58 lines
3.1 KiB
Python
58 lines
3.1 KiB
Python
import logging
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import requests
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# Configure logging
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(levelname)s] %(name)s - %(message)s",
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)
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logger = logging.getLogger(__name__)
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url = "http://centurion-h2o-server.default.svc.cluster.local:8000/scoring"
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headers = {
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"accept": "application/json",
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"Content-Type": "application/json"
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}
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def __main__(pti: float, score_results: int, revolving_amount_monthly_payment: int, AT31S: int, total_amount_high_credit: int, AT20S: int, BALMAG01: int, record_counts_revolving_trade_count: int, PAYMNT10: int, closed_with_balance_amount_current_balance: int, REV83: int, AGG102: int, BC21S: int, record_counts_total_trade_count: int, revolving_amount_current_balance: int, total_amount_current_balance: int, revolving_amount_high_credit: int, US01S: int, AGG101: int, closed_with_balance_amount_monthly_payment: int, revolving_amount_credit_limit: int, revolving_amount_percent_available_credit: int, AT09S: int) -> dict:
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input_data = {
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"pti": pti, "score_results": score_results, "revolving_amount_monthly_payment": revolving_amount_monthly_payment,
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"AT31S": AT31S, "total_amount_high_credit": total_amount_high_credit, "AT20S": AT20S, "BALMAG01": BALMAG01,
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"record_counts_revolving_trade_count": record_counts_revolving_trade_count, "PAYMNT10": PAYMNT10,
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"closed_with_balance_amount_current_balance": closed_with_balance_amount_current_balance, "REV83": REV83,
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"AGG102": AGG102, "BC21S": BC21S, "record_counts_total_trade_count": record_counts_total_trade_count,
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"revolving_amount_current_balance": revolving_amount_current_balance, "total_amount_current_balance": total_amount_current_balance,
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"revolving_amount_high_credit": revolving_amount_high_credit, "US01S": US01S, "AGG101": AGG101,
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"closed_with_balance_amount_monthly_payment": closed_with_balance_amount_monthly_payment,
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"revolving_amount_credit_limit": revolving_amount_credit_limit, "revolving_amount_percent_available_credit": revolving_amount_percent_available_credit,
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"AT09S": AT09S
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}
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filtered_data = {key: value for key, value in input_data.items() if value is not None}
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data = {
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"model_name": "pd_v1",
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"features": filtered_data
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}
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try:
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try:
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response = requests.post(url, headers=headers, json=data)
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response.raise_for_status() # Raise an error for HTTP issues
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# Parse the response and return the "p1" value
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response_data = response.json()
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print("response json", response_data)
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p1_value = response_data.get("prediction", {}).get("p1")
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except requests.RequestException as error:
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logger.error(f"Error while predicting: {error}")
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return None
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if p1_value is not None:
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logger.info(f"Prediction p1: {p1_value}")
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return {'probability': float(p1_value)}
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else:
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logger.error("Response does not contain 'p1'")
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return None
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except Exception as error:
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logger.error(f"Error while predicting: {error}")
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return {'error': str(error)} |