forecast_engine.py

import numpy as np
from joblib import load

model = load("model.pkl")

def forecast_consumables(usage_series, days):
    forecast = []
    current_series = usage_series.copy()
    
    for _ in range(days):
        # Prepare the input for prediction (last 60 days)
        input_series = np.array(current_series[-60:]).reshape(1, -1)
        # Predict the next day's usage
        next_usage = model.predict(input_series)[0]
        next_usage = max(0, int(next_usage))  # Ensure non-negative and integer
        next_usage = min(next_usage, 100)    # Cap at 100
        forecast.append(next_usage)
        # Append the predicted usage for the next iteration
        current_series.append(next_usage)
    
    return forecast