async def future_budget(budget: Budget):
"""
Suggest a budget for a specified user.
### Request Body
- `bank_account_id`: int
- `monthly_savings_goal`: integer
### Response
- `category`: grandparent category name
- `budgeted_amount`: integer suggesting the maximum the user should spend
in that catgory next month
"""
# Get the JSON object from the request body and cast it to a dictionary
input_dict = budget.to_dict()
bank_account_id = input_dict['bank_account_id']
monthly_savings_goal = input_dict['monthly_savings_goal']
transactions = load_user_data(bank_account_id)
# instantiate the user and chooses category column
# user = User(transactions, cat_column='grandparent_category_name')
# user = User(transactions, cat_column='parent_category_name')
user = User(transactions, cat_column='merchant_name')
# predict budget using time series model
pred_bud = user.predict_budget()
# if a fatal error was encountered while generating the budget,
# return no budget along with the warning list
if user.warning == 2:
return json.dumps([None, user.warning_list])
# modify budget based on savings goal
modified_budget = user.budget_modifier(
pred_bud, monthly_savings_goal=monthly_savings_goal)
# if a fatal error was encountered while modifying the budget,
# return no budget along with the warning list
if user.warning == 2:
return json.dumps([None, user.warning_list])
# if a non-fatal warning was encountered in predict_budget() or
# budget_modifier(), return the budget along with the warning list
elif user.warning == 1:
return json.dumps([modified_budget, user.warning_list])
return modified_budget
