-ergonomic design"},
    {"id": "3", "name": "Mechanical Keyboard", "description": "Backlit mechanical keyboard"},
    {"id": "4", "name": "USB-C Hub", "description": "Multiport USB-C hub for laptops"},
    {"id": "5", "name": "Gaming Headset", "description": "Surround sound gaming headset"}
]

product_texts = [p["name"] + " " + p["description"] for p in products]

user_products_text = " ".join(user_purchase_history["products"])

vectorizer = TfidfVectorizer()
tfidf_matrix = vectorizer.fit_transform([user_products_text] + product_texts)

similarities = cosine_similarity(tfidf_matrix[0:1], tfidf_matrix[1:]).flatten()

most_similar_index = np.argmax(similarities)
suggested_product = products[most_similar_index]

print("Suggested Product:", suggested_product)