predict_risk_of_default/test_prediction_api.py at main · whitks/predict_risk_of_default · GitHub

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import requests
import json

def test_prediction():
    url = "http://localhost:8000/api/model/predict-with-composite"

    # Healthy case features from our CSV
    payload = {
        "features": {
            "total_assets": 10000000,
            "net_worth": 6000000,
            "total_liabilities": 4000000,
            "reserves_and_funds": 2000000,
            "borrowings": 1000000,
            "current_liabilities": 2000000,
            "deferred_tax_liability": 100000,
            "shareholders_funds": 5000000,
            "cumulative_retained_profits": 3000000,
            "total_capital": 5000000,
            "capital_employed": 8000000,
            "contingent_liabilities": 500000,
            "net_fixed_assets": 4000000,
            "investments": 1000000,
            "current_assets": 5000000,
            "net_working_capital": 3000000,
            "log_total_assets": 16.11,
            "total_income": 20000000,
            "sales": 18000000,
            "income_financial_services": 500000,
            "other_income": 1500000,
            "change_in_stock": 0,
            "total_expenses": 15000000,
            "profit_after_tax": 3500000,
            "pbdita": 5000000,
            "pbt": 4500000,
            "cash_profit": 4000000,
            "pbdita_pct_income": 25.0,
            "pbt_pct_income": 22.5,
            "pat_pct_income": 17.5,
            "cash_profit_pct_income": 20.0,
            "pat_pct_networth": 58.33,
            "pat_to_assets": 0.35,
            "pbdita_to_assets": 0.5,
            "pat_to_sales": 0.19,
            "pbdita_to_sales": 0.27,
            "debt_to_equity": 0.16,
            "tol_to_tnw": 0.66,
            "term_liabilities_to_tnw": 0.1,
            "contingent_liab_pct_networth": 5.0,
            "borrowings_to_assets": 0.1,
            "liabilities_to_assets": 0.4,
            "current_ratio": 2.5,
            "quick_ratio": 2.0,
            "cash_to_cur_liabilities": 0.5,
            "cash_to_cost_per_day": 30.0,
            "creditors_turnover": 12.0,
            "debtors_turnover": 8.0,
            "finished_goods_turnover": 15.0,
            "wip_turnover": 10.0,
            "raw_material_turnover": 15.0,
            "altman_z": 4.5,
            "ohlson_prob": 0.01,
            "fin_services_pct_income": 0.025,
            "other_income_pct": 0.075
        },
        "ml_pd": 0.02,
        "company_name": "Test Healthy Corp"
    }

    # Wait, CompositeScoreRequest requires ml_pd
    # class CompositeScoreRequest(BaseModel):
    #     features: Dict[str, float]
    #     ml_pd: float
    #     company_name: Optional[str]

    try:
        # Pydantic schema expects features, ml_pd, company_name
        response = requests.post(url, json=payload)
        print(f"Status: {response.status_code}")
        try:
            print(f"Response: {json.dumps(response.json(), indent=2)}")
        except:
            print(f"Raw Response: {response.text}")
    except Exception as e:
        print(f"Error: {e}")

if __name__ == "__main__":
    test_prediction()