# Director Agent 核心逻辑（简化版）
class DirectorAgent:
    def __init__(self, gemini_client):
        self.gemini = gemini_client

    def run(self, brief: CreativeBrief, footage_index: FootageIndex) -> EditPlan:
        # 1. 解析创意简报
        product = brief.product
        audience = brief.audience
        tone = brief.tone
        duration = brief.duration_seconds

        # 2. 搜索相关素材
        # 实际实现会调用 Gemini 的多模态理解能力
        relevant_shots = self._search_footage(
            footage_index,
            criteria=f"{product} for {audience} with {tone} tone"
        )

        # 3. 生成剪辑计划
        # Gemini 会输出结构化的 EditPlan JSON
        edit_plan = self._generate_edit_plan(
            shots=relevant_shots,
            duration=duration,
            style=brief.style_ref
        )

        return edit_plan

    def _search_footage(self, index, criteria):
        # 实际实现：语义搜索 footage_index
        # 返回匹配的镜头列表
        pass

    def _generate_edit_plan(self, shots, duration, style):
        # 实际实现：调用 Gemini API
        # prompt 包含 shots 信息、duration、style 模板
        # 返回解析后的 EditPlan 对象
        pass

class EditPlan(BaseModel):
    shots: List[Shot]           # 镜头列表（有序）
    text_overlays: List[TextOverlay]  # 文字叠加
    transitions: List[str]       # 转场效果
    music_mood: str             # 音乐风格

class Shot(BaseModel):
    footage_id: str             # 素材 ID
    start_time: float           # 入点（秒）
    end_time: float             # 出点（秒）
    reason: str                 # 选择理由（用于调试）

# Trim Refiner Agent 核心逻辑（简化版）
class TrimRefinerAgent:
    def run(self, edit_plan: EditPlan) -> EditPlan:
        refined_plan = EditPlan()

        for shot in edit_plan.shots:
            # 精细化裁剪边界
            # 实际实现会分析视频帧，找到最佳切点
            new_start = self._find_best_cut_point(
                shot.start_time,
                mode='forward'  # 向后找到最佳切点
            )
            new_end = self._find_best_cut_point(
                shot.end_time,
                mode='backward'  # 向前找到最佳切点
            )

            refined_plan.shots.append(Shot(
                footage_id=shot.footage_id,
                start_time=new_start,
                end_time=new_end,
                reason=f"Refined: {shot.reason}"
            ))

        return refined_plan

    def _find_best_cut_point(self, time, mode):
        # 实际实现：
        # 1. 如果是 forward 模式，找到下一个动作完成帧
        # 2. 如果是 backward 模式，找到上一个动作完成帧
        # 3. 返回优化后的时间点
        # 这通常需要计算机视觉模型或 FFmpeg 场景检测
        pass

# Editor Agent 核心逻辑（简化版）
class EditorAgent:
    def __init__(self, ffmpeg_path="/usr/bin/ffmpeg"):
        self.ffmpeg = ffmpeg_path

    def run(self, edit_plan: EditPlan, footage_dir: Path) -> Path:
        output_path = Path("output") / f"{edit_plan.name}_v{version}.mp4"

        # 构建 FFmpeg 命令
        # 实际实现会：
        # 1. 为每个镜头生成 FFmpeg filter 复杂滤镜图
        # 2. 处理转场效果（需要额外滤镜）
        # 3. 叠加文字（drawtext 滤镜）
        # 4. 混音（amix 滤镜）
        ffmpeg_cmd = self._build_ffmpeg_cmd(edit_plan, footage_dir, output_path)

        # 执行渲染
        subprocess.run(ffmpeg_cmd, check=True, capture_output=True)

        return output_path

    def _build_ffmpeg_cmd(self, plan, footage_dir, output):
        # 实际 FFmpeg 命令构建非常复杂
        # 涉及 concat demuxer、filter complex、多个输入流
        # 这里只展示概念结构
        pass

# Reviewer Agent 核心逻辑（简化版）
class ReviewerAgent:
    def __init__(self, gemini_client):
        self.gemini = gemini_client

    def run(self, video_path: Path, brief: CreativeBrief) -> ReviewScore:
        # 实际实现：
        # 1. 如果 Gemini 支持视频输入，直接上传视频评估
        # 2. 否则提取关键帧，让 Gemini 评估
        # 3. 解析 Gemini 返回的 JSON 评分
        scores = self._evaluate_video(video_path, brief)
        return ReviewScore(**scores)

    def _evaluate_video(self, video_path, brief):
        # 实际实现会调用 Gemini 多模态 API
        # prompt 包含评分标准、创意简报、视频（或关键帧）
        pass

    def should_retry(self, score: ReviewScore, threshold: float) -> bool:
        return score.overall < threshold

# pipelines/ugc-ad.yaml
name: ugc-ad
description: "用户生成内容广告流水线"

steps:
  - agent: director          # 第一步：导演选择镜头
  - agent: trim_refiner      # 第二步：优化裁剪点
  - agent: editor            # 第三步：FFmpeg 渲染
  - agent: reviewer          # 第四步：质量评审
    retry_if:
      metric: overall        # 基于综合评分重试
      threshold: 0.65        # 阈值：0.65
      max_retries: 2         # 最多重试 2 次（3 次尝试）
      feedback_target: director  # 反馈给导演改进

# pipelines/fast-promo.yaml
name: fast-promo
description: "快速促销视频流水线（无 TrimRefiner）"

steps:
  - agent: director
  - agent: editor
  - agent: reviewer
    retry_if:
      metric: adherence
      threshold: 0.8
      max_retries: 1

{
  "product": "Smart Water Bottle",
  "audience": "Health-conscious women 25-45",
  "tone": "authentic",
  "duration_seconds": 30,
  "style_ref": "styles/dtc-testimonial.yaml"
}

{
  "footage_dir": "/path/to/footage",
  "shots": [
    {
      "id": "shot_001",
      "footage_file": "raw_clips/clip_001.mp4",
      "start_time": 0.0,
      "end_time": 15.3,
      "transcript": "This water bottle tracks my hydration...",
      "visual_features": {
        "scene_complexity": "low",
        "camera_motion": "static",
        "lighting": "natural"
      },
      "audio_features": {
        "energy": "medium",
        "has_speech": true,
        "has_music": false
      }
    }
  ],
  "generated_at": "2026-04-17T12:00:00Z"
}

# 只有素材变化时才重新处理
if footage_index.exists() and is_cache_valid(footage_dir, footage_index):
    logger.info("Using cached footage index")
else:
    logger.info("Generating new footage index")
    footage_index = preprocess(footage_dir)
    footage_index.save()

# 1. 克隆仓库
git clone https://github.com/poseljacob/agentic-video-editor.git
cd agentic-video-editor

# 2. 创建虚拟环境
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

# 3. 安装依赖
pip install -e ".[dev]"

# 4. 配置 API Key
cp .env.example .env
# 编辑 .env，添加：GOOGLE_AI_API_KEY=your_key_here

# 5. 验证 FFmpeg
ffmpeg -version

# 基本用法
ave edit \
  --footage-dir /path/to/your/footage \
  --brief '{"product": "My Product", "audience": "Women 25-45", "tone": "authentic", "duration_seconds": 30}' \
  --pipeline pipelines/ugc-ad.yaml \
  --style styles/dtc-testimonial.yaml

# 终端 1：启动 FastAPI 后端
source .venv/bin/activate
cd agentic-video-editor
uvicorn src.web.app:app --reload --port 8000

# 终端 2：启动 Next.js 前端
cd src/web/studio
pnpm install  # 首次需要安装依赖
pnpm dev --port 3000

# src/agents/my_agent.py
from .base import BaseAgent

class MyCustomAgent(BaseAgent):
    name = "my_custom_agent"
    description = "我的自定义 Agent"

    def run(self, context: dict) -> dict:
        # 实现 Agent 逻辑
        result = self._do_something(context)
        return result

    def _do_something(self, context):
        # 自定义逻辑
        pass

# src/tools/my_tools.py
from google.adk.tools import FunctionTool

@FunctionTool
def search_footage_by_emotion(footage_index: dict, emotion: str) -> list:
    """根据情感搜索素材片段"""
    # 实现搜索逻辑
    # 可以基于 visual_features 或 audio_features 过滤
    pass