demo.py

#!/usr/bin/env python3
"""
EduAdmin 主观题自动化评估系统演示脚本
"""

import requests
import json
import time
import sys

# 配置
EVAL_SERVICE_URL = "http://localhost:8000"
GO_BACKEND_URL = "http://localhost:8080"

def print_header(title):
    """打印标题"""
    print("\n" + "="*60)
    print(f" {title}")
    print("="*60)

def print_step(step, description):
    """打印步骤"""
    print(f"\n步骤 {step}: {description}")
    print("-" * 40)

def check_service_health():
    """检查服务健康状态"""
    print_step(1, "检查服务健康状态")
    
    services = [
        ("eval-service", f"{EVAL_SERVICE_URL}/health"),
        ("go-backend", f"{GO_BACKEND_URL}/health")
    ]
    
    for name, url in services:
        try:
            response = requests.get(url, timeout=5)
            if response.status_code == 200:
                print(f"✅ {name}: 运行正常")
            else:
                print(f"❌ {name}: 状态异常 ({response.status_code})")
                return False
        except requests.exceptions.RequestException as e:
            print(f"❌ {name}: 连接失败 - {e}")
            return False
    
    return True

def demo_evaluation():
    """演示主观题评估"""
    print_step(2, "主观题评估演示")
    
    # 测试题目
    test_cases = [
        {
            "question_id": "demo_001",
            "reference_answer": "人工智能是计算机科学的一个分支，它企图了解智能的实质，并生产出一种新的能以人类智能相似的方式做出反应的智能机器。",
            "student_answer": "人工智能是让机器像人一样思考的技术，包括机器学习和深度学习等方法。",
            "language": "zh"
        },
        {
            "question_id": "demo_002", 
            "reference_answer": "机器学习是人工智能的核心技术之一，通过算法让计算机从数据中学习模式。",
            "student_answer": "机器学习就是让计算机自己学习，不需要人工编程。",
            "language": "zh"
        },
        {
            "question_id": "demo_003",
            "reference_answer": "深度学习是机器学习的一个子领域，使用多层神经网络进行学习。",
            "student_answer": "深度学习使用神经网络，是机器学习的重要分支，可以处理复杂的模式识别任务。",
            "language": "zh"
        }
    ]
    
    for i, test_case in enumerate(test_cases, 1):
        print(f"\n测试案例 {i}:")
        print(f"题目ID: {test_case['question_id']}")
        print(f"参考答案: {test_case['reference_answer'][:50]}...")
        print(f"学生答案: {test_case['student_answer'][:50]}...")
        
        try:
            response = requests.post(
                f"{EVAL_SERVICE_URL}/api/v1/evaluate",
                json=test_case,
                timeout=30
            )
            
            if response.status_code == 200:
                result = response.json()
                print(f"✅ 评估成功")
                print(f"   最终得分: {result['final_score']:.1f}")
                print(f"   语义得分: {result['semantic_score']:.3f}")
                print(f"   关键词得分: {result['keyterm_score']:.1f}")
                print(f"   连贯性得分: {result['coherence_score']:.3f}")
                print(f"   核心关键词: {', '.join(result['hit_keywords']['core'])}")
                print(f"   评估总结: {result['explanations']['summary']}")
            else:
                print(f"❌ 评估失败: {response.status_code}")
                print(f"   错误信息: {response.text}")
                
        except requests.exceptions.RequestException as e:
            print(f"❌ 请求失败: {e}")

def demo_feedback():
    """演示反馈机制"""
    print_step(3, "反馈机制演示")
    
    feedback_data = {
        "question_id": "demo_001",
        "user_id": "demo_user_001",
        "human_score": 88.0,
        "system_score": 85.5
    }
    
    print(f"提交反馈:")
    print(f"  题目ID: {feedback_data['question_id']}")
    print(f"  用户ID: {feedback_data['user_id']}")
    print(f"  人工评分: {feedback_data['human_score']}")
    print(f"  系统评分: {feedback_data['system_score']}")
    
    try:
        response = requests.post(
            f"{EVAL_SERVICE_URL}/api/v1/feedback",
            json=feedback_data,
            timeout=10
        )
        
        if response.status_code == 200:
            result = response.json()
            print(f"✅ 反馈提交成功")
            print(f"   消息: {result['message']}")
            if 'updated_weights' in result:
                print(f"   更新权重: {result['updated_weights']}")
        else:
            print(f"❌ 反馈提交失败: {response.status_code}")
            print(f"   错误信息: {response.text}")
            
    except requests.exceptions.RequestException as e:
        print(f"❌ 请求失败: {e}")

def demo_go_backend():
    """演示Go后端API"""
    print_step(4, "Go后端API演示")
    
    # 测试考试提交
    exam_data = {
        "paper_id": "demo_paper_001",
        "user_id": "demo_user_001",
        "answers": {
            "question_1": "A",
            "question_2": "B"
        },
        "subjective_questions": {
            "subjective_1": "人工智能是计算机科学的一个分支，它研究如何让机器模拟人类智能。",
            "subjective_2": "机器学习是人工智能的核心技术，通过算法让计算机从数据中学习。"
        }
    }
    
    print(f"提交考试:")
    print(f"  试卷ID: {exam_data['paper_id']}")
    print(f"  用户ID: {exam_data['user_id']}")
    print(f"  主观题数量: {len(exam_data['subjective_questions'])}")
    
    try:
        response = requests.post(
            f"{GO_BACKEND_URL}/api/v1/exam/submit",
            json=exam_data,
            timeout=30
        )
        
        if response.status_code == 200:
            result = response.json()
            print(f"✅ 考试提交成功")
            print(f"   消息: {result['message']}")
        else:
            print(f"❌ 考试提交失败: {response.status_code}")
            print(f"   错误信息: {response.text}")
            
    except requests.exceptions.RequestException as e:
        print(f"❌ 请求失败: {e}")

def demo_teacher_api():
    """演示教师端API"""
    print_step(5, "教师端API演示")
    
    paper_id = "demo_paper_001"
    
    try:
        response = requests.get(
            f"{GO_BACKEND_URL}/api/v1/teacher/scores/{paper_id}",
            timeout=10
        )
        
        if response.status_code == 200:
            result = response.json()
            print(f"✅ 获取教师评分成功")
            print(f"   试卷ID: {result['paper_id']}")
            print(f"   评分记录数: {result['total']}")
            
            if result['scores']:
                score = result['scores'][0]
                print(f"   示例评分:")
                print(f"     学生: {score.get('student_name', 'N/A')}")
                print(f"     题目: {score['question_id']}")
                print(f"     得分: {score['final_score']}")
        else:
            print(f"❌ 获取教师评分失败: {response.status_code}")
            print(f"   错误信息: {response.text}")
            
    except requests.exceptions.RequestException as e:
        print(f"❌ 请求失败: {e}")

def demo_student_api():
    """演示学生端API"""
    print_step(6, "学生端API演示")
    
    paper_id = "demo_paper_001"
    user_id = "demo_user_001"
    
    try:
        response = requests.get(
            f"{GO_BACKEND_URL}/api/v1/student/scores/{paper_id}/{user_id}",
            timeout=10
        )
        
        if response.status_code == 200:
            result = response.json()
            print(f"✅ 获取学生评分成功")
            print(f"   试卷ID: {result['paper_id']}")
            print(f"   用户ID: {result['user_id']}")
            print(f"   评分记录数: {result['total']}")
            
            if result['scores']:
                score = result['scores'][0]
                print(f"   示例评分:")
                print(f"     题目: {score['question_id']}")
                print(f"     得分: {score['final_score']}")
                print(f"     总结: {score['explanations']['summary']}")
        else:
            print(f"❌ 获取学生评分失败: {response.status_code}")
            print(f"   错误信息: {response.text}")
            
    except requests.exceptions.RequestException as e:
        print(f"❌ 请求失败: {e}")

def main():
    """主函数"""
    print_header("EduAdmin 主观题自动化评估系统演示")
    
    print("本演示将展示系统的主要功能：")
    print("1. 服务健康检查")
    print("2. 主观题自动评估")
    print("3. 反馈机制")
    print("4. Go后端API")
    print("5. 教师端API")
    print("6. 学生端API")
    
    input("\n按回车键开始演示...")
    
    # 检查服务健康状态
    if not check_service_health():
        print("\n❌ 服务健康检查失败，请确保所有服务都已启动")
        print("   启动命令: ./run.sh 或 docker-compose up -d")
        sys.exit(1)
    
    # 演示各个功能
    demo_evaluation()
    demo_feedback()
    demo_go_backend()
    demo_teacher_api()
    demo_student_api()
    
    print_header("演示完成")
    print("🎉 所有功能演示完成！")
    print("\n📋 系统访问地址：")
    print(f"   - 前端管理端: http://localhost:8080")
    print(f"   - 前端学员端: http://localhost:8081")
    print(f"   - eval-service API: http://localhost:8000")
    print(f"   - API 文档: http://localhost:8000/docs")
    print("\n📖 更多信息请查看文档：")
    print("   - README.md - 项目概述")
    print("   - docs/API.md - API文档")
    print("   - docs/ALGORITHM.md - 算法详解")
    print("   - docs/DEPLOYMENT.md - 部署指南")

if __name__ == "__main__":
    main()