import importlib.util
import sys
import types
import unittest
from pathlib import Path


def load_predict_module():
    module_path = Path(r'D:\forsetsystem\backend\services\predict_service.py')

    fake_config = types.SimpleNamespace(
        MODELS_DIR='',
        SCALER_PATH='',
        JDR_DIMENSIONS={
            'job_demands': {'name_cn': '工作要求'},
            'job_resources': {'name_cn': '工作资源'},
            'personal_resources': {'name_cn': '个人资源'},
            'mediators': {'name_cn': '中介变量'},
        },
    )
    fake_deep_learning = types.ModuleType('core.deep_learning_model')
    fake_deep_learning.load_lstm_mlp_bundle = lambda path: None
    fake_deep_learning.predict_lstm_mlp = lambda model, data: 0.0

    fake_model_features = types.ModuleType('core.model_features')
    fake_model_features.align_feature_frame = lambda frame, names: frame
    fake_model_features.apply_label_encoders = lambda frame, encoders: frame
    fake_model_features.build_prediction_dataframe = lambda data: data
    fake_model_features.engineer_features = lambda frame: frame
    fake_model_features.to_float_array = lambda frame: frame

    sys.modules['config'] = fake_config
    sys.modules['core.deep_learning_model'] = fake_deep_learning
    sys.modules['core.model_features'] = fake_model_features

    spec = importlib.util.spec_from_file_location('test_predict_service_module', module_path)
    module = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(module)
    return module


class PredictExplanationTests(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        module = load_predict_module()
        cls.service = module.PredictService()

    def test_build_jdr_snapshot_marks_high_demands_and_low_resources(self):
        snapshot = self.service._build_jdr_snapshot({
            '工作要求指数': 5.8,
            '工作资源指数': 2.7,
            '个人资源指数': 2.8,
            'JD-R平衡度': -1.1,
            '倦怠风险指数': 3.1,
            '工作投入指数': 2.9,
        })

        self.assertEqual(snapshot['job_demands']['status'], '偏高')
        self.assertEqual(snapshot['job_resources']['status'], '偏低')
        self.assertEqual(snapshot['balance']['status'], '明显失衡')
        self.assertEqual(snapshot['burnout_risk']['status'], '偏高')

    def test_mechanism_summary_prefers_health_impairment_path(self):
        snapshot = self.service._build_jdr_snapshot({
            '工作要求指数': 5.6,
            '工作资源指数': 2.9,
            '个人资源指数': 2.8,
            'JD-R平衡度': -0.9,
            '倦怠风险指数': 3.0,
            '工作投入指数': 2.9,
        })
        shap_local = {
            'dimension_contribution': {
                '工作要求': 0.32,
                '中介变量': 0.18,
                '事件上下文': 0.11,
                '工作资源': -0.07,
            },
            'features': [
                {'name': 'monthly_overtime_hours', 'name_cn': '月均加班时长', 'dimension': 'job_demands', 'shap_value': 0.18},
                {'name': 'commute_minutes', 'name_cn': '通勤时长', 'dimension': 'job_demands', 'shap_value': 0.12},
                {'name': 'medical_certificate_flag', 'name_cn': '医院证明', 'dimension': 'event_context', 'shap_value': 0.08},
                {'name': 'coworker_support', 'name_cn': '同事支持', 'dimension': 'job_resources', 'shap_value': -0.05},
            ],
        }
        result = {'predicted_hours': 9.4, 'risk_label': '高风险'}
        data = {
            'monthly_overtime_hours': 38,
            'commute_minutes': 62,
            'is_night_shift': 1,
            'medical_certificate_flag': 1,
        }

        summary = self.service._build_mechanism_summary(result, data, snapshot, shap_local)

        self.assertIn('健康损耗', summary['pathway_label'])
        self.assertIn('月均加班时长', summary['mechanism'])
        self.assertTrue(summary['scenario_hint'])

    def test_intervention_suggestions_cover_resource_and_personal_support(self):
        snapshot = self.service._build_jdr_snapshot({
            '工作要求指数': 4.4,
            '工作资源指数': 2.7,
            '个人资源指数': 2.6,
            'JD-R平衡度': -0.7,
            '倦怠风险指数': 2.9,
            '工作投入指数': 2.8,
        })
        suggestions = self.service._build_intervention_suggestions(
            {
                'monthly_overtime_hours': 18,
                'commute_minutes': 28,
                'chronic_disease_flag': 1,
                'medical_certificate_flag': 1,
                'leave_reason_category': '子女照护',
            },
            snapshot,
            shap_local=None,
        )

        category_map = {item['category']: item['items'] for item in suggestions}
        self.assertIn('增资源', category_map)
        self.assertIn('补个人资源', category_map)
        self.assertTrue(any('支持' in item or '弹性' in item for item in category_map['增资源']))
        self.assertTrue(any('健康' in item or '倦怠' in item for item in category_map['补个人资源']))

    def test_buffer_text_mentions_protective_factors(self):
        snapshot = self.service._build_jdr_snapshot({
            '工作要求指数': 3.9,
            '工作资源指数': 4.2,
            '个人资源指数': 4.0,
            'JD-R平衡度': 0.9,
            '倦怠风险指数': 1.8,
            '工作投入指数': 4.1,
        })
        shap_local = {
            'dimension_contribution': {
                '工作要求': 0.08,
                '工作资源': -0.12,
                '个人资源': -0.09,
            },
            'features': [
                {'name': 'supervisor_support', 'name_cn': '上级支持', 'dimension': 'job_resources', 'shap_value': -0.07},
                {'name': 'self_efficacy', 'name_cn': '自我效能感', 'dimension': 'personal_resources', 'shap_value': -0.05},
            ],
        }

        summary = self.service._build_mechanism_summary({'predicted_hours': 5.3, 'risk_label': '中风险'}, {}, snapshot, shap_local)

        self.assertIn('缓冲作用', summary['buffer_text'])
        self.assertTrue(summary['protective_factors'])


if __name__ == '__main__':
    unittest.main()