"""
场景推荐接口测试
"""
import pytest
from fastapi.testclient import TestClient
from unittest.mock import patch, AsyncMock
from app.main import app

client = TestClient(app)


@pytest.fixture
def sample_request_data():
    """示例请求数据"""
    return {
        "project_id": "project_001",
        "company_info": {
            "industry": ["零售", "电商"],
            "description": "某连锁生鲜零售企业，主营水果、蔬菜等生鲜产品",
            "data_scale": "100GB",
            "data_sources": ["交易系统", "会员系统", "供应链系统"]
        },
        "data_assets": [
            {
                "name": "会员基础信息表",
                "description": "存储C端注册用户的核心身份信息",
                "core_tables": ["t_user_base_01", "t_user_profile_02"]
            },
            {
                "name": "交易流水表",
                "description": "记录所有交易订单的详细信息",
                "core_tables": ["t_order_detail_01", "t_order_summary_02"]
            }
        ],
        "existing_scenarios": [
            {
                "name": "会员画像分析",
                "description": "基于会员消费行为分析用户画像"
            }
        ],
        "options": {
            "model": "qwen-max",
            "temperature": 0.3
        }
    }


@pytest.fixture
def mock_llm_response():
    """模拟大模型响应"""
    return {
        "recommended_scenarios": [
            {
                "scenario_name": "智能推荐系统",
                "category": "营销增长",
                "description": "基于用户历史行为和偏好，智能推荐商品",
                "business_value": "提升转化率15%，增加客单价20%",
                "data_requirements": ["会员基础信息表", "交易流水表"],
                "priority": "高",
                "estimated_effort": "中等",
                "recommendation_score": 5
            },
            {
                "scenario_name": "供应链优化",
                "category": "降本增效",
                "description": "优化库存管理，减少损耗",
                "business_value": "降低库存成本10%，减少损耗5%",
                "data_requirements": ["交易流水表", "供应链数据"],
                "priority": "中",
                "estimated_effort": "高",
                "recommendation_score": 4
            }
        ]
    }


@pytest.mark.asyncio
async def test_scenario_recommendation_success(sample_request_data, mock_llm_response):
    """测试场景推荐成功"""
    import json
    with patch('app.services.scenario_recommendation_service.llm_client.call') as mock_call:
        # 模拟大模型返回 JSON 字符串
        mock_call.return_value = json.dumps(mock_llm_response, ensure_ascii=False)
        
        response = client.post(
            "/api/v1/value/scenario-recommendation",
            json=sample_request_data
        )
        
        assert response.status_code == 200
        data = response.json()
        assert data["success"] is True
        assert data["code"] == 200
        assert "data" in data
        assert "recommended_scenarios" in data["data"]
        assert len(data["data"]["recommended_scenarios"]) > 0


def test_scenario_recommendation_request_validation():
    """测试请求验证"""
    # 测试缺少必需字段
    invalid_request = {
        "project_id": "project_001"
    }
    
    response = client.post(
        "/api/v1/value/scenario-recommendation",
        json=invalid_request
    )
    
    assert response.status_code == 422  # 验证错误


def test_scenario_recommendation_empty_data_assets():
    """测试空数据资产列表"""
    request_data = {
        "project_id": "project_001",
        "data_assets": [],
        "existing_scenarios": []
    }
    
    response = client.post(
        "/api/v1/value/scenario-recommendation",
        json=request_data
    )
    
    # 应该返回 422 或 200（取决于业务逻辑）
    assert response.status_code in [200, 422]


def test_scenario_recommendation_with_options():
    """测试带选项的请求"""
    import json
    request_data = {
        "project_id": "project_001",
        "company_info": {
            "industry": ["零售"],
            "description": "某连锁生鲜零售企业",
            "data_scale": "100TB",
            "data_sources": ["交易系统", "会员系统"]
        },
        "data_assets": [
            {
                "name": "测试表",
                "description": "测试描述",
                "core_tables": ["test_table_01"]
            }
        ],
        "existing_scenarios": [],
        "options": {
            "model": "gpt-4",
            "temperature": 0.5
        }
    }
    
    with patch('app.services.scenario_recommendation_service.llm_client.call') as mock_call:
        mock_call.return_value = json.dumps({"recommended_scenarios": []}, ensure_ascii=False)
        
        response = client.post(
            "/api/v1/value/scenario-recommendation",
            json=request_data
        )
        
        assert response.status_code == 200


if __name__ == "__main__":
    pytest.main([__file__, "-v"])