import pandas as pd
import xgboost as xgb
import math
import joblib
import json

def processing(input_data):
    df = pd.DataFrame(input_data)
    model = joblib.load("./xgboost_model.joblib") 
    with open('./category_orders_train.json', 'r') as f:
        category_orders = json.load(f)

    if df.empty:
        raise ValueError("Input DataFrame is empty.")
    
    categorical_columns = ["application_source_name", "ownhome", "employmentstatus", "payfrequency", "fraud_risk", "ip_net_speed_cell", "riskrating"]
    for col in categorical_columns:
        if col in df.columns:
            df[col] = df[col].str.lower()
            df[col].replace([None, "", "null", math.isnan, pd.NA], "none", inplace=True)
            df[col] = pd.Categorical(df[col], categories=category_orders[col])
        else:
            df[col] = pd.Categorical(["none"], categories=category_orders.get(col, ["none"]))

    non_categorical_columns = [col for col in df.columns if col not in categorical_columns]
    for col in non_categorical_columns:
        if col in df.columns:
            df[col] = df[col].astype(str).str.lower().replace(["null", "nan", "", None], pd.NA)
            df[col] = pd.to_numeric(df[col], errors="coerce")
        else:
            df[col] = pd.NA

    expected_features = model.feature_names
    missing_features = [feature for feature in expected_features if feature not in df.columns]
    for feature in missing_features:
        df[feature] = None 

    dmatrix = xgb.DMatrix(df[expected_features], enable_categorical=True)
    predictions = model.predict(dmatrix)
    
    return predictions