import logging
import requests

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

url = "http://centurion-h2o-server.default.svc.cluster.local:8000/scoring"
# url = "http://localhost:8000/scoring"
headers = {
    "accept": "application/json",
    "Content-Type": "application/json"
}

def __main__(score_results:float, AT34B:int, AT12S:int, revolving_amount_percent_available_credit:float, AT28A:int, record_counts_total_trade_count:int, record_counts_negative_trade_count:int, record_counts_revolving_trade_count:int, AT33A:int, AT35A:int, 
             record_counts_total_inquiry_count:int, IN20S:int, RE102S:int, installment_amount_monthly_payment:float, S061S:int,
               record_counts_installment_trade_count:int, BR02S:int, AGG103:float, ALL231:float, G069S:int, AT24S:int, BI02S:int)->dict:
    input_data = {
        "score_results": score_results,
        "AT34B": AT34B,
        "AT12S": AT12S,
        "revolving_amount_percent_available_credit": revolving_amount_percent_available_credit,
        "AT28A": AT28A,
        "record_counts_total_trade_count": record_counts_total_trade_count,
        "record_counts_negative_trade_count": record_counts_negative_trade_count,
        "record_counts_revolving_trade_count": record_counts_revolving_trade_count,
        "AT33A": AT33A,
        "AT35A": AT35A,
        "record_counts_total_inquiry_count": record_counts_total_inquiry_count,
        "IN20S": IN20S,
        "RE102S": RE102S,
        "installment_amount_monthly_payment": installment_amount_monthly_payment,
        "S061S": S061S,
        "record_counts_installment_trade_count": record_counts_installment_trade_count,
        "BR02S": BR02S,
        "AGG103": AGG103,
        "ALL231": ALL231,
        "G069S": G069S,
        "AT24S": AT24S,
        "BI02S": BI02S
    }

    filtered_data = {key: value for key, value in input_data.items() if value is not None}
    data = {
            "model_name": "repeat_v1",
            "features": filtered_data
            }
    try:
        try:
            response = requests.post(url, headers=headers, json=data)
            response.raise_for_status()  # Raise an error for HTTP issues

            # Parse the response and return the "" value
            response_data = response.json()
            _value = response_data.get("prediction", {}).get("p1")
        except requests.RequestException as error:
            logger.error(f"Error while predicting: {error}")
            return None
        
        if _value is not None:
            logger.info(f"Prediction : {_value}")
            return {'probability': float(_value)}
        else:
            logger.error("Response does not contain ''")
            return None

    except Exception as error:
        logger.error(f"Error while predicting: {error}")
        return {'error': str(error)}

    # finally:
    #     h2o.cluster().shutdown()