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def similar_products(v, n = 6):
# extract most similar products for the input vector
ms = model.similar_by_vector(v, topn= n+1)[1:]
# extract name and similarity score of the similar products
new_ms = []
for j in ms:
pair = (products_dict[j[0]][0], j[1])
new_ms.append(pair)
return new_ms
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