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import joblib
import lightgbm as lgb
import pandas as pd
# Load artifacts
def load_artifacts():
model = lgb.Booster(model_file="models/lgb_sales_model.txt")
feature_cols = joblib.load("models/feature_cols.pkl")
return model, feature_cols
# Preprocess new input row into model-ready features
def preprocess_input(promo, holiday, date, past_sales):
"""
Args:
promo: int (0/1)
holiday: int (0/1)
date: datetime-like
past_sales: dict with keys ['lag_1','lag_7','mean_3','mean_7']
Returns:
pd.DataFrame with a single row ready for prediction
"""
date = pd.to_datetime(date)
features = {
"promo": promo,
"holiday": holiday,
"day": date.day,
"month": date.month,
"year": date.year,
"day_of_week": date.weekday(),
"is_weekend": 1 if date.weekday() >= 5 else 0,
"sales_lag_1": past_sales.get("lag_1", 0),
"sales_lag_7": past_sales.get("lag_7", 0),
"rolling_mean_3": past_sales.get("mean_3", 0),
"rolling_mean_7": past_sales.get("mean_7", 0),
}
return pd.DataFrame([features])
# Prediction
def predict_sales(model, feature_cols, promo, holiday, date, lag_1, lag_7, mean_3, mean_7):
past_sales = {
"lag_1": lag_1,
"lag_7": lag_7,
"mean_3": mean_3,
"mean_7": mean_7,
}
X = preprocess_input(promo, holiday, date, past_sales)
X = X[feature_cols] # ensure correct column order
prediction = model.predict(X)[0]
return round(prediction, 2)
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