"""
场景优化接口测试
"""
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 {
        "existing_scenarios": [
            {
                "name": "会员画像分析",
                "description": "基于会员消费行为分析用户画像"
            }
        ],
        "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"]
            }
        ],
        "company_info": {
            "industry": ["零售"],
            "description": "某连锁生鲜零售企业"
        }
    }


@pytest.fixture
def sample_request_data_with_screenshots():
    """示例请求数据（含截图）"""
    return {
        "existing_scenarios": [
            {
                "name": "会员画像分析",
                "description": "基于会员消费行为分析用户画像"
            }
        ],
        "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"]
            }
        ],
        "company_info": {
            "industry": ["零售"],
            "description": "某连锁生鲜零售企业"
        },
        "scenario_screenshots": [
            "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNk+M9QDwADhgGAWjR9awAAAABJRU5ErkJggg=="
        ]
    }


@pytest.fixture
def mock_llm_response():
    """模拟大模型响应"""
    return {
        "optimization_suggestions": [
            {
                "scenario_name": "会员画像分析",
                "current_status": "当前仅基于交易数据进行画像，维度单一",
                "suggestions": [
                    "增加用户行为轨迹分析，包括浏览、收藏、分享等",
                    "引入第三方数据源，丰富用户标签体系",
                    "优化画像可视化展示，提升用户体验"
                ],
                "potential_value": "提升画像准确率20%，增加营销转化率15%"
            }
        ]
    }


@pytest.fixture
def mock_vision_llm_response():
    """模拟视觉大模型响应"""
    return """场景截图分析结果：
1. 界面布局：整体布局清晰，但信息密度较高，建议优化留白
2. 数据展示：图表类型单一，建议增加更多可视化方式
3. 交互体验：筛选功能不够直观，建议优化交互设计
4. 功能完整性：缺少导出功能，建议添加"""


@pytest.mark.asyncio
async def test_scenario_optimization_success(sample_request_data, mock_llm_response):
    """测试场景优化成功"""
    import json
    with patch('app.services.scenario_optimization_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-optimization",
            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 "optimization_suggestions" in data["data"]
        assert len(data["data"]["optimization_suggestions"]) > 0


@pytest.mark.asyncio
async def test_scenario_optimization_with_screenshots(sample_request_data_with_screenshots, mock_llm_response, mock_vision_llm_response):
    """测试场景优化（含截图）"""
    import json
    with patch('app.services.scenario_optimization_service.llm_client.call') as mock_call:
        with patch('httpx.AsyncClient') as mock_httpx_client:
            # 模拟大模型返回 JSON 字符串
            mock_call.return_value = json.dumps(mock_llm_response, ensure_ascii=False)
            
            # 模拟视觉大模型响应
            mock_response = AsyncMock()
            mock_response.status_code = 200
            mock_response.raise_for_status = AsyncMock()
            mock_response.json.return_value = {
                "choices": [{
                    "message": {
                        "content": mock_vision_llm_response
                    }
                }]
            }
            
            # 设置 AsyncClient 的上下文管理器
            mock_client_instance = AsyncMock()
            mock_client_instance.post = AsyncMock(return_value=mock_response)
            mock_httpx_client.return_value.__aenter__ = AsyncMock(return_value=mock_client_instance)
            mock_httpx_client.return_value.__aexit__ = AsyncMock(return_value=False)
            
            response = client.post(
                "/api/v1/value/scenario-optimization",
                json=sample_request_data_with_screenshots
            )
            
            assert response.status_code == 200
            data = response.json()
            assert data["success"] is True
            assert "data" in data


def test_scenario_optimization_request_validation():
    """测试请求验证"""
    # 测试空场景列表（应该是有效的，因为 existing_scenarios 是必需的但可以是空列表）
    valid_request = {
        "existing_scenarios": [],
        "data_assets": [],
        "company_info": {
            "industry": ["零售"],
            "description": "某连锁生鲜零售企业"
        }
    }
    
    with patch('app.services.scenario_optimization_service.llm_client.call') as mock_call:
        import json
        mock_call.return_value = json.dumps({"optimization_suggestions": []}, ensure_ascii=False)
        
        response = client.post(
            "/api/v1/value/scenario-optimization",
            json=valid_request
        )
        
        # 应该返回 200（空场景列表也可以处理）
        assert response.status_code == 200


def test_scenario_optimization_empty_scenarios():
    """测试空场景列表"""
    request_data = {
        "existing_scenarios": [],
        "data_assets": [],
        "company_info": {
            "industry": ["零售"],
            "description": "某连锁生鲜零售企业"
        }
    }
    
    with patch('app.services.scenario_optimization_service.llm_client.call') as mock_call:
        import json
        mock_call.return_value = json.dumps({"optimization_suggestions": []}, ensure_ascii=False)
        
        response = client.post(
            "/api/v1/value/scenario-optimization",
            json=request_data
        )
        
        # 应该返回 200（空场景列表也可以处理）
        assert response.status_code == 200


def test_scenario_optimization_with_options():
    """测试带选项的请求"""
    import json
    request_data = {
        "existing_scenarios": [
            {
                "name": "测试场景",
                "description": "测试描述"
            }
        ],
        "data_assets": [],
        "options": {
            "model": "gpt-4",
            "temperature": 0.5
        }
    }
    
    with patch('app.services.scenario_optimization_service.llm_client.call') as mock_call:
        mock_call.return_value = json.dumps({"optimization_suggestions": []}, ensure_ascii=False)
        
        response = client.post(
            "/api/v1/value/scenario-optimization",
            json=request_data
        )
        
        assert response.status_code == 200


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