#!/usr/bin/env python3
"""
Scene Detection CLI - Refactored
场景检测命令行工具 - 重构版

使用重构后的场景检测模块，代码更简洁、模块化更好。
"""

import typer
from pathlib import Path
from typing import Optional, List
from rich.console import Console
from rich.table import Table

from python_core.scene_detection import (
    SceneDetector, 
    DetectorType, 
    OutputFormat
)
from python_core.utils.logger import logger

scene_detect = typer.Typer(help="场景检测工具 - 重构版")
console = Console()

@scene_detect.command("split")
def split(
    video_path: Path = typer.Argument(..., help="视频文件路径"),
    detector_type: DetectorType = typer.Option(DetectorType.CONTENT, "--detector", "-d", help="检测器类型"),
    threshold: float = typer.Option(30.0, "--threshold", "-t", help="检测阈值"),
    min_scene_length: float = typer.Option(1.0, "--min-length", "-m", help="最小场景长度(秒)"),
    output: Optional[Path] = typer.Option(None, "--output", "-o", help="输出文件路径"),
    output_format: OutputFormat = typer.Option(OutputFormat.JSON, "--format", "-f", help="输出格式"),
    ai_analysis: bool = typer.Option(True, "--ai/--no-ai", help="启用/禁用AI分析"),
    verbose: bool = typer.Option(False, "--verbose", "-v", help="详细输出")
):
    """使用LangGraph工作流进行场景检测"""
    console.print(f"🔄 使用工作流检测视频: [bold blue]{video_path}[/bold blue]")
    
    try:
        # 创建检测器
        detector = SceneDetector()
        
        # 执行工作流检测
        result = detector.detect_with_workflow(
            video_path, detector_type, threshold, min_scene_length,
            output, output_format, ai_analysis
        )
        
        # 显示结果
        if result.get("workflow_state") == "completed":
            detection_result = result.get("detection_result")
            if detection_result and detection_result.success:
                console.print(f"✅ 工作流完成: [bold green]{detection_result.total_scenes}[/bold green] 个场景")
                console.print(f"📊 检测时间: {detection_result.detection_time:.2f}秒")
                
                # 显示AI分析结果
                ai_analysis_result = result.get("ai_analysis")
                if ai_analysis_result and ai_analysis_result != "AI分析已禁用":
                    console.print("\n🧠 AI分析结果:")
                    console.print(ai_analysis_result[:500] + "..." if len(ai_analysis_result) > 500 else ai_analysis_result)
                
                # 显示场景列表
                if verbose:
                    _display_scenes_table(detection_result.scenes)
            else:
                console.print(f"❌ 检测失败: [bold red]{detection_result.error if detection_result else '未知错误'}[/bold red]")
                raise typer.Exit(1)
        else:
            errors = result.get("errors", [])
            error_msg = "; ".join(errors) if errors else "工作流执行失败"
            console.print(f"❌ 工作流失败: [bold red]{error_msg}[/bold red]")
            raise typer.Exit(1)
            
    except Exception as e:
        console.print(f"❌ 执行失败: [bold red]{str(e)}[/bold red]")
        raise typer.Exit(1)


def _display_scenes_table(scenes):
    """显示场景表格"""
    table = Table(title="检测到的场景")
    table.add_column("场景", style="cyan")
    table.add_column("开始时间", style="green")
    table.add_column("结束时间", style="green")
    table.add_column("时长", style="yellow")

    for scene in scenes:
        table.add_row(
            str(scene.index + 1),
            f"{scene.start_time:.2f}s",
            f"{scene.end_time:.2f}s",
            f"{scene.duration:.2f}s"
        )

    console.print(table)


if __name__ == "__main__":
    scene_detect()