import xgboost as xgb
import pandas as pd
import joblib
import logging

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(name)s - %(message)s",
)
logger = logging.getLogger(__name__)


def processing(data):
    df = pd.DataFrame([data])
    if df.empty:
        logger.error("Input DataFrame is empty.")

    # Load Model
    model_path = "./xgboost_model.joblib"
    # model_path ="C:/Users/abinisha/habemco_flowx/g1_v1/xgboost_model.joblib"
    model = joblib.load(model_path)

    expected_features = model.feature_names

    df = df.applymap(lambda x: float('nan') if x is None else x)

    dmatrix = xgb.DMatrix(df[expected_features], enable_categorical=True, missing=float('nan'))

    prediction = model.predict(dmatrix)

    df['prediction'] = prediction

    return df.iloc[0].to_dict()