test.py

"""
    TEST CODE NOT NEEDED
"""

# from transformer import Model
# import numpy as np

# def convert_to_str(data: dict)->str: # doesnt modify data
#     out_str = ""
#     for key in data.keys():
#         out_str += key + ":" + str(data[key]) + ","
#     out_str += " | "
#     return out_str

# def vectorize_database(model:Model, dataset: list[dict])->np.ndarray: # creates dataset
#     embd_dataset = [0]*len(dataset)
#     for index,data in enumerate(dataset):
#         prompt = convert_to_str(data)
#         embd = model.getEmbeddings(prompt)
#         embd_dataset[index] = embd
#     return np.array(embd_dataset)

# def vectorize_database_search(model: Model, query, vector_db: np.ndarray):
#     if type(query) == type([]):
#         q_len = len(query)
#         if type(query[0]) == type({}):
#             query = list(map(convert_to_str, query))
#         elif type(query[0]) == type(""):
#             pass
#         else:
#             raise AttributeError("Query type not supported")
        
#     elif type(query) == type({}):
#         query = [convert_to_str(query)]

#     elif type(query) == type(""):
#         query = [query]

#     else:
#         raise AttributeError("Query type not supported")

#     embd = model.getEmbeddings(query)
#     same = model.compare(embd,vector_db).numpy()
#     index = np.argmax(same,axis=1)
#     print("Similarity for all data:", same)
#     print("Similarity for all data:", index)
#     return index

# def main():
#     model = Model()
#     model.listModels()
#     model.loadModel("paraphrase-MiniLM-L3-v2")
#     database = np.array([
#         {
#             "product name": "Smartphone X",
#             "product description": "A high-end smartphone with a powerful processor and stunning display.",
#             "product category": "Electronics",
#             "product cost": 799.99,
#             "product id": "P1234"
#         },
#         {
#             "product name": "Wireless Earbuds",
#             "product description": "Bluetooth earbuds with noise cancellation and long battery life.",
#             "product category": "Electronics",
#             "product cost": 149.99,
#             "product id": "P5678"
#         },
#         {
#             "product name": "Laptop Pro",
#             "product description": "A powerful laptop for work and gaming, featuring a high-resolution screen and fast SSD storage.",
#             "product category": "Electronics",
#             "product cost": 1299.99,
#             "product id": "P9012"
#         },
#         {
#             "product name": "Smartwatch Classic",
#             "product description": "A classic smartwatch with fitness tracking and notification features.",
#             "product category": "Wearables",
#             "product cost": 249.99,
#             "product id": "P3456"
#         },
#         {
#             "product name": "Gaming Console",
#             "product description": "A next-generation gaming console with high-definition graphics and exclusive games.",
#             "product category": "Electronics",
#             "product cost": 499.99,
#             "product id": "P7890"
#         }
#     ])

#     vector_db = vectorize_database(model, database)

#     test_data = "Noise Cancelling and Good Sound Quality"

#     index = vectorize_database_search(model,test_data,vector_db)
#     print("Query: ", test_data)
#     print("Likey Product: ", database[index])

# if '__main__' == __name__:
#     main()