250 lines
9.7 KiB
Python
250 lines
9.7 KiB
Python
import os
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from openai import OpenAI
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from dotenv import load_dotenv
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from typing import Dict, Any, Optional
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from pydantic import BaseModel
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# 加载环境变量
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load_dotenv()
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class LLMService:
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"""LLM服务类，用于调用DeepSeek API"""
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def __init__(self):
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"""初始化LLM服务"""
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# 直接从.env文件读取API密钥
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env_path = os.path.join(os.path.dirname(__file__), "..", "..", ".env")
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env_vars = {}
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if os.path.exists(env_path):
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with open(env_path, "r") as f:
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for line in f:
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line = line.strip()
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if line and not line.startswith("#"):
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key, value = line.split("=", 1)
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env_vars[key.strip()] = value.strip()
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# 获取API密钥和基础URL
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self.api_key = env_vars.get("DEEPSEEK_API_KEY") or os.getenv("DEEPSEEK_API_KEY")
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self.base_url = env_vars.get("DEEPSEEK_BASE_URL") or os.getenv("DEEPSEEK_BASE_URL", "https://api.deepseek.com")
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# 清理并修复base_url，确保包含协议前缀
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if self.base_url:
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# 清理URL：移除多余的空格、引号和反引号
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self.base_url = self.base_url.strip()
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# 移除可能的引号
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for quote_char in ['"', "'", '`']:
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if self.base_url.startswith(quote_char) and self.base_url.endswith(quote_char):
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self.base_url = self.base_url[1:-1].strip()
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break
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# 确保包含协议前缀
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if not (self.base_url.startswith("http://") or self.base_url.startswith("https://")):
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print(f"警告: base_url '{self.base_url}' 缺少协议前缀，将添加 'https://'")
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self.base_url = f"https://{self.base_url}"
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# 默认模型
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self.default_model = "deepseek-chat"
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# 延迟创建客户端，直到实际需要时
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self.client = None
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def _ensure_client(self):
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"""确保客户端已初始化"""
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if self.client is None:
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# 确保API密钥存在
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if not self.api_key:
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raise ValueError("DEEPSEEK_API_KEY环境变量未设置")
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# 创建客户端
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self.client = OpenAI(
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api_key=self.api_key,
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base_url=self.base_url
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)
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def _call_api(
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self,
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messages: list,
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model: str = None,
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temperature: float = 0.7,
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max_tokens: int = 1000,
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response_format: Optional[Dict[str, Any]] = None
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) -> str:
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"""调用LLM API"""
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if model is None:
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model = self.default_model
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try:
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# 确保客户端已初始化
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self._ensure_client()
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response = self.client.chat.completions.create(
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model=model,
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messages=messages,
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temperature=temperature,
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max_tokens=max_tokens,
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response_format=response_format
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)
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return response.choices[0].message.content
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except Exception as e:
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import json
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import traceback
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print(f"LLM API调用失败: {e}")
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print(f"错误类型: {type(e).__name__}")
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# 尝试获取更详细的错误信息
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if hasattr(e, 'response') and e.response:
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try:
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error_details = e.response.json()
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print(f"详细错误信息: {json.dumps(error_details, ensure_ascii=False, indent=2)}")
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except:
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print(f"原始响应内容: {e.response.text}")
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traceback.print_exc()
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return ""
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def translate_text(self, text: str, target_lang: str = "zh-CN") -> str:
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"""将文本翻译成目标语言"""
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messages = [
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{
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"role": "system",
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"content": f"你是一个专业的翻译助手，请将给定的文本翻译成{target_lang}。保持原文的意思，翻译要准确、自然。"
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},
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{
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"role": "user",
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"content": text
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}
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]
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return self._call_api(messages, temperature=0.3)
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def explain_prediction(self, text: str, label: str, probability: float) -> Dict[str, Any]:
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"""解释模型预测结果"""
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messages = [
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{
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"role": "system",
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"content": "你是一个专业的短信分类解释专家。请根据给定的短信内容、分类结果和概率，生成一个清晰、简洁的解释。解释应该包括：\n1. 短信的主要内容\n2. 为什么被分类为垃圾短信或正常短信\n3. 分类的可信度\n\n请使用结构化的JSON格式输出，包含以下字段：\n- content_summary: 短信内容摘要\n- classification_reason: 分类原因\n- confidence_level: 可信度级别（高、中、低）\n- confidence_explanation: 可信度解释\n- suggestions: 针对该短信的建议"
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},
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{
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"role": "user",
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"content": f"短信内容: {text}\n分类结果: {label}\n分类概率: {probability:.2f}"
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}
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]
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response = self._call_api(
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messages,
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temperature=0.5,
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response_format={"type": "json_object"}
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)
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import json
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try:
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result = json.loads(response)
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# 确保suggestions字段是一个列表
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if not isinstance(result.get("suggestions"), list):
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suggestions_text = result.get("suggestions", "")
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# 如果是字符串，尝试分割成列表
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if isinstance(suggestions_text, str):
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# 移除可能的前缀
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suggestions_text = suggestions_text.replace("建议用户：", "")
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suggestions_text = suggestions_text.replace("建议：", "")
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# 按序号分割
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import re
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# 匹配数字. 开头的模式
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suggestions = re.split(r'\d+\.\s*', suggestions_text)
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# 过滤掉空字符串
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suggestions = [s.strip() for s in suggestions if s.strip()]
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result["suggestions"] = suggestions
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else:
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# 如果是其他类型，设置为空列表
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result["suggestions"] = []
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return result
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except json.JSONDecodeError:
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return {
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"content_summary": "",
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"classification_reason": "",
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"confidence_level": "中",
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"confidence_explanation": "",
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"suggestions": []
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}
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def generate_advice(self, text: str, label: str) -> str:
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"""生成行动建议"""
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messages = [
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{
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"role": "system",
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"content": "你是一个专业的短信管理顾问。请根据给定的短信内容和分类结果，生成具体、实用的行动建议。建议要简洁明了，针对性强。"
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},
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{
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"role": "user",
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"content": f"短信内容: {text}\n分类结果: {label}"
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}
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]
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return self._call_api(messages, temperature=0.5)
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def analyze_spam_patterns(self, spam_texts: list) -> str:
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"""分析垃圾短信的模式"""
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if len(spam_texts) == 0:
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return "没有提供垃圾短信样本"
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# 限制短信数量，避免超过API限制
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sample_texts = spam_texts[:5]
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texts_str = "\n".join([f"{i+1}. {text}" for i, text in enumerate(sample_texts)])
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messages = [
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{
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"role": "system",
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"content": "你是一个垃圾短信模式分析专家。请分析给定的垃圾短信样本，总结出常见的模式和特征。分析要全面、准确，包括但不限于：\n1. 内容特征\n2. 语言风格\n3. 发送目的\n4. 常见关键词\n\n请使用简洁明了的语言输出分析结果。"
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},
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{
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"role": "user",
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"content": f"垃圾短信样本：\n{texts_str}"
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}
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]
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return self._call_api(messages, temperature=0.5)
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# 创建全局LLM服务实例
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llm_service = LLMService()
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def get_llm_service() -> LLMService:
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"""获取LLM服务实例"""
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return llm_service
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def main():
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"""主函数，用于测试"""
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# 测试翻译功能
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test_text = "Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's"
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print("翻译测试:")
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print(f"原文: {test_text}")
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translation = llm_service.translate_text(test_text)
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print(f"译文: {translation}")
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# 测试解释功能
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print("\n解释测试:")
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explanation = llm_service.explain_prediction(test_text, "spam", 0.95)
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print(f"解释结果: {explanation}")
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# 测试建议功能
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print("\n建议测试:")
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advice = llm_service.generate_advice(test_text, "spam")
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print(f"建议: {advice}")
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# 测试模式分析功能
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print("\n模式分析测试:")
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spam_samples = [
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"Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005.",
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"WINNER!! As a valued network customer you have been selected to receivea <20>900 prize reward!",
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"Had your mobile 11 months or more? U R entitled to Update to the latest colour mobiles with camera for Free!"
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]
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pattern_analysis = llm_service.analyze_spam_patterns(spam_samples)
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print(f"模式分析: {pattern_analysis}")
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if __name__ == "__main__":
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main() |