import joblib
import xgboost as xgb
import shap

def processing(processed_dict):
    # model_path = r"C:\Users\abinisha\citrus\centurion-dataform-mcard\flowx\model_1\artifacts\xgboost_model_v2.joblib"
    model_path = "./xgboost_model.joblib"
    model       = joblib.load(model_path)
    explainer   = shap.TreeExplainer(model)
    feature_names = model.feature_names

    # build and score
    X       = [processed_dict[f] for f in feature_names]
    dmatrix = xgb.DMatrix([X], feature_names=feature_names)
    pred    = float(model.predict(dmatrix)[0])

    # get SHAP values as 1×n_features
    shap_matrix = explainer.shap_values(dmatrix)
    shap_vals   = shap_matrix[0] if getattr(shap_matrix, "ndim", 1) > 1 else shap_matrix

    # rank & sort features by descending SHAP impact
    ranked = sorted(zip(feature_names, shap_vals),
                    key=lambda fv: fv[1],
                    reverse=True)

    # build a *list* of pre-sorted reason-codes
    shape_reasoncode = [
        {
            "feature":   feat,
            "value":     processed_dict[feat],
            "shap_rank": i+1
        }
        for i, (feat, _) in enumerate(ranked)
    ]

    return {
        "prediction":       pred,
        "shape_reasoncode": shape_reasoncode
    }