757 lines
30 KiB
Python
757 lines
30 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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全功能产品抓取与分析系统
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整合integrated_scraper.py和product_ai_analysis.py的功能
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支持从ProductHunt抓取数据并进行AI分析
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"""
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import sqlite3
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import asyncio
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import os
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import argparse
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from datetime import datetime
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from loguru import logger
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from tqdm import tqdm
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import sys
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import requests
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import json
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import time
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from typing import List, Tuple, Optional
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# 配置日志
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logger.remove()
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logger.add(sys.stderr, level="INFO", format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>")
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# 动态导入playwright-get-data.py
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import importlib.util
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playwright_data_path = os.path.join(os.path.dirname(__file__), "playwright-get-data.py")
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spec = importlib.util.spec_from_file_location("playwright_get_data", playwright_data_path)
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playwright_get_data = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(playwright_get_data)
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ProductHuntScraper = playwright_get_data.ProductHuntScraper
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class IntegratedProductSystem:
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"""全功能产品抓取与分析系统"""
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def __init__(self, tophub_db_path=None, product_db_path=None, debug_port=9222,
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limit=0, skip_duplicates=True, api_key=""):
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"""
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初始化系统
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Args:
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tophub_db_path: tophub数据库路径
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product_db_path: 产品数据库路径
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debug_port: Chrome调试端口
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limit: 抓取链接数量限制
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skip_duplicates: 是否跳过已存在的URL
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api_key: API密钥（Ollama不需要）
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"""
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# 设置数据库路径
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if tophub_db_path:
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self.tophub_db_path = tophub_db_path
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else:
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self.tophub_db_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "tophub_data.db")
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if product_db_path:
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self.product_db_path = product_db_path
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else:
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self.product_db_path = os.path.join(os.path.dirname(__file__), "products.db")
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self.debug_port = debug_port
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self.limit = limit
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self.skip_duplicates = skip_duplicates
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self.api_key = api_key
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self.api_url = "http://localhost:11434/api/generate"
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self.product_urls = []
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logger.info(f"初始化全功能产品系统，数据库: {self.product_db_path}")
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def connect_to_database(self) -> sqlite3.Connection:
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"""连接到SQLite数据库"""
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try:
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conn = sqlite3.connect(self.product_db_path)
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logger.success(f"成功连接到数据库: {self.product_db_path}")
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return conn
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except Exception as e:
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logger.error(f"连接数据库失败: {e}")
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raise
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def init_database(self):
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"""初始化数据库，创建所有需要的表"""
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logger.info("正在初始化产品数据库...")
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try:
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conn = sqlite3.connect(self.product_db_path)
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cursor = conn.cursor()
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# 创建产品信息表（来自integrated_scraper.py）
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cursor.execute('''
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CREATE TABLE IF NOT EXISTS products (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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url TEXT NOT NULL UNIQUE,
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name TEXT,
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introduction TEXT,
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user_count TEXT,
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maker_link TEXT,
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maker_statement TEXT,
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created_at TEXT NOT NULL,
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updated_at TEXT NOT NULL
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)
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''')
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# 创建分析结果表
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# 根据最新数据库结构更新
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cursor.execute('''
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CREATE TABLE IF NOT EXISTS product_analysis (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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original_name TEXT,
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product_intro TEXT,
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development_difficulty TEXT,
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ai_response TEXT,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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difficulty_score INTEGER,
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product_link TEXT
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)
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''')
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conn.commit()
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conn.close()
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logger.success("产品数据库初始化完成")
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except Exception as e:
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logger.error(f"初始化数据库失败: {e}")
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def query_producthunt_urls(self, limit=None):
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"""查询包含producthunt.com的链接"""
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if limit is None:
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limit = self.limit
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logger.info(f"正在查询tophub_data.db数据库，限制: {limit}条")
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try:
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conn = sqlite3.connect(self.tophub_db_path)
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cursor = conn.cursor()
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# 查询包含producthunt.com的链接
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cursor.execute("SELECT url FROM articles WHERE url LIKE '%producthunt.com%'")
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urls = [row[0] for row in cursor.fetchall()]
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conn.close()
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logger.success(f"找到 {len(urls)} 个包含producthunt.com的链接")
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return urls
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except Exception as e:
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logger.error(f"查询数据库失败: {e}")
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return []
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def check_duplicate(self, url):
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"""检查URL是否已存在"""
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try:
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conn = sqlite3.connect(self.product_db_path)
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cursor = conn.cursor()
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cursor.execute("SELECT COUNT(*) FROM products WHERE url = ?", (url,))
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count = cursor.fetchone()[0]
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conn.close()
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return count > 0
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except Exception as e:
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logger.error(f"检查重复失败: {e}")
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return False
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def save_product_info(self, product_info):
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"""保存产品信息到数据库"""
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try:
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conn = sqlite3.connect(self.product_db_path)
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cursor = conn.cursor()
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current_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
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# 检查是否已存在
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cursor.execute("SELECT id FROM products WHERE url = ?", (product_info['url'],))
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existing = cursor.fetchone()
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if existing:
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# 更新现有记录
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cursor.execute('''
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UPDATE products SET
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name = ?, introduction = ?, user_count = ?,
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maker_link = ?, maker_statement = ?, updated_at = ?
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WHERE url = ?
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''', (
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product_info.get('name'),
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product_info.get('introduction'),
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product_info.get('user_count'),
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product_info.get('maker_link'),
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product_info.get('maker_statement'),
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current_time,
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product_info['url']
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))
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logger.info(f"更新产品信息: {product_info.get('name', '未知')}")
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else:
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# 插入新记录
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cursor.execute('''
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INSERT INTO products
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(url, name, introduction, user_count, maker_link, maker_statement, created_at, updated_at)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?)
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''', (
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product_info['url'],
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product_info.get('name'),
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product_info.get('introduction'),
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product_info.get('user_count'),
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product_info.get('maker_link'),
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product_info.get('maker_statement'),
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current_time,
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current_time
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))
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logger.info(f"新增产品信息: {product_info.get('name', '未知')}")
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conn.commit()
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conn.close()
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return True
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except Exception as e:
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logger.error(f"保存产品信息失败: {e}")
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return False
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async def scrape_product_info(self, url):
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"""使用playwright-get-data.py中的专业功能抓取产品信息"""
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try:
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logger.info(f"开始抓取: {url}")
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# 创建ProductHuntScraper实例
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scraper = ProductHuntScraper(debug_port=self.debug_port)
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# 连接到已运行的Chrome实例
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connected = await scraper.connect_to_existing_chrome()
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if not connected:
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logger.error("连接Chrome失败，跳过此URL")
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return None
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# 导航到ProductHunt页面
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navigated = await scraper.navigate_to_producthunt(url)
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if not navigated:
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logger.error("导航到页面失败，跳过此URL")
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await scraper.close()
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return None
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# 提取产品信息
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product_info = await scraper.extract_product_info()
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if product_info:
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product_info['url'] = url
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logger.success(f"成功提取产品信息: {product_info.get('name', '未知')}")
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else:
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logger.error("提取产品信息失败")
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# 关闭连接
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await scraper.close()
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return product_info
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except Exception as e:
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logger.error(f"抓取产品信息失败: {e}")
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return None
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def get_product_data(self, conn: sqlite3.Connection) -> List[Tuple]:
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"""从数据库获取产品数据"""
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try:
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cursor = conn.cursor()
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# 查询products表中的name和introduction字段
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cursor.execute("""
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SELECT id, name, introduction
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FROM products
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WHERE name IS NOT NULL AND introduction IS NOT NULL
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AND name != '' AND introduction != ''
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""")
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products = cursor.fetchall()
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logger.info(f"从数据库获取到 {len(products)} 个产品")
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# 显示前几个产品作为示例
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for i, (id, name, intro) in enumerate(products[:3], 1):
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logger.info(f"示例产品{i}: ID={id}, 名称='{name}', 简介='{intro[:50]}...'")
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return products
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except Exception as e:
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logger.error(f"获取产品数据失败: {e}")
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raise
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def call_ollama_ai_api(self, name: str, introduction: str) -> Optional[str]:
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"""调用Ollama AI API进行分析"""
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try:
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# 构建请求数据 - 使用Ollama API格式
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prompt = f"这个是【{name}】，简介内容是【{introduction}】。请把产品的简介翻译成中文，并返回假设一个人加上AI辅助能否开发这个产品，请详细回答。返回的内容是产品名称/产品简介/开发难度。返回的例子一：notion/这个是笔记产品等等/一个人开发难度较高"
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data = {
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"model": "qwen3:8b",
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"prompt": prompt,
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"stream": False
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}
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headers = {
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"Content-Type": "application/json"
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}
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logger.info(f"调用Ollama AI API分析产品: {name}")
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response = requests.post(
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self.api_url,
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headers=headers,
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data=json.dumps(data, ensure_ascii=False),
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timeout=60
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)
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if response.status_code == 200:
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result = response.json()
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content = result.get("response", "")
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logger.success(f"API调用成功: {name}")
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return content
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else:
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logger.error(f"API调用失败: {response.status_code}, {response.text}")
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return None
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except Exception as e:
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logger.error(f"调用Ollama AI API时出错: {e}")
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return None
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def parse_ai_response(self, response: str) -> Tuple[str, str, str, int]:
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"""解析AI响应内容，提取产品名称、简介、难度描述和难度分数"""
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try:
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# 使用/分割响应内容
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parts = response.split('/')
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product_intro = ""
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difficulty = ""
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difficulty_score = None
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if len(parts) >= 3:
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# 不再使用product_name，直接从原始名称中获取
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product_intro = parts[1].strip()
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difficulty = parts[2].strip()
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logger.info(f"解析结果: 简介='{product_intro[:30]}...', 难度='{difficulty}'")
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# 从难度描述中提取分数
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import re
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# 尝试匹配数字分数
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score_match = re.search(r'\b(\d+)\b分|\b难度(\d+)\b|\b(\d+)\b', difficulty)
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if score_match:
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# 获取第一个匹配的数字
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for group in score_match.groups():
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if group:
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difficulty_score = int(group)
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break
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# 如果没有提取到分数，根据关键词设置默认分数
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if difficulty_score is None:
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difficulty_lower = difficulty.lower()
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if any(keyword in difficulty_lower for keyword in ['高', '很难', '非常难', '复杂']):
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difficulty_score = 85
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elif any(keyword in difficulty_lower for keyword in ['中', '一般', '适中', '普通']):
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difficulty_score = 60
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elif any(keyword in difficulty_lower for keyword in ['低', '简单', '容易']):
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difficulty_score = 35
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else:
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difficulty_score = 50 # 默认中等难度
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logger.info(f"提取到难度分数: {difficulty_score}")
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else:
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logger.warning(f"响应格式不符合预期: {response}")
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# 如果格式不符合，返回原始内容
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difficulty = response
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difficulty_score = 50 # 默认中等难度
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return product_intro, difficulty, difficulty_score
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except Exception as e:
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logger.error(f"解析AI响应失败: {e}")
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return "", response, 50
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def check_product_exists_in_analysis(self, conn: sqlite3.Connection, original_name: str) -> bool:
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"""检查产品是否已存在于分析结果表中"""
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try:
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cursor = conn.cursor()
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cursor.execute("""
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SELECT COUNT(*) FROM product_analysis
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WHERE original_name = ?
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""", (original_name,))
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count = cursor.fetchone()[0]
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exists = count > 0
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if exists:
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logger.info(f"产品 '{original_name}' 已存在，跳过分析")
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return exists
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except Exception as e:
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logger.error(f"检查产品存在性失败: {e}")
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return False
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def save_analysis_result(self, conn: sqlite3.Connection,
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original_name: str, difficulty: str,
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ai_response: str, difficulty_score: int = None,
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product_link: str = None):
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"""保存分析结果到数据库，包括难度分数和产品链接"""
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try:
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cursor = conn.cursor()
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# 如果没有提供难度分数，设置默认值50
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if difficulty_score is None:
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difficulty_score = 50
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cursor.execute("""
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INSERT INTO product_analysis
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(original_name, development_difficulty, difficulty_score, ai_response, product_link)
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VALUES (?, ?, ?, ?, ?)
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""", (original_name, difficulty, difficulty_score, ai_response, product_link))
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conn.commit()
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logger.success(f"保存分析结果成功: {original_name}, 难度分数: {difficulty_score}")
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except Exception as e:
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logger.error(f"保存分析结果失败: {e}")
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raise
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def analyze_products(self, max_products: int = None):
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"""分析产品数据"""
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if max_products is None:
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logger.info("开始分析所有产品数据")
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else:
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logger.info(f"开始分析产品数据，最大数量: {max_products}")
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conn = None
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try:
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# 连接数据库
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conn = self.connect_to_database()
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# 获取产品数据
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products = self.get_product_data(conn)
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if not products:
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logger.warning("没有找到可分析的产品数据")
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return
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# 限制分析数量
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if max_products is not None:
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products_to_analyze = products[:max_products]
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else:
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products_to_analyze = products
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logger.info(f"准备分析 {len(products_to_analyze)} 个产品")
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# 逐个分析产品
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success_count = 0
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skip_count = 0
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for i, (original_id, name, introduction) in enumerate(products_to_analyze, 1):
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logger.info(f"\n分析进度: {i}/{len(products_to_analyze)} - {name}")
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# 检查产品是否已存在
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if self.check_product_exists_in_analysis(conn, name):
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skip_count += 1
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logger.info(f"跳过已存在产品，当前进度: {i}/{len(products_to_analyze)}")
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continue
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# 显示API调用状态
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logger.info(f"正在提交API请求... 进度: {i}/{len(products_to_analyze)}")
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# 调用AI API
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ai_response = self.call_ollama_ai_api(name, introduction)
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if ai_response:
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# 显示数据处理状态
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logger.info(f"API调用成功，正在处理数据...")
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# 解析响应
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product_intro, difficulty, difficulty_score = self.parse_ai_response(ai_response)
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# 保存结果（不再保存product_intro，避免与ai_response重复）
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self.save_analysis_result(conn, name, difficulty, ai_response, difficulty_score)
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success_count += 1
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# 显示完成状态
|
||
logger.success(f"产品 '{name}' 分析完成，进度: {i}/{len(products_to_analyze)}")
|
||
else:
|
||
logger.error(f"分析失败: {name}")
|
||
|
||
# 处理完数据后延时2秒
|
||
logger.info("数据处理完成，等待2秒后继续...")
|
||
time.sleep(2)
|
||
|
||
logger.success(f"分析完成! 成功分析 {success_count} 个产品，跳过 {skip_count} 个已存在产品")
|
||
|
||
except Exception as e:
|
||
logger.error(f"分析过程中出错: {e}")
|
||
finally:
|
||
if conn:
|
||
conn.close()
|
||
logger.info("数据库连接已关闭")
|
||
|
||
async def run_scraping(self, urls=None):
|
||
"""运行抓取任务"""
|
||
logger.info("=== 开始ProductHunt数据抓取 ===")
|
||
|
||
# 获取要抓取的URL列表
|
||
if urls is None:
|
||
self.product_urls = self.query_producthunt_urls()
|
||
else:
|
||
self.product_urls = urls
|
||
|
||
if not self.product_urls:
|
||
logger.error("未找到要抓取的ProductHunt链接")
|
||
return False
|
||
|
||
logger.info(f"找到 {len(self.product_urls)} 个ProductHunt链接")
|
||
|
||
# 统计抓取结果
|
||
success_count = 0
|
||
skip_count = 0
|
||
error_count = 0
|
||
|
||
# 使用进度条显示处理进度
|
||
with tqdm(total=len(self.product_urls), desc="抓取ProductHunt链接") as pbar:
|
||
for url in self.product_urls:
|
||
logger.info(f"处理URL: {url}")
|
||
|
||
# 检查是否已存在
|
||
if self.skip_duplicates and self.check_duplicate(url):
|
||
logger.info(f"URL已存在，跳过: {url}")
|
||
skip_count += 1
|
||
pbar.update(1)
|
||
continue
|
||
|
||
# 抓取产品信息
|
||
product_info = await self.scrape_product_info(url)
|
||
|
||
if product_info:
|
||
# 保存到数据库
|
||
success = self.save_product_info(product_info)
|
||
if success:
|
||
logger.success(f"成功保存产品信息: {product_info.get('name', '未知')}")
|
||
success_count += 1
|
||
else:
|
||
logger.error(f"保存产品信息失败: {url}")
|
||
error_count += 1
|
||
else:
|
||
logger.error(f"抓取产品信息失败: {url}")
|
||
error_count += 1
|
||
|
||
pbar.update(1)
|
||
|
||
# 显示抓取结果统计
|
||
self.show_scraping_results(success_count, skip_count, error_count)
|
||
|
||
logger.success("=== ProductHunt数据抓取完成 ===")
|
||
return True
|
||
|
||
def show_scraping_results(self, success_count, skip_count, error_count):
|
||
"""显示抓取结果统计"""
|
||
try:
|
||
conn = sqlite3.connect(self.product_db_path)
|
||
cursor = conn.cursor()
|
||
|
||
# 统计数据库中的产品数量
|
||
cursor.execute("SELECT COUNT(*) FROM products")
|
||
total_count = cursor.fetchone()[0]
|
||
|
||
# 获取最新抓取的产品信息
|
||
cursor.execute("SELECT name, url FROM products ORDER BY updated_at DESC LIMIT 10")
|
||
recent_products = cursor.fetchall()
|
||
|
||
conn.close()
|
||
|
||
logger.info("=== 抓取结果统计 ===")
|
||
logger.info(f"成功抓取: {success_count} 个产品")
|
||
logger.info(f"跳过重复: {skip_count} 个链接")
|
||
logger.info(f"抓取失败: {error_count} 个链接")
|
||
logger.info(f"数据库中的产品总数: {total_count}")
|
||
|
||
if recent_products:
|
||
logger.info("最新抓取的产品:")
|
||
for name, url in recent_products:
|
||
logger.info(f" - {name}: {url}")
|
||
else:
|
||
logger.info("数据库中暂无产品记录")
|
||
|
||
except Exception as e:
|
||
logger.error(f"显示抓取结果失败: {e}")
|
||
|
||
def analyze_missing_scores(self):
|
||
"""分析并补充缺失难度分数的产品"""
|
||
logger.info("=== 开始分析缺失难度分数的产品 ===")
|
||
|
||
conn = None
|
||
try:
|
||
# 连接数据库
|
||
conn = self.connect_to_database()
|
||
cursor = conn.cursor()
|
||
|
||
# 查询缺失难度分数的产品
|
||
cursor.execute("""
|
||
SELECT pa.id, pa.original_name, pa.product_intro, pa.ai_response
|
||
FROM product_analysis pa
|
||
WHERE pa.difficulty_score IS NULL OR pa.difficulty_score = ''
|
||
""")
|
||
|
||
products_with_missing_scores = cursor.fetchall()
|
||
logger.info(f"找到 {len(products_with_missing_scores)} 个缺失难度分数的产品")
|
||
|
||
if not products_with_missing_scores:
|
||
logger.info("没有发现缺失难度分数的产品")
|
||
return
|
||
|
||
# 为每个缺失分数的产品分析并更新分数
|
||
updated_count = 0
|
||
for i, (analysis_id, name, introduction, ai_response) in enumerate(products_with_missing_scores, 1):
|
||
logger.info(f"处理缺失分数的产品 {i}/{len(products_with_missing_scores)}: {name}")
|
||
|
||
# 如果已有AI响应，从响应中重新提取分数
|
||
difficulty_score = None
|
||
if ai_response:
|
||
try:
|
||
_, _, _, difficulty_score = self.parse_ai_response(ai_response)
|
||
logger.info(f"从现有AI响应中提取分数: {difficulty_score}")
|
||
except Exception as e:
|
||
logger.error(f"从现有响应提取分数失败: {e}")
|
||
|
||
# 如果无法从现有响应提取，重新调用API
|
||
if difficulty_score is None:
|
||
logger.info(f"重新调用API分析产品: {name}")
|
||
ai_response = self.call_ollama_ai_api(name, introduction)
|
||
if ai_response:
|
||
_, _, _, difficulty_score = self.parse_ai_response(ai_response)
|
||
# 更新AI响应
|
||
cursor.execute("""
|
||
UPDATE product_analysis
|
||
SET ai_response = ?
|
||
WHERE id = ?
|
||
""", (ai_response, analysis_id))
|
||
|
||
# 更新难度分数
|
||
if difficulty_score is not None:
|
||
cursor.execute("""
|
||
UPDATE product_analysis
|
||
SET difficulty_score = ?
|
||
WHERE id = ?
|
||
""", (difficulty_score, analysis_id))
|
||
conn.commit()
|
||
updated_count += 1
|
||
logger.success(f"成功更新产品 '{name}' 的难度分数为 {difficulty_score}")
|
||
else:
|
||
logger.warning(f"无法为产品 '{name}' 确定难度分数")
|
||
|
||
# 避免API调用过于频繁
|
||
if i < len(products_with_missing_scores):
|
||
time.sleep(2)
|
||
|
||
logger.success(f"缺失分数分析完成! 成功更新 {updated_count} 个产品的难度分数")
|
||
|
||
except Exception as e:
|
||
logger.error(f"分析缺失分数过程中出错: {e}")
|
||
finally:
|
||
if conn:
|
||
conn.close()
|
||
logger.info("数据库连接已关闭")
|
||
|
||
async def run_full_workflow_async(self, max_products=None, analyze_only=False):
|
||
"""异步运行完整工作流程：抓取+分析+补充缺失分数"""
|
||
logger.info("=== 开始全功能产品系统工作流程 ===")
|
||
|
||
# 初始化数据库
|
||
self.init_database()
|
||
|
||
if not analyze_only:
|
||
# 步骤1: 抓取数据
|
||
logger.info("步骤1: 开始抓取ProductHunt数据...")
|
||
await self.run_scraping()
|
||
else:
|
||
logger.info("跳过抓取步骤，直接进行分析")
|
||
|
||
# 步骤2: AI分析
|
||
logger.info("步骤2: 开始AI分析产品数据...")
|
||
self.analyze_products(max_products)
|
||
|
||
# 步骤3: 分析并补充缺失的难度分数
|
||
logger.info("步骤3: 开始分析并补充缺失的难度分数...")
|
||
self.analyze_missing_scores()
|
||
|
||
logger.success("=== 全功能产品系统工作流程完成 ===")
|
||
|
||
def run_full_workflow(self, max_products=None, analyze_only=False):
|
||
"""运行完整工作流程：抓取+分析（同步入口）"""
|
||
# 创建新的事件循环来运行异步函数
|
||
loop = asyncio.new_event_loop()
|
||
asyncio.set_event_loop(loop)
|
||
try:
|
||
loop.run_until_complete(self.run_full_workflow_async(max_products, analyze_only))
|
||
finally:
|
||
loop.close()
|
||
|
||
|
||
def parse_arguments():
|
||
"""解析命令行参数"""
|
||
parser = argparse.ArgumentParser(description="全功能产品抓取与分析系统")
|
||
parser.add_argument("--tophub-db", help="tophub数据库路径", default=None)
|
||
parser.add_argument("--product-db", help="产品数据库路径", default=None)
|
||
parser.add_argument("--debug-port", type=int, help="Chrome调试端口", default=9222)
|
||
parser.add_argument("--limit", type=int, help="抓取链接数量限制", default=0)
|
||
parser.add_argument("--no-skip-duplicates", action="store_true", help="不跳过重复URL")
|
||
parser.add_argument("--urls", nargs="+", help="指定要抓取的URL列表")
|
||
parser.add_argument("--log-file", help="日志文件路径", default="integrated_product_system.log")
|
||
parser.add_argument("--max-products", type=int, help="最大分析产品数量", default=None)
|
||
parser.add_argument("--analyze-only", action="store_true", help="仅进行分析，不抓取数据")
|
||
|
||
return parser.parse_args()
|
||
|
||
|
||
async def main():
|
||
"""主函数"""
|
||
args = parse_arguments()
|
||
|
||
# 配置日志文件输出
|
||
logger.add(args.log_file, level="INFO", rotation="10 MB")
|
||
|
||
# 运行start_chrome.bat批处理程序
|
||
import subprocess
|
||
chrome_bat_path = os.path.join(os.path.dirname(__file__), "start_chrome.bat")
|
||
logger.info(f"正在运行Chrome启动脚本: {chrome_bat_path}")
|
||
try:
|
||
# 运行批处理程序，等待其完成
|
||
subprocess.run([chrome_bat_path], check=True, shell=True)
|
||
logger.success("Chrome启动脚本执行成功")
|
||
except subprocess.CalledProcessError as e:
|
||
logger.error(f"Chrome启动脚本执行失败: {e}")
|
||
except FileNotFoundError:
|
||
logger.error(f"未找到Chrome启动脚本: {chrome_bat_path}")
|
||
|
||
# 创建系统实例
|
||
system = IntegratedProductSystem(
|
||
tophub_db_path=args.tophub_db,
|
||
product_db_path=args.product_db,
|
||
debug_port=args.debug_port,
|
||
limit=args.limit,
|
||
skip_duplicates=not args.no_skip_duplicates,
|
||
api_key="" # Ollama不需要API密钥
|
||
)
|
||
|
||
# 运行完整工作流程
|
||
if args.urls:
|
||
# 如果指定了URL，先抓取这些URL
|
||
await system.run_scraping(urls=args.urls)
|
||
# 然后进行分析
|
||
system.analyze_products(max_products=args.max_products)
|
||
else:
|
||
# 运行完整工作流程
|
||
await system.run_full_workflow_async(max_products=args.max_products, analyze_only=args.analyze_only)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
# 运行异步主函数
|
||
asyncio.run(main()) |