AI 自主执行时代：从 OpenAI Codex 到 Claude Design，AI 正在替你操作一切

from typing import List, Optional
from dataclasses import dataclass
from enum import Enum

class ActionType(Enum):
    CLICK = "click"
    TYPE = "type"
    SCROLL = "scroll"
    KEY_PRESS = "key_press"
    DRAG = "drag"

@dataclass
class ScreenElement:
    """界面元素抽象"""
    element_id: str
    element_type: str  # button, text_field, menu, etc.
    label: str
    position: tuple  # (x, y)
    bounding_box: tuple  # (x1, y1, x2, y2)
    is_enabled: bool = True
    children: List['ScreenElement'] = None

class MacOSAgent:
    """自主操控 macOS 应用的 AI Agent"""
    
    def __init__(self, model: str = "codex-latest"):
        self.model = model
        self.action_history: List[ActionType] = []
        self.max_steps = 50
        
    def capture_screen(self) -> bytes:
        """捕获当前屏幕截图"""
        # 通过 macOS Screenshot API 获取
        pass
    
    def parse_elements(self, screenshot: bytes) -> List[ScreenElement]:
        """从屏幕截图中解析出可交互元素"""
        # 使用视觉模型识别界面元素
        # 返回元素列表及其位置、类型、标签
        pass
    
    def plan_action(self, goal: str, elements: List[ScreenElement], 
                    history: List[str]) -> dict:
        """规划下一步操作"""
        prompt = f"""
目标：{goal}
当前界面元素：{elements}
已执行操作：{history}
请规划下一步操作。
"""
        # 调用 LLM 生成操作计划
        return {"action": "click", "target": "save_button"}
    
    def execute(self, action: dict) -> bool:
        """执行操作"""
        # 通过 Accessibility API 执行
        # 返回操作是否成功
        pass
    
    def run_task(self, goal: str) -> str:
        """执行完整任务"""
        for step in range(self.max_steps):
            screenshot = self.capture_screen()
            elements = self.parse_elements(screenshot)
            action = self.plan_action(goal, elements, self.action_history)
            success = self.execute(action)
            
            if not success:
                # 错误恢复：回退并尝试其他策略
                self.rollback()
                continue
                
            if self.is_task_complete(goal):
                return "任务完成"
                
        return "超出最大步数"

"""
Claude Design → Claude Code 的设计到代码流水线
展示了 AI 如何将设计规格转化为可执行的代码
"""
import json
from dataclasses import dataclass, asdict
from typing import List, Dict, Optional

@dataclass
class DesignToken:
    """设计系统 token"""
    name: str
    value: str  # e.g., "#3b82f6", "16px", "font-bold"
    category: str  # color, spacing, typography, etc.

@dataclass
class DesignComponent:
    """设计组件定义"""
    name: str
    type: str  # button, card, nav, etc.
    properties: Dict[str, str]
    children: List['DesignComponent'] = None
    tokens_used: List[str] = None  # 引用哪些 token

@dataclass
class DesignSpec:
    """完整设计规格"""
    project_name: str
    tokens: List[DesignToken]
    components: List[DesignComponent]
    layout: Dict  # 页面布局信息
    interactions: List[Dict]  # 交互行为定义

class DesignToCodePipeline:
    """设计到代码转换流水线"""
    
    def __init__(self):
        self.tokens: List[DesignToken] = []
        self.components: List[DesignComponent] = []
        
    def learn_design_system(self, codebase_path: str, 
                           design_files: List[str]) -> None:
        """从代码库和设计文件中自动学习设计系统"""
        # 扫描代码库中的颜色、字体、间距定义
        self._extract_tokens_from_code(codebase_path)
        # 解析 Figma/设计文件中的组件规范
        self._extract_components_from_design(design_files)
        # 建立 token 和组件的映射关系
        self._map_tokens_to_components()
    
    def generate_react_code(self, spec: DesignSpec) -> str:
        """将设计规格转化为 React 代码"""
        code_parts = []
        
        # 1. 生成 CSS/Tailwind token 定义
        code_parts.append(self._generate_token_definitions(spec.tokens))
        
        # 2. 生成组件代码
        for component in spec.components:
            code_parts.append(self._generate_component(component))
        
        # 3. 生成页面布局
        code_parts.append(self._generate_layout(spec.layout))
        
        # 4. 生成交互逻辑
        code_parts.append(self._generate_interactions(spec.interactions))
        
        return "\n".join(code_parts)
    
    def _generate_component(self, comp: DesignComponent) -> str:
        """生成单个 React 组件"""
        props = ", ".join(
            f"{k}={json.dumps(v)}" 
            for k, v in comp.properties.items()
        )
        return f"""
function {comp.name}({{ {props} }}) {{
  return (
    <{comp.type.toLowerCase()} className="...">
      {/* 自动应用设计系统 token */}
    </{comp.type.toLowerCase()}>
  );
}}
"""

# 使用示例
pipeline = DesignToCodePipeline()
pipeline.learn_design_system(
    codebase_path="./my-app/src",
    design_files=["landing-page.fig", "design-system.fig"]
)

# 用户描述需求后，AI 生成设计规格
spec = DesignSpec(
    project_name="电商着陆页",
    tokens=[
        DesignToken("primary-color", "#3b82f6", "color"),
        DesignToken("heading-size", "2rem", "typography"),
    ],
    components=[
        DesignComponent("HeroBanner", "section", {
            "background": "primary-color",
            "padding": "4rem 2rem",
        }),
    ],
    layout={"type": "single-column", "sections": ["hero", "features", "cta"]},
    interactions=[{"event": "click", "target": "cta-button", "action": "navigate"}],
)

react_code = pipeline.generate_react_code(spec)
print(react_code)

"""
自主 AI Agent 框架
综合 OpenAI（视觉）、Anthropic（语义）、Google（模板）三条路线
"""
from abc import ABC, abstractmethod
from typing import List, Dict, Optional, Any
from dataclasses import dataclass
from enum import Enum

class ExecutionMode(Enum):
    VISUAL = "visual"       # 看屏幕→模拟操作（OpenAI 路线）
    SEMANTIC = "semantic"   # 理解意图→生成结构化输出（Anthropic 路线）
    TEMPLATE = "template"   # 匹配模板→参数填充（Google 路线）

@dataclass
class AgentAction:
    """Agent 执行的动作"""
    action_type: str  # click, type, generate, navigate, etc.
    target: str       # 操作目标
    parameters: Dict[str, Any]
    confidence: float  # 0-1，Agent 的置信度

@dataclass
class TaskResult:
    """任务执行结果"""
    success: bool
    output: Any
    actions_taken: List[AgentAction]
    error: Optional[str] = None

class Executor(ABC):
    """执行器基类"""
    @abstractmethod
    def execute(self, action: AgentAction) -> TaskResult:
        pass

class VisualExecutor(Executor):
    """视觉执行器：通过屏幕操作（OpenAI 路线）"""
    def execute(self, action: AgentAction) -> TaskResult:
        # 通过 Accessibility API 执行
        # 截图 → 识别元素 → 执行操作 → 验证结果
        pass

class SemanticExecutor(Executor):
    """语义执行器：直接生成结构化输出（Anthropic 路线）"""
    def execute(self, action: AgentAction) -> TaskResult:
        # 直接调用 API 生成代码/设计/文档
        pass

class TemplateExecutor(Executor):
    """模板执行器：按预设模板执行（Google 路线）"""
    def execute(self, action: AgentAction) -> TaskResult:
        # 加载 Skill 模板 → 填充参数 → 执行流程
        pass

class AutonomousAgent:
    """自主 AI Agent"""
    
    def __init__(self):
        self.executors = {
            ExecutionMode.VISUAL: VisualExecutor(),
            ExecutionMode.SEMANTIC: SemanticExecutor(),
            ExecutionMode.TEMPLATE: TemplateExecutor(),
        }
        self.action_log: List[AgentAction] = []
        self.max_steps = 100
        
    def select_executor(self, task: str, context: Dict) -> ExecutionMode:
        """根据任务类型选择执行策略"""
        # 需要操控桌面应用？→ Visual
        # 需要生成代码/设计？→ Semantic
        # 是重复性工作流？→ Template
        if "打开" in task or "点击" in task:
            return ExecutionMode.VISUAL
        elif "生成" in task or "创建" in task:
            return ExecutionMode.SEMANTIC
        else:
            return ExecutionMode.TEMPLATE
    
    def plan_steps(self, goal: str, mode: ExecutionMode) -> List[AgentAction]:
        """将目标拆解为可执行步骤"""
        # 调用 LLM 进行任务规划
        pass
    
    def verify_result(self, goal: str, result: TaskResult) -> bool:
        """验证结果是否符合预期"""
        # 通过视觉检查或逻辑判断验证
        pass
    
    def run(self, goal: str) -> TaskResult:
        """执行任务主循环"""
        mode = self.select_executor(goal, {})
        steps = self.plan_steps(goal, mode)
        executor = self.executors[mode]
        
        for step in steps[:self.max_steps]:
            result = executor.execute(step)
            self.action_log.append(step)
            
            if not result.success:
                # 自动重试或切换执行模式
                return result
            
            if self.verify_result(goal, result):
                return result
        
        return TaskResult(
            success=False, 
            output=None,
            actions_taken=self.action_log,
            error="超出最大执行步数"
        )

# 使用示例
agent = AutonomousAgent()

# 场景 1：操控桌面应用（OpenAI 路线）
result = agent.run("打开 Safari，搜索 Python 教程，保存前 5 个链接")

# 场景 2：生成设计+代码（Anthropic 路线）
result = agent.run("设计一个登录页面并生成 React 代码")

# 场景 3：执行预设工作流（Google 路线）
result = agent.run("/比价 iPhone 16 在京东、淘宝、拼多多")