# File: logic/financial_model.py

def normalize_bool(value):
    if isinstance(value, str):
        value = value.strip().lower()
        return value in ["yes", "true", "1"]
    return bool(value)

def estimate_financial_recovery(care_gaps_df, patient_data, base_rate, sdoh_keys):
    results = []
    hcc_weights = {
        'HCC18': 0.4, 'HCC85': 0.5, 'HCC19': 0.3
    }

    for _, row in care_gaps_df.iterrows():
        patient = patient_data[patient_data['patient_id'] == row['patient_id']].iloc[0]
        hccs = [h.strip() for h in str(patient.get('hcc_codes', '')).split(';') if h.strip() in hcc_weights]
        risk_score = sum(hcc_weights[h] for h in hccs)

        sdoh_count = sum(1 for key in sdoh_keys if normalize_bool(patient.get(key)))
        sdoh_modifier = 1 + 0.05 * sdoh_count  # Each SDOH adds 5% to the opportunity

        care_gap_count = len(row['care_gaps'])
        expected_gain = base_rate * (risk_score * 0.02 * care_gap_count) * sdoh_modifier

        results.append({
            'patient_id': row['patient_id'],
            'risk_score': round(risk_score, 2),
            'projected_gain': round(expected_gain, 2),
            'sdoh_flags': sdoh_count
        })

    return pd.DataFrame(results)