import pickle
import pandas as pd

# Load the ARIMA model
with open("arima_model.pkl", "rb") as f:
    model = pickle.load(f)

def predict_stock(input_data):
    """
    input_data: dictionary with structure like {"feature": value}
    For ARIMA, usually you just forecast next n steps.
    """
    # Example: forecasting next n steps
    n_steps = input_data.get("steps", 5)
    forecast = model.forecast(steps=n_steps)
    return forecast.tolist()

# Example usage
if __name__ == "__main__":
    data = {"steps": 10}
    print(predict_stock(data))