import pytest
from app.models.schemas import InputSchema
from app.services.ml_service import ml_service

def test_input_schema_validation():
    # Test valid data
    valid_data = {
        "age": 30,
        "genre": "M",
        "revenu_mensuel": 5000,
        "statut_marital": "Célibataire",
        "departement": "R&D",
        "poste": "Ingénieur",
        "nombre_experiences_precedentes": 2,
        "nombre_heures_travailless": 40,
        "annee_experience_totale": 5,
        "annees_dans_l_entreprise": 2,
        "annees_dans_le_poste_actuel": 1,
        "satisfaction_employee_environnement": 3,
        "note_evaluation_precedente": 3,
        "niveau_hierarchique_poste": 2,
        "satisfaction_employee_nature_travail": 3,
        "satisfaction_employee_equipe": 4,
        "satisfaction_employee_equilibre_pro_perso": 3,
        "note_evaluation_actuelle": 3,
        "heure_supplementaires": "Non",
        "augementation_salaire_precedente": "10-15%",
        "nombre_participation_pee": 0,
        "nb_formations_suivies": 1,
        "nombre_employee_sous_responsabilite": 0,
        "distance_domicile_travail": 10,
        "niveau_education": 3,
        "domaine_etude": "Sciences",
        "ayant_enfants": "Non",
        "frequence_deplacement": "Rare",
        "annees_depuis_la_derniere_promotion": 1,
        "annes_sous_responsable_actuel": 1
    }
    schema = InputSchema(**valid_data)
    assert schema.age == 30
    assert schema.genre == "M"

    # Test invalid data (missing field)
    invalid_data = valid_data.copy()
    del invalid_data["age"]
    with pytest.raises(ValueError):
        InputSchema(**invalid_data)

def test_model_loading():
    assert ml_service.model is not None
    assert ml_service.expected_features is not None