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| import requests
import json
import time
from typing import List, Dict, Any
from abc import ABC, abstractmethod
class BaseExtractor(ABC):
"""抽象基类 - 数据提取器接口"""
def __init__(self, config: Dict[str, Any]):
self.config = config
self.session = self._create_session()
def _create_session(self) -> requests.Session:
"""创建HTTP会话"""
session = requests.Session()
session.headers.update(self.config.get('headers', {}))
return session
@abstractmethod
def search(self, query: str) -> List[str]:
"""搜索方法 - 子类必须实现"""
pass
@abstractmethod
def extract(self, item_id: str) -> Dict[str, Any]:
"""提取方法 - 子类必须实现"""
pass
class CacheManager:
"""缓存管理器"""
def __init__(self, cache_file: str):
self.cache_file = cache_file
self.data = self._load()
def _load(self) -> Dict:
try:
with open(self.cache_file, 'r') as f:
return json.load(f)
except FileNotFoundError:
return {}
def get(self, key: str) -> Any:
return self.data.get(key)
def set(self, key: str, value: Any):
self.data[key] = value
self._save()
def _save(self):
with open(self.cache_file, 'w') as f:
json.dump(self.data, f)
class DataProcessor:
"""数据处理器"""
@staticmethod
def process_batch(items: List[str], processor_func) -> List[Dict]:
"""批量处理数据"""
results = []
for item in items:
try:
result = processor_func(item)
if result:
results.append(result)
except Exception as e:
print(f"处理失败: {item}, 错误: {e}")
return results
@staticmethod
def save_results(data: List[Dict], filename: str):
"""保存结果"""
with open(filename, 'w', encoding='utf-8') as f:
json.dump(data, f, ensure_ascii=False, indent=2)
class WebExtractor(BaseExtractor):
"""具体实现类"""
def __init__(self, config: Dict[str, Any]):
super().__init__(config)
self.cache = CacheManager(config.get('cache_file', 'cache.json'))
def search(self, query: str) -> List[str]:
"""搜索实现"""
# 检查缓存
cache_key = f"search_{query}"
cached = self.cache.get(cache_key)
if cached:
return cached
# 模拟网络请求
time.sleep(1) # 请求延迟
# 这里是具体的搜索逻辑
# 实际项目中会有具体的API调用和数据解析
results = self._perform_search(query)
# 缓存结果
self.cache.set(cache_key, results)
return results
def extract(self, item_id: str) -> Dict[str, Any]:
"""提取实现"""
# 检查缓存
cached = self.cache.get(item_id)
if cached:
return cached
# 模拟网络请求
time.sleep(0.5)
# 这里是具体的提取逻辑
data = self._perform_extraction(item_id)
# 缓存结果
if data:
self.cache.set(item_id, data)
return data
def _perform_search(self, query: str) -> List[str]:
"""执行搜索 - 具体实现会根据目标网站调整"""
# 这里会有具体的HTTP请求和HTML解析逻辑
return [f"item_{i}" for i in range(3)] # 示例返回
def _perform_extraction(self, item_id: str) -> Dict[str, Any]:
"""执行提取 - 具体实现会根据目标网站调整"""
# 这里会有具体的数据提取逻辑
return {
'id': item_id,
'title': f'Title for {item_id}',
'data': f'Data for {item_id}'
}
class Application:
"""应用程序主类"""
def __init__(self, config: Dict[str, Any]):
self.config = config
self.extractor = WebExtractor(config)
self.processor = DataProcessor()
def run(self, queries: List[str]):
"""运行应用程序"""
all_results = []
for query in queries:
# 搜索相关项目
items = self.extractor.search(query)
# 提取详细数据
for item_id in items:
data = self.extractor.extract(item_id)
if data:
data['query'] = query
all_results.append(data)
# 保存结果
self.processor.save_results(all_results, 'results.json')
print(f"完成处理，共获取 {len(all_results)} 条数据")
def main():
"""主函数"""
# 配置参数
config = {
'headers': {'User-Agent': 'Generic-Bot/1.0'},
'cache_file': 'app_cache.json',
'delay': 1.0
}
# 创建应用实例
app = Application(config)
# 示例查询列表
test_queries = ['query1', 'query2', 'query3']
# 运行程序
app.run(test_queries)
if __name__ == '__main__':
main()
|