"""
简化的预测CLI工具
Simplified CLI Tool for emotion and physiological state prediction

该工具提供了简化的命令行界面，支持：
- 交互式输入
- 批量文件处理
- 清晰的输出格式和解释
- 快速预测模式
"""

import argparse
import sys
import os
import json
import csv
import logging
from pathlib import Path
from typing import List, Dict, Any, Optional
import numpy as np

# 添加项目根目录到Python路径
project_root = Path(__file__).parent.parent.parent
sys.path.insert(0, str(project_root))

from src.utils.inference_engine import create_inference_engine
from src.utils.logger import setup_logger


class PredictCLI:
    """简化的预测CLI类"""
    
    def __init__(self, model_path: str, preprocessor_path: Optional[str] = None):
        """
        初始化预测CLI
        
        Args:
            model_path: 模型文件路径
            preprocessor_path: 预处理器文件路径
        """
        self.logger = logging.getLogger(__name__)
        
        try:
            # 创建推理引擎
            self.engine = create_inference_engine(
                model_path=model_path,
                preprocessor_path=preprocessor_path,
                device='auto'
            )
            
            # 获取模型信息
            self.model_info = self.engine.get_model_info()
            self.logger.info("预测CLI初始化成功")
            
        except Exception as e:
            self.logger.error(f"预测CLI初始化失败: {e}")
            raise
    
    def interactive_mode(self):
        """交互式预测模式"""
        print("\n" + "="*60)
        print("情绪与生理状态变化预测工具 - 交互式模式")
        print("="*60)
        print("\n请输入以下7个参数:")
        print("1. 用户快乐度 (User Pleasure): [-1.0, 1.0]")
        print("2. 用户激活度 (User Arousal): [-1.0, 1.0]")
        print("3. 用户支配度 (User Dominance): [-1.0, 1.0]")
        print("4. 活力值 (Vitality): [0.0, 100.0]")
        print("5. 当前快乐度 (Current Pleasure): [-1.0, 1.0]")
        print("6. 当前激活度 (Current Arousal): [-1.0, 1.0]")
        print("7. 当前支配度 (Current Dominance): [-1.0, 1.0]")
        print("\n输入 'quit' 退出交互模式")
        print("-"*60)
        
        while True:
            try:
                print("\n请输入7个数值 (用空格分隔):")
                user_input = input("> ").strip()
                
                if user_input.lower() in ['quit', 'exit', 'q']:
                    print("退出交互模式")
                    break
                
                # 解析输入
                values = user_input.split()
                if len(values) != 7:
                    print(f"错误: 需要输入7个数值，但得到{len(values)}个")
                    continue
                
                try:
                    input_data = np.array([float(v) for v in values], dtype=np.float32)
                except ValueError:
                    print("错误: 输入必须是数字")
                    continue
                
                # 验证输入范围
                if not self._validate_input_ranges(input_data):
                    continue
                
                # 执行预测
                result = self.predict_single(input_data)
                
                # 显示结果
                self._display_result(result, input_data)
                
            except KeyboardInterrupt:
                print("\n\n用户中断，退出交互模式")
                break
            except Exception as e:
                print(f"预测出错: {e}")
    
    def _validate_input_ranges(self, input_data: np.ndarray) -> bool:
        """验证输入数据范围"""
        user_pad = input_data[:3]
        vitality = input_data[3]
        current_pad = input_data[4:]
        
        # 检查PAD值范围
        if np.any(np.abs(user_pad) > 1.5):
            print("警告: 用户PAD值超出正常范围 [-1.0, 1.0]")
            response = input("是否继续? (y/n): ").strip().lower()
            if response != 'y':
                return False
        
        if np.any(np.abs(current_pad) > 1.5):
            print("警告: 当前PAD值超出正常范围 [-1.0, 1.0]")
            response = input("是否继续? (y/n): ").strip().lower()
            if response != 'y':
                return False
        
        # 检查活力值范围
        if not (0 <= vitality <= 150):
            print("警告: 活力值超出正常范围 [0.0, 100.0]")
            response = input("是否继续? (y/n): ").strip().lower()
            if response != 'y':
                return False
        
        return True
    
    def predict_single(self, input_data: np.ndarray):
        """预测单个样本"""
        try:
            result = self.engine.predict(input_data)
            return result
        except Exception as e:
            raise RuntimeError(f"预测失败: {e}")
    
    def _display_result(self, result, input_data: np.ndarray):
        """显示预测结果"""
        print("\n" + "="*50)
        print("预测结果")
        print("="*50)
        
        # 显示输入信息
        print(f"\n输入信息:")
        print(f"  用户PAD: 快乐度={input_data[0]:+.3f}, 激活度={input_data[1]:+.3f}, 支配度={input_data[2]:+.3f}")
        print(f"  活力值: {input_data[3]:.1f}")
        print(f"  当前PAD: 快乐度={input_data[4]:+.3f}, 激活度={input_data[5]:+.3f}, 支配度={input_data[6]:+.3f}")
        
        # 显示预测结果
        delta_pad = result.delta_pad[0]
        delta_pressure = result.delta_pressure[0]
        confidence = result.confidence[0]
        
        print(f"\n预测变化:")
        print(f"  情绪变化 (ΔPAD):")
        print(f"    快乐度变化: {delta_pad:+.6f} {'↗' if delta_pad > 0 else '↘' if delta_pad < 0 else '→'}")
        print(f"    激活度变化: {delta_pad:+.6f} {'↗' if delta_pad > 0 else '↘' if delta_pad < 0 else '→'}")
        print(f"    支配度变化: {delta_pad:+.6f} {'↗' if delta_pad > 0 else '↘' if delta_pad < 0 else '→'}")
        print(f"  压力变化: {delta_pressure:+.6f} {'↗' if delta_pressure > 0 else '↘' if delta_pressure < 0 else '→'}")
        print(f"  预测置信度: {confidence:.6f} ({confidence*100:.1f}%)")
        
        # 提供解释
        self._provide_interpretation(delta_pad, delta_pressure, confidence)
        
        # 显示性能信息
        print(f"\n性能信息:")
        print(f"  推理时间: {result.inference_time*1000:.2f}ms")
        
        print("="*50)
    
    def _provide_interpretation(self, delta_pad: np.ndarray, delta_pressure: float, confidence: float):
        """提供预测结果解释"""
        print(f"\n结果解释:")
        
        # 情绪变化解释
        pleasure_change = delta_pad[0]
        arousal_change = delta_pad[1]
        dominance_change = delta_pad[2]
        
        if abs(pleasure_change) > 0.1:
            if pleasure_change > 0:
                print("  • 情绪趋向积极愉快")
            else:
                print("  • 情绪趋向消极低落")
        
        if abs(arousal_change) > 0.1:
            if arousal_change > 0:
                print("  • 激活度提升，趋向兴奋")
            else:
                print("  • 激活度降低，趋向平静")
        
        if abs(dominance_change) > 0.1:
            if dominance_change > 0:
                print("  • 支配感增强，趋向自信")
            else:
                print("  • 支配感减弱，趋向顺从")
        
        # 压力变化解释
        if abs(delta_pressure) > 0.05:
            if delta_pressure > 0:
                print("  • 压力水平可能上升")
            else:
                print("  • 压力水平可能下降")
        
        # 置信度解释
        if confidence > 0.8:
            print("  • 预测置信度很高")
        elif confidence > 0.6:
            print("  • 预测置信度中等")
        else:
            print("  • 预测置信度较低，结果可能不太准确")
    
    def batch_predict(self, input_file: str, output_file: Optional[str] = None):
        """批量预测"""
        print(f"\n批量预测模式")
        print(f"输入文件: {input_file}")
        
        try:
            # 加载输入数据
            input_data = self._load_batch_input(input_file)
            print(f"加载了 {len(input_data)} 个样本")
            
            # 执行批量预测
            print("执行批量预测...")
            results = self.engine.predict_batch(input_data)
            
            # 处理结果
            processed_results = []
            for i, result in enumerate(results):
                processed_results.append({
                    'sample_id': i + 1,
                    'delta_pleasure': float(result.delta_pad[0]),
                    'delta_arousal': float(result.delta_pad[1]),
                    'delta_dominance': float(result.delta_pad[2]),
                    'delta_pressure': float(result.delta_pressure[0]),
                    'confidence': float(result.confidence[0]),
                    'inference_time': float(result.inference_time)
                })
            
            # 保存结果
            if output_file:
                self._save_batch_results(processed_results, output_file)
                print(f"结果已保存到: {output_file}")
            else:
                self._display_batch_summary(processed_results)
            
        except Exception as e:
            print(f"批量预测失败: {e}")
            raise
    
    def _load_batch_input(self, input_file: str) -> np.ndarray:
        """加载批量输入数据"""
        file_path = Path(input_file)
        
        if not file_path.exists():
            raise FileNotFoundError(f"输入文件不存在: {input_file}")
        
        if file_path.suffix.lower() == '.json':
            return self._load_json_batch(file_path)
        elif file_path.suffix.lower() == '.csv':
            return self._load_csv_batch(file_path)
        else:
            raise ValueError(f"不支持的文件格式: {file_path.suffix}")
    
    def _load_json_batch(self, file_path: Path) -> np.ndarray:
        """加载JSON格式的批量数据"""
        with open(file_path, 'r', encoding='utf-8') as f:
            data = json.load(f)
        
        if isinstance(data, list):
            return np.array(data, dtype=np.float32)
        elif isinstance(data, dict) and 'data' in data:
            return np.array(data['data'], dtype=np.float32)
        else:
            raise ValueError("JSON格式不正确，需要数据数组或包含'data'字段的对象")
    
    def _load_csv_batch(self, file_path: Path) -> np.ndarray:
        """加载CSV格式的批量数据"""
        import pandas as pd
        
        df = pd.read_csv(file_path)
        
        # 检查列数
        if len(df.columns) < 7:
            raise ValueError(f"CSV文件至少需要7列，但得到{len(df.columns)}列")
        
        # 使用前7列
        data = df.iloc[:, :7].values
        return data.astype(np.float32)
    
    def _save_batch_results(self, results: List[Dict[str, Any]], output_file: str):
        """保存批量结果"""
        file_path = Path(output_file)
        
        if file_path.suffix.lower() == '.json':
            with open(file_path, 'w', encoding='utf-8') as f:
                json.dump({
                    'results': results,
                    'summary': {
                        'total_samples': len(results),
                        'avg_confidence': np.mean([r['confidence'] for r in results]),
                        'avg_inference_time': np.mean([r['inference_time'] for r in results])
                    }
                }, f, indent=2, ensure_ascii=False)
        
        elif file_path.suffix.lower() == '.csv':
            with open(file_path, 'w', newline='', encoding='utf-8') as f:
                if results:
                    fieldnames = results[0].keys()
                    writer = csv.DictWriter(f, fieldnames=fieldnames)
                    writer.writeheader()
                    writer.writerows(results)
        
        else:
            raise ValueError(f"不支持的输出格式: {file_path.suffix}")
    
    def _display_batch_summary(self, results: List[Dict[str, Any]]):
        """显示批量预测摘要"""
        if not results:
            print("没有预测结果")
            return
        
        print(f"\n批量预测摘要:")
        print(f"总样本数: {len(results)}")
        
        # 统计信息
        confidences = [r['confidence'] for r in results]
        inference_times = [r['inference_time'] for r in results]
        
        print(f"平均置信度: {np.mean(confidences):.4f}")
        print(f"置信度范围: [{np.min(confidences):.4f}, {np.max(confidences):.4f}]")
        print(f"平均推理时间: {np.mean(inference_times)*1000:.2f}ms")
        print(f"总推理时间: {np.sum(inference_times):.4f}s")
        
        # 显示前几个结果
        print(f"\n前5个样本结果:")
        for i, result in enumerate(results[:5]):
            print(f"样本{result['sample_id']}: "
                  f"ΔPAD=[{result['delta_pleasure']:+.3f}, {result['delta_arousal']:+.3f}, {result['delta_dominance']:+.3f}], "
                  f"Δ压力={result['delta_pressure']:+.3f}, "
                  f"置信度={result['confidence']:.3f}")
    
    def quick_predict(self, values: List[float]):
        """快速预测模式"""
        if len(values) != 7:
            raise ValueError(f"需要7个输入值，但得到{len(values)}个")
        
        input_data = np.array(values, dtype=np.float32)
        
        try:
            result = self.predict_single(input_data)
            
            # 简洁输出
            delta_pad = result.delta_pad[0]
            delta_pressure = result.delta_pressure[0]
            confidence = result.confidence[0]
            
            print(f"ΔPAD: [{delta_pad[0]:+.4f}, {delta_pad[1]:+.4f}, {delta_pad[2]:+.4f}], "
                  f"Δ压力: {delta_pressure:+.4f}, "
                  f"置信度: {confidence:.4f}")
            
        except Exception as e:
            print(f"预测失败: {e}")
            return False
        
        return True


def main():
    """主函数"""
    parser = argparse.ArgumentParser(
        description="情绪与生理状态变化预测工具",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog="""
使用示例:
  # 交互式模式
  python predict.py --model model.pth
  
  # 快速预测
  python predict.py --model model.pth --quick 0.5 0.3 -0.2 80 0.1 0.4 -0.1
  
  # 批量预测
  python predict.py --model model.pth --batch input.json --output results.json
        """
    )
    
    # 必需参数
    parser.add_argument('--model', '-m', type=str, required=True,
                       help='模型文件路径 (.pth)')
    parser.add_argument('--preprocessor', '-p', type=str,
                       help='预处理器文件路径')
    
    # 模式选择
    mode_group = parser.add_mutually_exclusive_group()
    mode_group.add_argument('--interactive', '-i', action='store_true',
                           help='交互式模式')
    mode_group.add_argument('--quick', nargs=7, type=float, metavar='VALUE',
                           help='快速预测模式 (7个数值)')
    mode_group.add_argument('--batch', type=str, metavar='FILE',
                           help='批量预测模式 (输入文件)')
    
    # 输出选项
    parser.add_argument('--output', '-o', type=str,
                       help='输出文件路径 (批量模式)')
    parser.add_argument('--verbose', '-v', action='store_true',
                       help='详细输出')
    parser.add_argument('--log-level', type=str, 
                       choices=['DEBUG', 'INFO', 'WARNING', 'ERROR'],
                       default='WARNING', help='日志级别')
    
    args = parser.parse_args()
    
    # 设置日志
    setup_logger(level=args.log_level)
    
    try:
        # 创建预测CLI
        cli = PredictCLI(args.model, args.preprocessor)
        
        # 显示模型信息
        if args.verbose:
            print(f"模型信息:")
            print(f"  设备: {cli.model_info['device']}")
            print(f"  总参数量: {cli.model_info['total_parameters']:,}")
            print(f"  输入维度: {cli.model_info['input_dim']}")
            print(f"  输出维度: {cli.model_info['output_dim']}")
        
        # 根据模式执行
        if args.interactive or (not args.quick and not args.batch):
            # 默认进入交互模式
            cli.interactive_mode()
        
        elif args.quick:
            # 快速预测
            success = cli.quick_predict(args.quick)
            if not success:
                sys.exit(1)
        
        elif args.batch:
            # 批量预测
            cli.batch_predict(args.batch, args.output)
        
    except Exception as e:
        print(f"错误: {e}")
        if args.verbose:
            import traceback
            traceback.print_exc()
        sys.exit(1)


if __name__ == "__main__":
    main()