391 lines
16 KiB
Python
391 lines
16 KiB
Python
from typing import List, Dict, Optional
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import random
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from ...common.database import Database
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import os
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import json
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from dataclasses import dataclass
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import jieba.analyse as jieba_analyse
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import aiohttp
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import hashlib
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from datetime import datetime
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import base64
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import shutil
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import asyncio
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import time
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from PIL import Image
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import io
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from loguru import logger
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import traceback
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from nonebot import get_driver
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from ..chat.config import global_config
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from ..models.utils_model import LLM_request
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from ..chat.utils import get_embedding
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driver = get_driver()
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config = driver.config
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class EmojiManager:
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_instance = None
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EMOJI_DIR = "data/emoji" # 表情包存储目录
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def __new__(cls):
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if cls._instance is None:
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cls._instance = super().__new__(cls)
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cls._instance.db = None
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cls._instance._initialized = False
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return cls._instance
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def __init__(self):
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self.db = Database.get_instance()
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self._scan_task = None
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self.llm = LLM_request(model=global_config.vlm, temperature=0.3, max_tokens=1000)
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self.lm = LLM_request(model=global_config.llm_reasoning_minor, max_tokens=1000)
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def _ensure_emoji_dir(self):
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"""确保表情存储目录存在"""
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os.makedirs(self.EMOJI_DIR, exist_ok=True)
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def initialize(self):
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"""初始化数据库连接和表情目录"""
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if not self._initialized:
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try:
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self.db = Database.get_instance()
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self._ensure_emoji_collection()
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self._ensure_emoji_dir()
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self._initialized = True
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# 启动时执行一次完整性检查
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self.check_emoji_file_integrity()
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except Exception as e:
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logger.error(f"初始化表情管理器失败: {str(e)}")
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def _ensure_db(self):
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"""确保数据库已初始化"""
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if not self._initialized:
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self.initialize()
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if not self._initialized:
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raise RuntimeError("EmojiManager not initialized")
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def _ensure_emoji_collection(self):
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"""确保emoji集合存在并创建索引"""
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if 'emoji' not in self.db.db.list_collection_names():
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self.db.db.create_collection('emoji')
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self.db.db.emoji.create_index([('embedding', '2dsphere')])
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self.db.db.emoji.create_index([('tags', 1)])
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self.db.db.emoji.create_index([('filename', 1)], unique=True)
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def record_usage(self, emoji_id: str):
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"""记录表情使用次数"""
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try:
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self._ensure_db()
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self.db.db.emoji.update_one(
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{'_id': emoji_id},
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{'$inc': {'usage_count': 1}}
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)
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except Exception as e:
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logger.error(f"记录表情使用失败: {str(e)}")
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async def get_emoji_for_text(self, text: str) -> Optional[str]:
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"""根据文本内容获取相关表情包
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Args:
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text: 输入文本
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Returns:
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Optional[str]: 表情包文件路径，如果没有找到则返回None
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"""
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try:
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self._ensure_db()
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# 获取文本的embedding
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text_for_search= await self._get_kimoji_for_text(text)
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text_embedding = get_embedding(text_for_search)
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if not text_embedding:
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logger.error("无法获取文本的embedding")
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return None
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try:
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# 获取所有表情包
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all_emojis = list(self.db.db.emoji.find({}, {'_id': 1, 'path': 1, 'embedding': 1, 'discription': 1}))
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if not all_emojis:
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logger.warning("数据库中没有任何表情包")
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return None
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# 计算余弦相似度并排序
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def cosine_similarity(v1, v2):
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if not v1 or not v2:
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return 0
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dot_product = sum(a * b for a, b in zip(v1, v2))
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norm_v1 = sum(a * a for a in v1) ** 0.5
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norm_v2 = sum(b * b for b in v2) ** 0.5
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if norm_v1 == 0 or norm_v2 == 0:
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return 0
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return dot_product / (norm_v1 * norm_v2)
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# 计算所有表情包与输入文本的相似度
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emoji_similarities = [
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(emoji, cosine_similarity(text_embedding, emoji.get('embedding', [])))
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for emoji in all_emojis
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]
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# 按相似度降序排序
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emoji_similarities.sort(key=lambda x: x[1], reverse=True)
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# 获取前3个最相似的表情包
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top_3_emojis = emoji_similarities[:3]
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if not top_3_emojis:
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logger.warning("未找到匹配的表情包")
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return None
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# 从前3个中随机选择一个
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selected_emoji, similarity = random.choice(top_3_emojis)
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if selected_emoji and 'path' in selected_emoji:
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# 更新使用次数
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self.db.db.emoji.update_one(
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{'_id': selected_emoji['_id']},
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{'$inc': {'usage_count': 1}}
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)
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logger.success(f"找到匹配的表情包: {selected_emoji.get('discription', '无描述')} (相似度: {similarity:.4f})")
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return selected_emoji['path']
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except Exception as search_error:
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logger.error(f"搜索表情包失败: {str(search_error)}")
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return None
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return None
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except Exception as e:
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logger.error(f"获取表情包失败: {str(e)}")
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return None
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async def _get_emoji_discription(self, image_base64: str) -> str:
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"""获取表情包的标签"""
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try:
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prompt = '这是一个表情包，使用中文简洁的描述一下表情包的内容和表情包所表达的情感'
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content, _ = await self.llm.generate_response_for_image(prompt, image_base64)
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logger.debug(f"输出描述: {content}")
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return content
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except Exception as e:
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logger.error(f"获取标签失败: {str(e)}")
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return None
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async def _check_emoji(self, image_base64: str) -> str:
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try:
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prompt = f'这是一个表情包，请回答这个表情包是否满足\"{global_config.EMOJI_CHECK_PROMPT}\"的要求，是则回答是，否则回答否，不要出现任何其他内容'
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content, _ = await self.llm.generate_response_for_image(prompt, image_base64)
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logger.debug(f"输出描述: {content}")
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return content
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except Exception as e:
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logger.error(f"获取标签失败: {str(e)}")
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return None
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async def _get_kimoji_for_text(self, text:str):
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try:
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prompt = f'这是{global_config.BOT_NICKNAME}将要发送的消息内容:\n{text}\n若要为其配上表情包，请你输出这个表情包应该表达怎样的情感，应该给人什么样的感觉，不要太简洁也不要太长，注意不要输出任何对消息内容的分析内容，只输出\"一种什么样的感觉\"中间的形容词部分。'
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content, _ = await self.lm.generate_response_async(prompt)
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logger.info(f"输出描述: {content}")
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return content
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except Exception as e:
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logger.error(f"获取标签失败: {str(e)}")
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return None
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async def _compress_image(self, image_path: str, target_size: int = 0.8 * 1024 * 1024) -> Optional[str]:
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"""压缩图片并返回base64编码
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Args:
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image_path: 图片文件路径
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target_size: 目标文件大小（字节），默认0.8MB
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Returns:
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Optional[str]: 成功返回base64编码的图片数据，失败返回None
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"""
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try:
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file_size = os.path.getsize(image_path)
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if file_size <= target_size:
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# 如果文件已经小于目标大小，直接读取并返回base64
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with open(image_path, 'rb') as f:
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return base64.b64encode(f.read()).decode('utf-8')
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# 打开图片
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with Image.open(image_path) as img:
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# 获取原始尺寸
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original_width, original_height = img.size
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# 计算缩放比例
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scale = min(1.0, (target_size / file_size) ** 0.5)
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# 计算新的尺寸
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new_width = int(original_width * scale)
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new_height = int(original_height * scale)
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# 创建内存缓冲区
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output_buffer = io.BytesIO()
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# 如果是GIF，处理所有帧
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if getattr(img, "is_animated", False):
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frames = []
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for frame_idx in range(img.n_frames):
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img.seek(frame_idx)
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new_frame = img.copy()
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new_frame = new_frame.resize((new_width, new_height), Image.Resampling.LANCZOS)
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frames.append(new_frame)
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# 保存到缓冲区
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frames[0].save(
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output_buffer,
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format='GIF',
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save_all=True,
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append_images=frames[1:],
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optimize=True,
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duration=img.info.get('duration', 100),
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loop=img.info.get('loop', 0)
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)
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else:
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# 处理静态图片
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resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
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# 保存到缓冲区，保持原始格式
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if img.format == 'PNG' and img.mode in ('RGBA', 'LA'):
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resized_img.save(output_buffer, format='PNG', optimize=True)
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else:
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resized_img.save(output_buffer, format='JPEG', quality=95, optimize=True)
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# 获取压缩后的数据并转换为base64
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compressed_data = output_buffer.getvalue()
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logger.success(f"压缩图片: {os.path.basename(image_path)} ({original_width}x{original_height} -> {new_width}x{new_height})")
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return base64.b64encode(compressed_data).decode('utf-8')
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except Exception as e:
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logger.error(f"压缩图片失败: {os.path.basename(image_path)}, 错误: {str(e)}")
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return None
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async def scan_new_emojis(self):
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"""扫描新的表情包"""
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try:
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emoji_dir = "data/emoji"
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os.makedirs(emoji_dir, exist_ok=True)
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# 获取所有支持的图片文件
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files_to_process = [f for f in os.listdir(emoji_dir) if f.lower().endswith(('.jpg', '.jpeg', '.png', '.gif'))]
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for filename in files_to_process:
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image_path = os.path.join(emoji_dir, filename)
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# 检查是否已经注册过
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existing_emoji = self.db.db['emoji'].find_one({'filename': filename})
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if existing_emoji:
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continue
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# 压缩图片并获取base64编码
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image_base64 = await self._compress_image(image_path)
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if image_base64 is None:
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os.remove(image_path)
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continue
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# 获取表情包的描述
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discription = await self._get_emoji_discription(image_base64)
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if global_config.EMOJI_CHECK:
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check = await self._check_emoji(image_base64)
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if '是' not in check:
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os.remove(image_path)
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logger.info(f"描述: {discription}")
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logger.info(f"其不满足过滤规则，被剔除 {check}")
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continue
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logger.info(f"check通过 {check}")
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embedding = get_embedding(discription)
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if discription is not None:
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# 准备数据库记录
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emoji_record = {
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'filename': filename,
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'path': image_path,
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'embedding':embedding,
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'discription': discription,
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'timestamp': int(time.time())
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}
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# 保存到数据库
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self.db.db['emoji'].insert_one(emoji_record)
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logger.success(f"注册新表情包: {filename}")
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logger.info(f"描述: {discription}")
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else:
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logger.warning(f"跳过表情包: {filename}")
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except Exception as e:
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logger.error(f"扫描表情包失败: {str(e)}")
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logger.error(traceback.format_exc())
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async def _periodic_scan(self, interval_MINS: int = 10):
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"""定期扫描新表情包"""
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while True:
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print(f"\033[1;36m[表情包]\033[0m 开始扫描新表情包...")
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await self.scan_new_emojis()
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await asyncio.sleep(interval_MINS * 60) # 每600秒扫描一次
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def check_emoji_file_integrity(self):
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"""检查表情包文件完整性
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如果文件已被删除，则从数据库中移除对应记录
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"""
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try:
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self._ensure_db()
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# 获取所有表情包记录
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all_emojis = list(self.db.db.emoji.find())
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removed_count = 0
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total_count = len(all_emojis)
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for emoji in all_emojis:
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try:
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if 'path' not in emoji:
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logger.warning(f"发现无效记录（缺少path字段），ID: {emoji.get('_id', 'unknown')}")
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self.db.db.emoji.delete_one({'_id': emoji['_id']})
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removed_count += 1
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continue
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if 'embedding' not in emoji:
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logger.warning(f"发现过时记录（缺少embedding字段），ID: {emoji.get('_id', 'unknown')}")
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self.db.db.emoji.delete_one({'_id': emoji['_id']})
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removed_count += 1
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continue
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# 检查文件是否存在
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if not os.path.exists(emoji['path']):
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logger.warning(f"表情包文件已被删除: {emoji['path']}")
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# 从数据库中删除记录
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result = self.db.db.emoji.delete_one({'_id': emoji['_id']})
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if result.deleted_count > 0:
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logger.success(f"成功删除数据库记录: {emoji['_id']}")
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removed_count += 1
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else:
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logger.error(f"删除数据库记录失败: {emoji['_id']}")
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except Exception as item_error:
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logger.error(f"处理表情包记录时出错: {str(item_error)}")
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continue
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# 验证清理结果
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remaining_count = self.db.db.emoji.count_documents({})
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if removed_count > 0:
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logger.success(f"已清理 {removed_count} 个失效的表情包记录")
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logger.info(f"清理前总数: {total_count} | 清理后总数: {remaining_count}")
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else:
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logger.info(f"已检查 {total_count} 个表情包记录")
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except Exception as e:
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logger.error(f"检查表情包完整性失败: {str(e)}")
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logger.error(traceback.format_exc())
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async def start_periodic_check(self, interval_MINS: int = 120):
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while True:
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self.check_emoji_file_integrity()
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await asyncio.sleep(interval_MINS * 60)
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# 创建全局单例
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emoji_manager = EmojiManager() |