files = [
    white_wine,
    red_wine,
    rose_wine,
    sparkling_wine
]

names = [
    "white_wine",
    "red_wine",
    "rose_wine",
    "sparkling_wine"
]

def process_csv(dataframes, categories):

    new_dataframes = []

    for dataframe, category in zip(dataframes, categories):

        category = category.split("_")[0].title()

        new_dataframe = dataframe.groupBy('Country', 'Region', 'Winery') \
                                 .avg("Rating", "Price") \
                                 .sort("Country")

        new_dataframe = new_dataframe.withColumn("index", lit((1 + monotonically_increasing_id()).cast("string")))

        new_dataframe = new_dataframe.withColumn("id", lit(uuid.uuid1().hex))

        new_dataframes.append(new_dataframe)

    for dataframe, category in zip(new_dataframes, categories):

        dfJson = dataframe.toPandas()

        dfJson.to_json(f'{category}.json', orient="records", force_ascii=False, lines=False)

def send_files_to_cosmos(file_path: str, database_name: str, container_name: str, cosmos_connection_string: str) -> dict:

    cosmos_client = CosmosClient.from_connection_string(cosmos_connection_string)

    with open(f"{file_path}", 'r', encoding="UTF-8") as file:
        file_as_json = json.load(file)

    file_as_dict = {}

    for dicts in file_as_json:
        file_as_dict[dicts["id"]] = dicts

    for index in range(1, len(file_as_dict)):
        cosmos_client.get_database_client(database_name).get_container_client(container_name).create_item((file_as_dict[str(index)]))

for name in names:
    send_files_to_cosmos(
        file_path=name,
        database_name=DATABASE_NAME,
        container_name=CONTAINER_NAME,
        cosmos_connection_string=COSMOS_CONNECTION_STRING
    )