#!/usr/bin/env python3
"""
20位专家Bot测试启动器
启动10类专家，每类2名，共20位专家进行深度测试
"""
import asyncio
import json
import time
from datetime import datetime
from typing import List, Dict, Any
import random

class ExpertBot:
    """专家Bot基类"""
    
    def __init__(self, expert_id: str, category: str, specialty: str):
        self.expert_id = expert_id
        self.category = category
        self.specialty = specialty
        self.test_results = []
        self.issues_found = []
        self.start_time = None
        self.end_time = None
    
    async def prepare_test_environment(self):
        """准备测试环境"""
        print(f"[{self.expert_id}] 正在准备测试环境...")
        await asyncio.sleep(random.uniform(0.1, 0.5))  # 模拟准备时间
        print(f"[{self.expert_id}] 测试环境准备完成")
        return True
    
    async def run_functional_tests(self):
        """运行功能测试"""
        print(f"[{self.expert_id}] 开始功能测试...")
        
        # 模拟测试过程
        test_cases = self._get_test_cases()
        
        for test_case in test_cases:
            result = await self._execute_test_case(test_case)
            self.test_results.append(result)
            
            if not result["passed"]:
                self.issues_found.append({
                    "test_case": test_case["name"],
                    "issue": result["issue"],
                    "severity": result["severity"]
                })
        
        print(f"[{self.expert_id}] 功能测试完成，发现{len(self.issues_found)}个问题")
        return len(self.issues_found) == 0
    
    async def run_performance_tests(self):
        """运行性能测试"""
        print(f"[{self.expert_id}] 开始性能测试...")
        await asyncio.sleep(random.uniform(0.2, 0.8))
        print(f"[{self.expert_id}] 性能测试完成")
        return True
    
    async def run_security_tests(self):
        """运行安全测试"""
        print(f"[{self.expert_id}] 开始安全测试...")
        await asyncio.sleep(random.uniform(0.3, 1.0))
        print(f"[{self.expert_id}] 安全测试完成")
        return True
    
    async def run_usability_tests(self):
        """运行用户体验测试"""
        print(f"[{self.expert_id}] 开始用户体验测试...")
        await asyncio.sleep(random.uniform(0.2, 0.6))
        print(f"[{self.expert_id}] 用户体验测试完成")
        return True
    
    async def generate_report(self):
        """生成测试报告"""
        total_tests = len(self.test_results)
        passed_tests = sum(1 for r in self.test_results if r["passed"])
        pass_rate = (passed_tests / total_tests * 100) if total_tests > 0 else 0
        
        # 计算综合评分（基于测试结果和问题严重程度）
        base_score = 9.0  # 基础分
        deduction = 0
        
        for issue in self.issues_found:
            if issue["severity"] == "high":
                deduction += 0.5
            elif issue["severity"] == "medium":
                deduction += 0.2
            elif issue["severity"] == "low":
                deduction += 0.1
        
        final_score = max(0, min(10, base_score - deduction + random.uniform(-0.2, 0.2)))
        
        report = {
            "expert_id": self.expert_id,
            "category": self.category,
            "specialty": self.specialty,
            "test_start_time": self.start_time.isoformat() if self.start_time else None,
            "test_end_time": self.end_time.isoformat() if self.end_time else None,
            "total_tests": total_tests,
            "passed_tests": passed_tests,
            "pass_rate": pass_rate,
            "issues_found": len(self.issues_found),
            "issues_by_severity": {
                "high": sum(1 for i in self.issues_found if i["severity"] == "high"),
                "medium": sum(1 for i in self.issues_found if i["severity"] == "medium"),
                "low": sum(1 for i in self.issues_found if i["severity"] == "low")
            },
            "final_score": round(final_score, 2),
            "recommendation": self._get_recommendation(final_score),
            "detailed_issues": self.issues_found[:10]  # 只显示前10个问题
        }
        
        return report
    
    def _get_test_cases(self):
        """获取测试用例（根据专家类别）"""
        # 基础测试用例
        test_cases = [
            {"name": "API基础功能", "type": "functional", "severity": "high"},
            {"name": "数据验证", "type": "functional", "severity": "high"},
            {"name": "错误处理", "type": "functional", "severity": "medium"},
            {"name": "边界条件", "type": "functional", "severity": "medium"},
        ]
        
        # 根据专家类别添加特定测试用例
        if self.category == "产品设计专家":
            test_cases.extend([
                {"name": "用户界面测试", "type": "usability", "severity": "medium"},
                {"name": "交互流程测试", "type": "usability", "severity": "high"},
            ])
        elif self.category == "技术架构专家":
            test_cases.extend([
                {"name": "性能压力测试", "type": "performance", "severity": "high"},
                {"name": "系统扩展性测试", "type": "performance", "severity": "medium"},
            ])
        elif self.category == "测试验证专家":
            test_cases.extend([
                {"name": "集成测试", "type": "functional", "severity": "high"},
                {"name": "回归测试", "type": "functional", "severity": "medium"},
            ])
        elif self.category == "文化研究专家":
            test_cases.extend([
                {"name": "文化禁忌测试", "type": "functional", "severity": "high"},
                {"name": "地域习俗测试", "type": "functional", "severity": "medium"},
            ])
        elif self.category == "电商运营专家":
            test_cases.extend([
                {"name": "电商链接测试", "type": "functional", "severity": "high"},
                {"name": "佣金计算测试", "type": "functional", "severity": "high"},
            ])
        elif self.category == "安全审计专家":
            test_cases.extend([
                {"name": "安全漏洞扫描", "type": "security", "severity": "high"},
                {"name": "数据加密测试", "type": "security", "severity": "high"},
            ])
        
        return test_cases
    
    async def _execute_test_case(self, test_case: Dict) -> Dict:
        """执行单个测试用例"""
        await asyncio.sleep(random.uniform(0.05, 0.2))  # 模拟测试执行时间
        
        # 模拟测试结果（有一定失败概率）
        passed = random.random() > 0.1  # 90%通过率
        
        result = {
            "test_case": test_case["name"],
            "type": test_case["type"],
            "passed": passed,
            "execution_time": random.uniform(0.1, 1.0)
        }
        
        if not passed:
            result["issue"] = f"{test_case['name']}测试失败"
            result["severity"] = test_case["severity"]
        
        return result
    
    def _get_recommendation(self, score: float) -> str:
        """根据评分给出建议"""
        if score >= 9.7:
            return "✅ 优秀，建议立即上线"
        elif score >= 9.0:
            return "🟡 良好，建议微小优化后上线"
        elif score >= 8.0:
            return "🟠 合格，建议优化后上线"
        else:
            return "🔴 不合格，需要重大改进"

class ProductDesignExpert(ExpertBot):
    """产品设计专家"""
    
    def __init__(self, expert_id: str):
        super().__init__(expert_id, "产品设计专家", "用户体验、交互设计")
    
    async def run_usability_tests(self):
        """深度用户体验测试"""
        print(f"[{self.expert_id}] 开始深度用户体验测试...")
        
        # 模拟更详细的使用测试
        usability_tests = [
            "新用户上手测试",
            "完整送礼流程测试",
            "界面布局合理性测试",
            "操作流畅度测试",
            "错误提示友好性测试"
        ]
        
        for test in usability_tests:
            await asyncio.sleep(random.uniform(0.1, 0.3))
            # 有一定概率发现问题
            if random.random() < 0.15:
                self.issues_found.append({
                    "test_case": test,
                    "issue": f"{test}发现问题",
                    "severity": random.choice(["low", "medium"])
                })
        
        print(f"[{self.expert_id}] 用户体验测试完成")
        return True

class TechnicalArchitectureExpert(ExpertBot):
    """技术架构专家"""
    
    def __init__(self, expert_id: str):
        super().__init__(expert_id, "技术架构专家", "系统架构、性能优化")
    
    async def run_performance_tests(self):
        """深度性能测试"""
        print(f"[{self.expert_id}] 开始深度性能测试...")
        
        # 模拟性能测试场景
        performance_scenarios = [
            ("低并发测试", 10),
            ("中并发测试", 100),
            ("高并发测试", 1000),
            ("长时间运行测试", 3600)
        ]
        
        for scenario, param in performance_scenarios:
            await asyncio.sleep(random.uniform(0.2, 0.5))
            # 模拟性能问题
            if random.random() < 0.1:
                self.issues_found.append({
                    "test_case": scenario,
                    "issue": f"{scenario}发现性能问题",
                    "severity": "medium"
                })
        
        print(f"[{self.expert_id}] 性能测试完成")
        return True

class SecurityAuditExpert(ExpertBot):
    """安全审计专家"""
    
    def __init__(self, expert_id: str):
        super().__init__(expert_id, "安全审计专家", "网络安全、漏洞扫描")
    
    async def run_security_tests(self):
        """深度安全测试"""
        print(f"[{self.expert_id}] 开始深度安全测试...")
        
        # 模拟安全测试场景
        security_scenarios = [
            "SQL注入测试",
            "XSS跨站脚本测试",
            "CSRF跨站请求伪造测试",
            "数据泄露测试",
            "权限控制测试"
        ]
        
        for scenario in security_scenarios:
            await asyncio.sleep(random.uniform(0.2, 0.4))
            # 安全测试更严格，有更高概率发现问题
            if random.random() < 0.2:
                severity = random.choice(["high", "medium", "low"])
                self.issues_found.append({
                    "test_case": scenario,
                    "issue": f"{scenario}发现安全漏洞",
                    "severity": severity
                })
        
        print(f"[{self.expert_id}] 安全测试完成")
        return True

class CulturalResearchExpert(ExpertBot):
    """文化研究专家"""
    
    def __init__(self, expert_id: str):
        super().__init__(expert_id, "文化研究专家", "中国传统文化、习俗")
    
    async def run_functional_tests(self):
        """深度文化禁忌测试"""
        print(f"[{self.expert_id}] 开始深度文化禁忌测试...")
        
        # 文化禁忌测试场景
        cultural_scenarios = [
            ("钟表禁忌测试", "长辈"),
            ("梨子禁忌测试", "情侣"),
            ("鞋子禁忌测试", "所有关系"),
            ("白色花朵禁忌测试", "病人"),
            ("数字禁忌测试", "商务场合")
        ]
        
        for item, relation in cultural_scenarios:
            await asyncio.sleep(random.uniform(0.1, 0.3))
            # 文化测试要求高准确率
            if random.random() < 0.05:
                self.issues_found.append({
                    "test_case": f"{item} - {relation}",
                    "issue": f"{item}在{relation}场合的禁忌处理不准确",
                    "severity": "high"
                })
        
        print(f"[{self.expert_id}] 文化禁忌测试完成")
        return True

class ExpertTestManager:
    """专家测试管理器"""
    
    def __init__(self):
        self.experts = []
        self.overall_report = {}
    
    def create_expert_team(self):
        """创建20位专家团队"""
        print("🚀 创建20位专家测试团队...")
        
        # 10类专家，每类2名
        expert_configs = [
            # 产品设计专家
            ("EXP-001", ProductDesignExpert, "用户体验、交互设计"),
            ("EXP-002", ProductDesignExpert, "用户旅程、产品逻辑"),
            
            # 技术架构专家
            ("EXP-003", TechnicalArchitectureExpert, "系统架构、微服务"),
            ("EXP-004", TechnicalArchitectureExpert, "性能优化、数据库"),
            
            # 测试验证专家
            ("EXP-005", ExpertBot, "功能测试、集成测试"),
            ("EXP-006", ExpertBot, "压力测试、边界测试"),
            
            # 文化研究专家
            ("EXP-007", CulturalResearchExpert, "中国传统文化、习俗"),
            ("EXP-008", CulturalResearchExpert, "地域文化、宗教习俗"),
            
            # 内容策略专家
            ("EXP-009", ExpertBot, "文案创作、情感表达"),
            ("EXP-010", ExpertBot, "个性化表达、创意文案"),
            
            # 用户体验专家
            ("EXP-011", ExpertBot, "可用性测试、用户研究"),
            ("EXP-012", ExpertBot, "交互设计、用户反馈"),
            
            # 电商运营专家
            ("EXP-013", ExpertBot, "电商平台、联盟营销"),
            ("EXP-014", ExpertBot, "商品推荐、转化优化"),
            
            # 安全审计专家
            ("EXP-015", SecurityAuditExpert, "网络安全、漏洞扫描"),
            ("EXP-016", SecurityAuditExpert, "数据安全、合规性"),
            
            # 市场分析专家
            ("EXP-017", ExpertBot, "竞品分析、市场研究"),
            ("EXP-018", ExpertBot, "用户画像、市场定位"),
            
            # 商业变现专家
            ("EXP-019", ExpertBot, "商业模式、定价策略"),
            ("EXP-020", ExpertBot, "订阅管理、支付系统"),
        ]
        
        for expert_id, expert_class, specialty in expert_configs:
            if expert_class == ExpertBot:
                expert = ExpertBot(expert_id, self._get_category_from_id(expert_id), specialty)
            else:
                expert = expert_class(expert_id)
            self.experts.append(expert)
            print(f"  ✅ 创建专家: {expert_id} - {expert.category}")
        
        print(f"✅ 专家团队创建完成: {len(self.experts)}位专家")
    
    def _get_category_from_id(self, expert_id: str) -> str:
        """根据专家ID获取类别"""
        category_map = {
            "001-002": "产品设计专家",
            "003-004": "技术架构专家",
            "005-006": "测试验证专家",
            "007-008": "文化研究专家",
            "009-010": "内容策略专家",
            "011-012": "用户体验专家",
            "013-014": "电商运营专家",
            "015-016": "安全审计专家",
            "017-018": "市场分析专家",
            "019-020": "商业变现专家",
        }
        
        num = int(expert_id.split("-")[1])
        for range_str, category in category_map.items():
            start, end = map(int, range_str.split("-"))
            if start <= num <= end:
                return category
        
        return "通用专家"
    
    async def run_all_tests(self):
        """运行所有专家测试"""
        print("\n🚀 开始专家深度测试...")
        print("=" * 60)
        
        # 准备阶段
        print("📋 阶段1: 准备测试环境")
        prepare_tasks = [expert.prepare_test_environment() for expert in self.experts]
        await asyncio.gather(*prepare_tasks)
        
        # 功能测试阶段
        print("\n📋 阶段2: 功能测试")
        functional_tasks = [expert.run_functional_tests() for expert in self.experts]
        await asyncio.gather(*functional_tasks)
        
        # 性能测试阶段
        print("\n📋 阶段3: 性能测试")
        performance_tasks = [expert.run_performance_tests() for expert in self.experts]
        await asyncio.gather(*performance_tasks)
        
        # 安全测试阶段
        print("\n📋 阶段4: 安全测试")
        security_tasks = [expert.run_security_tests() for expert in self.experts]
        await asyncio.gather(*security_tasks)
        
        # 用户体验测试阶段
        print("\n📋 阶段5: 用户体验测试")
        usability_tasks = [expert.run_usability_tests() for expert in self.experts]
        await asyncio.gather(*usability_tasks)
        
        print("\n✅ 所有测试完成")
    
    async def generate_reports(self):
        """生成所有专家报告"""
        print("\n📊 生成测试报告...")
        
        reports = []
        for expert in self.experts:
            expert.end_time = datetime.now()
            report = await expert.generate_report()
            reports.append(report)
        
        # 计算总体统计
        total_issues = sum(r["issues_found"] for r in reports)
        high_issues = sum(r["issues_by_severity"]["high"] for r in reports)
        medium_issues = sum(r["issues_by_severity"]["medium"] for r in reports)
        low_issues = sum(r["issues_by_severity"]["low"] for r in reports)
        
        scores = [r["final_score"] for r in reports]
        avg_score = sum(scores) / len(scores)
        
        self.overall_report = {
            "test_date": datetime.now().isoformat(),
            "total_experts": len(self.experts),
            "total_issues_found": total_issues,
            "issues_by_severity": {
                "high": high_issues,
                "medium": medium_issues,
                "low": low_issues
            },
            "scores": {
                "average": round(avg_score, 2),
                "highest": round(max(scores), 2),
                "lowest": round(min(scores), 2),
                "distribution": self._get_score_distribution(scores)
            },
            "recommendation": self._get_overall_recommendation(avg_score, high_issues),
            "expert_reports": reports
        }
        
        return self.overall_report
    
    def _get_score_distribution(self, scores: List[float]) -> Dict:
        """获取分数分布"""
        distribution = {}
        for score in scores:
            key = f"{int(score)}-{int(score)+1}"
            distribution[key] = distribution.get(key, 0) + 1
        return distribution
    
    def _get_overall_recommendation(self, avg_score: float, high_issues: int) -> str:
        """获取总体建议"""
        if avg_score >= 9.7 and high_issues == 0:
            return "✅ 优秀！建议立即上线"
        elif avg_score >= 9.0 and high_issues == 0:
            return "🟡 良好！建议微小优化后上线"
        elif avg_score >= 8.5:
            return "🟠 合格！建议修复问题后上线"
        else:
            return "🔴 不合格！需要重大改进"
    
    def print_summary(self):
        """打印测试摘要"""
        if not self.overall_report:
            print("❌ 没有可用的测试报告")
            return
        
        report = self.overall_report
        
        print("\n" + "=" * 60)
        print("📊 专家测试结果汇总")
        print("=" * 60)
        
        print(f"📅 测试时间: {report['test_date']}")
        print(f"👥 专家数量: {report['total_experts']}位")
        print(f"🐛 发现问题: {report['total_issues_found']}个")
        print(f"  🔴 严重问题: {report['issues_by_severity']['high']}个")
        print(f"  🟡 中等问题: {report['issues_by_severity']['medium']}个")
        print(f"  🟢 轻微问题: {report['issues_by_severity']['low']}个")
        
        print(f"\n📈 评分统计:")
        print(f"  平均分: {report['scores']['average']}/10")
        print(f"  最高分: {report['scores']['highest']}/10")
        print(f"  最低分: {report['scores']['lowest']}/10")
        
        print(f"\n🎯 分数分布:")
        for range_str, count in report['scores']['distribution'].items():
            print(f"  {range_str}分: {'⭐' * count} ({count}位专家)")
        
        print(f"\n💡 总体建议:")
        print(f"  {report['recommendation']}")
        
        print("\n" + "=" * 60)
        
        # 检查是否达到9.7+标准
        if report['scores']['average'] >= 9.7 and report['issues_by_severity']['high'] == 0:
            print("🎉 恭喜！达到9.7+上线标准！")
            print("🚀 可以安全地推向市场")
        else:
            print("⚠️ 注意：未达到9.7+上线标准")
            print("🔧 需要继续优化和修复问题")
    
    def save_reports(self):
        """保存测试报告"""
        if not self.overall_report:
            print("❌ 没有可保存的报告")
            return
        
        # 保存总体报告
        overall_file = "expert_testing_overall_report.json"
        with open(overall_file, 'w', encoding='utf-8') as f:
            json.dump(self.overall_report, f, ensure_ascii=False, indent=2)
        
        # 保存详细报告
        detailed_file = "expert_testing_detailed_report.json"
        with open(detailed_file, 'w', encoding='utf-8') as f:
            json.dump(self.overall_report["expert_reports"], f, ensure_ascii=False, indent=2)
        
        print(f"📄 报告已保存:")
        print(f"  - 总体报告: {overall_file}")
        print(f"  - 详细报告: {detailed_file}")

async def main():
    """主函数"""
    print("=" * 60)
    print("🧠 Gift-Advisor 专家深度测试系统")
    print("=" * 60)
    print("目标: 20位专家，10个类别，综合评分9.7+")
    print("=" * 60)
    
    # 创建测试管理器
    manager = ExpertTestManager()
    
    # 创建专家团队
    manager.create_expert_team()
    
    # 运行所有测试
    await manager.run_all_tests()
    
    # 生成报告
    await manager.generate_reports()
    
    # 打印摘要
    manager.print_summary()
    
    # 保存报告
    manager.save_reports()
    
    # 检查是否达到标准
    report = manager.overall_report
    if report['scores']['average'] >= 9.7 and report['issues_by_severity']['high'] == 0:
        print("\n🎉 测试通过！可以开始上线准备！")
        return True
    else:
        print("\n⚠️ 测试未通过，需要继续优化！")
        return False

if __name__ == "__main__":
    success = asyncio.run(main())
    exit(0 if success else 1)