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(feat) /predict/spend endpoint

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ishaan-jaff 2024-03-01 08:20:35 -08:00
parent 3bb861ae02
commit 47c5b94c50
2 changed files with 44 additions and 20 deletions
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52
enterprise/utils.py
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@ -251,26 +251,27 @@ def _forecast_daily_cost(data: list):
import requests
from datetime import datetime, timedelta
# Get the current date
current_date = datetime.now().date()
# Get the last entry in the data
last_entry = data[-1]
# Parse the date from the last entry
last_entry_date = datetime.strptime(last_entry["date"], "%Y-%m-%d").date()
# print("Last Entry Date:", last_entry_date)
# Get the month of the last entry
last_entry_month = last_entry_date.month
# print("Last Entry Month:", last_entry_month)
# Calculate the last day of the month
last_day_of_month = (
datetime(last_entry_date.year, last_entry_date.month % 12 + 1, 1)
- timedelta(days=1)
).day
# print("Last Day of Month:", last_day_of_month)
# Calculate the remaining days in the month
remaining_days = last_day_of_month - last_entry_date.day
# print("Remaining Days:", remaining_days)
series = {}
for entry in data:
@ -278,7 +279,7 @@ def _forecast_daily_cost(data: list):
spend = entry["spend"]
series[date] = spend
payload = {"series": series, "count": 5}
payload = {"series": series, "count": remaining_days}
print("Prediction Data:", payload)
headers = {
@ -294,23 +295,34 @@ def _forecast_daily_cost(data: list):
json_response = response.json()
forecast_data = json_response["forecast"]
print("Forecast Data:", forecast_data)
# print("Forecast Data:", forecast_data)
response_data = []
for date in forecast_data:
spend = forecast_data[date]
entry = {
"date": date,
"predicted_spend": spend,
}
response_data.append(entry)
# print("Response Data:", response_data)
return response_data
# print(f"Date: {entry['date']}, Spend: {entry['spend']}, Response: {response.text}")
_forecast_daily_cost(
[
{"date": "2022-01-01", "spend": 100},
{"date": "2022-01-02", "spend": 200},
{"date": "2022-01-03", "spend": 300},
{"date": "2022-01-04", "spend": 400},
{"date": "2022-01-05", "spend": 500},
{"date": "2022-01-06", "spend": 600},
{"date": "2022-01-07", "spend": 700},
{"date": "2022-01-08", "spend": 800},
{"date": "2022-01-09", "spend": 900},
{"date": "2022-01-10", "spend": 1000},
{"date": "2022-01-11", "spend": 50},
]
)
# _forecast_daily_cost(
# [
# {"date": "2022-01-01", "spend": 100},
# {"date": "2022-01-02", "spend": 200},
# {"date": "2022-01-03", "spend": 300},
# {"date": "2022-01-04", "spend": 400},
# {"date": "2022-01-05", "spend": 500},
# {"date": "2022-01-06", "spend": 600},
# {"date": "2022-01-07", "spend": 700},
# {"date": "2022-01-08", "spend": 800},
# {"date": "2022-01-09", "spend": 900},
# {"date": "2022-01-10", "spend": 1000},
# {"date": "2022-01-11", "spend": 50},
# ]
# )