mirror of
https://github.com/violettoolssite/CFspider.git
synced 2026-04-05 03:09:01 +08:00
393 lines
13 KiB
Python
393 lines
13 KiB
Python
"""
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CFspider AI Browser V2 - 基于 Playwright 的 AI 智能浏览器
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更稳定的实现，使用 Playwright 替代原生 CDP。
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支持爬虫模式和操作模式，由 AI 驱动完成任务。
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"""
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import asyncio
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import json
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import re
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import random
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from typing import Optional, List, Dict, Any, Callable
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from dataclasses import dataclass
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try:
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from playwright.async_api import async_playwright, Page, Browser
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PLAYWRIGHT_AVAILABLE = True
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except ImportError:
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PLAYWRIGHT_AVAILABLE = False
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try:
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import aiohttp
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except ImportError:
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aiohttp = None
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# 预设 API
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PRESET_APIS = {
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"nvidia": {
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"base_url": "https://integrate.api.nvidia.com/v1",
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"model": "nvidia/llama-3.1-nemotron-70b-instruct",
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"description": "NVIDIA NIM"
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},
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"nvidia-glm": {
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"base_url": "https://integrate.api.nvidia.com/v1",
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"model": "z-ai/glm4.7",
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"description": "NVIDIA GLM4.7"
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},
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"nvidia-minimax": {
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"base_url": "https://integrate.api.nvidia.com/v1",
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"model": "minimaxai/minimax-m2.1",
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"description": "NVIDIA Minimax"
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},
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"modelscope": {
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"base_url": "https://api-inference.modelscope.cn/v1",
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"model": "Qwen/Qwen2.5-Coder-32B-Instruct",
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"description": "ModelScope Qwen"
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},
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"deepseek": {
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"base_url": "https://api.deepseek.com/v1",
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"model": "deepseek-chat",
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"description": "DeepSeek"
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},
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"glm": {
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"base_url": "https://open.bigmodel.cn/api/paas/v4",
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"model": "glm-4-flash",
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"description": "智谱 GLM-4-Flash"
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},
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}
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@dataclass
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class TaskResult:
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"""任务结果"""
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success: bool
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result: str
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steps: List[str]
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error: Optional[str] = None
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class AIBrowserV2:
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"""
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AI 驱动的智能浏览器 V2
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基于 Playwright，更稳定可靠。
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"""
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def __init__(
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self,
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base_url: str = None,
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api_key: str = None,
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model: str = None,
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preset: str = None,
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headless: bool = False,
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slow_mo: int = 100, # 操作延迟（毫秒）
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verbose: bool = True
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):
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if not PLAYWRIGHT_AVAILABLE:
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raise ImportError("请安装 playwright: pip install playwright && playwright install")
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# 处理预设
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if preset and preset in PRESET_APIS:
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config = PRESET_APIS[preset]
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self.base_url = base_url or config["base_url"]
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self.model = model or config["model"]
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else:
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self.base_url = base_url
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self.model = model
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self.api_key = api_key
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self.headless = headless
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self.slow_mo = slow_mo
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self.verbose = verbose
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self._playwright = None
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self._browser: Browser = None
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self._page: Page = None
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def _log(self, msg: str):
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if self.verbose:
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print(f"[AI浏览器] {msg}")
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async def _call_llm(self, messages: List[Dict]) -> str:
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"""调用 LLM"""
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if not aiohttp:
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raise ImportError("请安装 aiohttp: pip install aiohttp")
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {self.api_key}"
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}
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payload = {
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"model": self.model,
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"messages": messages,
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"temperature": 0.3,
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"max_tokens": 2000
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}
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async with aiohttp.ClientSession() as session:
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async with session.post(
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f"{self.base_url.rstrip('/')}/chat/completions",
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headers=headers,
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json=payload,
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timeout=aiohttp.ClientTimeout(total=120)
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) as resp:
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if resp.status != 200:
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error = await resp.text()
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raise Exception(f"API 错误: {error}")
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data = await resp.json()
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return data["choices"][0]["message"]["content"]
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async def start(self):
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"""启动浏览器"""
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self._playwright = await async_playwright().start()
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self._browser = await self._playwright.chromium.launch(
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headless=self.headless,
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slow_mo=self.slow_mo
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)
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self._page = await self._browser.new_page()
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# 设置视口
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await self._page.set_viewport_size({"width": 1280, "height": 800})
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self._log("浏览器已启动")
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async def goto(self, url: str):
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"""导航到 URL"""
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await self._page.goto(url, wait_until="domcontentloaded")
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self._log(f"打开页面: {url}")
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await asyncio.sleep(1)
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async def _get_page_elements(self) -> str:
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"""获取页面可交互元素"""
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elements = await self._page.evaluate("""
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() => {
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const results = [];
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const selectors = 'a, button, input, select, textarea, [onclick], [role="button"], [role="search"], [type="search"], [aria-label*="search" i], [placeholder*="search" i], [placeholder*="搜索" i]';
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const seen = new Set();
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document.querySelectorAll(selectors).forEach((el) => {
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const rect = el.getBoundingClientRect();
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if (rect.width > 0 && rect.height > 0 && results.length < 40) {
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// 避免重复
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const key = el.tagName + el.id + el.className;
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if (seen.has(key)) return;
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seen.add(key);
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let text = (el.innerText || el.value || el.placeholder || el.title || el.getAttribute('aria-label') || '').slice(0, 50).trim();
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let selector = '';
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if (el.id) selector = '#' + el.id;
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else if (el.name) selector = '[name="' + el.name + '"]';
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else if (el.placeholder) selector = '[placeholder*="' + el.placeholder.slice(0,10) + '"]';
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else if (el.className) selector = '.' + el.className.split(' ')[0];
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else selector = el.tagName.toLowerCase();
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results.push({
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idx: results.length,
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tag: el.tagName.toLowerCase(),
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text: text,
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selector: selector,
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type: el.type || '',
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placeholder: el.placeholder || ''
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});
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}
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});
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return results;
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}
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""")
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# 格式化为文本
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lines = []
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for el in elements:
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desc = f"[{el['idx']}] <{el['tag']}> {el['selector']}"
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if el.get('text'):
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desc += f" \"{el['text']}\""
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if el.get('placeholder'):
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desc += f" placeholder=\"{el['placeholder']}\""
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if el.get('type'):
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desc += f" (type={el['type']})"
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lines.append(desc)
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return "\n".join(lines)
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async def execute(self, task: str, max_steps: int = 10) -> TaskResult:
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"""
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执行任务
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Args:
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task: 任务描述
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max_steps: 最大步骤数
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Returns:
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TaskResult
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"""
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steps = []
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system_prompt = """你是一个浏览器自动化助手。根据页面元素和任务，返回下一步操作。
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操作格式（每次只返回一个操作）：
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- CLICK [idx] - 点击元素
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- TYPE [idx] "文本" - 在输入框输入
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- SCROLL down/up - 滚动页面
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- WAIT - 等待页面加载
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- DONE "结果" - 任务完成
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只返回操作命令，不要解释。"""
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for step in range(max_steps):
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# 获取页面信息
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title = await self._page.title()
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url = self._page.url
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elements = await self._get_page_elements()
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user_msg = f"""当前页面: {title}
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URL: {url}
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可交互元素:
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{elements}
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任务: {task}
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已完成步骤: {steps}
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下一步操作:"""
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self._log(f"步骤 {step + 1}: 分析页面...")
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try:
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response = await self._call_llm([
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_msg}
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])
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# 清理响应（移除思考标签）
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response = re.sub(r'<think>.*?</think>', '', response, flags=re.DOTALL).strip()
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self._log(f"AI 决定: {response}")
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# 解析并执行操作
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action = response.strip().split('\n')[0]
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steps.append(action)
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if action.startswith("CLICK"):
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match = re.search(r'CLICK\s*\[?(\d+)\]?', action)
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if match:
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idx = int(match.group(1))
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await self._click_by_index(idx)
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await asyncio.sleep(1)
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elif action.startswith("TYPE"):
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match = re.search(r'TYPE\s*\[?(\d+)\]?\s*["\'](.+?)["\']', action)
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if match:
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idx = int(match.group(1))
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text = match.group(2)
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await self._type_by_index(idx, text)
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await asyncio.sleep(0.5)
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elif action.startswith("SCROLL"):
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direction = "down" if "down" in action.lower() else "up"
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await self._page.evaluate(f"window.scrollBy(0, {'300' if direction == 'down' else '-300'})")
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await asyncio.sleep(0.5)
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elif action.startswith("WAIT"):
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await asyncio.sleep(2)
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elif action.startswith("DONE"):
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match = re.search(r'DONE\s*["\'](.+?)["\']', action)
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result = match.group(1) if match else "任务完成"
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return TaskResult(success=True, result=result, steps=steps)
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except Exception as e:
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self._log(f"步骤错误: {e}")
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steps.append(f"错误: {e}")
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# 如果页面已关闭，停止执行
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if "closed" in str(e).lower():
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break
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return TaskResult(
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success=True,
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result="达到最大步骤数",
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steps=steps
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)
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async def _click_by_index(self, idx: int):
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"""通过索引点击元素"""
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await self._page.evaluate(f"""
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() => {{
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const selectors = 'a, button, input, select, textarea, [onclick], [role="button"]';
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const elements = document.querySelectorAll(selectors);
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if (elements[{idx}]) {{
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elements[{idx}].click();
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}}
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}}
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""")
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async def _type_by_index(self, idx: int, text: str):
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"""通过索引输入文本"""
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# 先点击获取焦点
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await self._click_by_index(idx)
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await asyncio.sleep(0.3)
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# 输入文本（模拟人类打字）
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for char in text:
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await self._page.keyboard.type(char, delay=random.randint(50, 150))
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async def crawl(self, goal: str) -> Dict:
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"""
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爬虫模式：让 AI 分析页面并提取数据
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Args:
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goal: 提取目标
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Returns:
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提取的数据
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"""
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html = await self._page.content()
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prompt = f"""分析这个 HTML 页面，{goal}
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HTML（前5000字符）：
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{html[:5000]}
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返回 JSON 格式数据，只返回 JSON，不要解释。"""
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self._log("AI 分析页面中...")
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response = await self._call_llm([
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{"role": "user", "content": prompt}
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])
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# 提取 JSON
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response = re.sub(r'<think>.*?</think>', '', response, flags=re.DOTALL).strip()
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# 尝试解析 JSON
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try:
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# 尝试找到 JSON 块
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json_match = re.search(r'```(?:json)?\s*([\s\S]*?)\s*```', response)
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if json_match:
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return json.loads(json_match.group(1))
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return json.loads(response)
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except:
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return {"raw": response}
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async def screenshot(self, path: str = "screenshot.png"):
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"""截图"""
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await self._page.screenshot(path=path)
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self._log(f"截图保存: {path}")
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async def close(self):
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"""关闭浏览器"""
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if self._browser:
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await self._browser.close()
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if self._playwright:
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await self._playwright.stop()
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self._log("浏览器已关闭")
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async def __aenter__(self):
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await self.start()
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return self
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async def __aexit__(self, *args):
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await self.close()
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