"""
测试情绪分析模型效果
对比规则引擎、官方模型和微调模型
"""
import sys
import os
from pathlib import Path

# 添加项目路径
sys.path.insert(0, str(Path(__file__).parent.parent))

# 直接导入模块
from models.sentiment import SentimentAnalyzer


def test_sentiment_analyzer():
    """测试情绪分析器"""

    # HR场景测试用例
    test_cases = [
        # 积极情绪
        ("好的，谢谢你的帮助，非常满意！", "positive"),
        ("没问题，我理解公司的规定", "positive"),
        ("太好了，感谢你的解答", "positive"),
        ("流程很规范，我很认可", "positive"),
        ("专业高效的回复，感谢支持", "positive"),

        # 中性情绪
        ("您好，请问有什么可以帮您？", "neutral"),
        ("请问申请年假需要什么材料", "neutral"),
        ("我需要了解培训的具体时间", "neutral"),
        ("好的，我知道了", "neutral"),
        ("请问还有什么需要补充的吗", "neutral"),

        # 消极情绪
        ("我对这个处理结果很不满意", "negative"),
        ("这个制度太不合理了，我很生气", "negative"),
        ("为什么要强制执行这个规定", "negative"),
        ("你们的做法让我很失望", "negative"),
        ("我要投诉这个处理方式", "negative"),
    ]

    print("=" * 80)
    print("情绪分析模型测试")
    print("=" * 80)

    # 测试规则引擎（强制不加载模型）
    print("\n【方法1: 规则引擎】")
    print("-" * 80)
    analyzer_rule = SentimentAnalyzer(model_path=None)
    correct_rule = 0
    for text, expected in test_cases:
        result = analyzer_rule.analyze_turn(text)
        predicted = result['emotion']
        confidence = result.get('confidence', 0)
        method = result.get('method', 'unknown')
        status = "✓" if predicted == expected else "✗"
        if predicted == expected:
            correct_rule += 1
        print(f"{status} {text[:35]:35} -> {predicted:8} (期望: {expected:8}) 置信度: {confidence:.2f} 方法: {method}")

    accuracy_rule = correct_rule / len(test_cases) * 100
    print(f"\n准确率: {correct_rule}/{len(test_cases)} = {accuracy_rule:.1f}%")

    # 测试微调模型（如果存在）
    finetuned_model_paths = [
        "./models/sentiment-hr/final_model",
        "./models/sentiment-hr/checkpoint-epoch-3",
        "./models/sentiment-hr/checkpoint-epoch-2",
        "./models/sentiment-hr/checkpoint-epoch-1",
    ]

    model_loaded = False
    for model_path in finetuned_model_paths:
        full_path = Path(__file__).parent.parent / model_path
        if full_path.exists():
            print(f"\n【方法2: 微调模型】({model_path})")
            print("-" * 80)
            try:
                analyzer_finetuned = SentimentAnalyzer(model_path=str(full_path))
                if analyzer_finetuned.use_model:
                    correct_finetuned = 0
                    for text, expected in test_cases:
                        result = analyzer_finetuned.analyze_turn(text)
                        predicted = result['emotion']
                        confidence = result.get('confidence', 0)
                        method = result.get('method', 'unknown')
                        status = "✓" if predicted == expected else "✗"
                        if predicted == expected:
                            correct_finetuned += 1
                        print(f"{status} {text[:35]:35} -> {predicted:8} (期望: {expected:8}) 置信度: {confidence:.2f} 方法: {method}")

                    accuracy_finetuned = correct_finetuned / len(test_cases) * 100
                    print(f"\n准确率: {correct_finetuned}/{len(test_cases)} = {accuracy_finetuned:.1f}%")

                    # 对比提升
                    improvement = accuracy_finetuned - accuracy_rule
                    print(f"\n相比规则引擎提升: {improvement:+.1f}%")
                    model_loaded = True
                    break
            except Exception as e:
                print(f"加载微调模型失败: {e}")

    if not model_loaded:
        print("\n【方法2: 微调模型】未找到微调模型")
        print("提示: 运行以下命令训练模型")
        print("  python scripts/prepare_sentiment_data.py")
        print("  python scripts/train_sentiment.py --train_data ./data/processed/sentiment/train.json")

    # 总结
    print("\n" + "=" * 80)
    print("总结")
    print("=" * 80)
    print(f"规则引擎准确率: {accuracy_rule:.1f}%")
    if model_loaded:
        print(f"微调模型准确率: {accuracy_finetuned:.1f}%")
        print(f"性能提升: {improvement:+.1f}%")
    print("=" * 80)


def interactive_test():
    """交互式测试"""
    print("\n" + "=" * 80)
    print("交互式情绪分析测试")
    print("=" * 80)
    print("输入文本进行分析，输入 'quit' 退出")

    # 尝试加载微调模型
    model_path = "./models/sentiment-hr/final_model"
    full_path = Path(__file__).parent.parent / model_path

    if full_path.exists():
        print(f"使用微调模型: {model_path}")
        analyzer = SentimentAnalyzer(model_path=str(full_path))
    else:
        print("未找到微调模型，使用规则引擎")
        analyzer = SentimentAnalyzer(model_path=None)

    while True:
        text = input("\n请输入文本: ").strip()
        if text.lower() in ['quit', 'exit', 'q']:
            break

        if not text:
            continue

        result = analyzer.analyze_turn(text)
        print(f"\n分析结果:")
        print(f"  情绪: {result['emotion']}")
        print(f"  置信度: {result.get('confidence', 0):.4f}")
        print(f"  方法: {result.get('method', 'unknown')}")

        if 'probabilities' in result:
            print(f"  概率分布:")
            for label, prob in result['probabilities'].items():
                print(f"    {label}: {prob:.4f}")


if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser(description="测试情绪分析模型")
    parser.add_argument("--interactive", "-i", action="store_true",
                        help="启用交互式测试模式")
    args = parser.parse_args()

    if args.interactive:
        interactive_test()
    else:
        test_sentiment_analyzer()