def get_game(names):
    #name = "The Lord of the Rings: The Battle for Middle-Earth"
    games=[]
    for name in names:
        datas= game_id_df[game_id_df.Title == name]
        if not datas.empty:
            data_index = game_id_df[game_id_df.Title == name].index[0]
            train_data_reviews[data_index]

            new_datapoint = [train_data_reviews[data_index]]
            new_vec = lsa.transform(tfidf_vectorizer.transform(new_datapoint))

            nn = NearestNeighbors(n_neighbors=5, metric='cosine', algorithm='brute')
            nn.fit(lsa_tfidf_data)

            result = nn.kneighbors(new_vec)


            for r in result[1][0]:
                game = game_id_df.Title[r]
                if game not in games:
                    games.append(game)
    return(games)

profile = sys.argv[1]
response = req.get("https://localhost:5001/api/profile/getUserGamesNames?userId=" + profile)

data = json.loads(response.text)
games_list = []
for games in data['games']:
    games_list.append(games)
list_of_games= get_game(games_list)

with open('list_of_games.txt', 'w') as f:
    for item in list_of_games:
        f.write("%s\n" % item)