v0.4.0 支持任意替换的模型，改进配置文件
好好好好好好好好好
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SengokuCola
@@ -14,6 +14,8 @@ import asyncio
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import time
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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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driver = get_driver()
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config = driver.config
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@@ -43,6 +45,7 @@ class EmojiManager:
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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=50)
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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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@@ -87,55 +90,23 @@ class EmojiManager:
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print(f"\033[1;31m[错误]\033[0m 记录表情使用失败: {str(e)}")
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async def _get_emotion_from_text(self, text: str) -> List[str]:
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"""从文本中识别情感关键词，使用DeepSeek API进行分析
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"""从文本中识别情感关键词
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Args:
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text: 输入文本
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Returns:
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List[str]: 匹配到的情感标签列表
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"""
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try:
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# 准备请求数据
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {config.siliconflow_key}"
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}
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prompt = f'分析这段文本："{text}"，从"happy,angry,sad,surprised,disgusted,fearful,neutral"中选出最匹配的1个情感标签。只需要返回标签，不要输出其他任何内容。'
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payload = {
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"model": "deepseek-ai/DeepSeek-V3",
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"messages": [
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": f'分析这段文本："{text}"，从"happy,angry,sad,surprised,disgusted,fearful,neutral"中选出最匹配的1个情感标签。只需要返回标签，不要输出其他任何内容。'
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}
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]
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}
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],
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"max_tokens": 50,
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"temperature": 0.3
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}
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content, _ = await self.llm.generate_response(prompt)
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emotion = content.strip().lower()
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async with aiohttp.ClientSession() as session:
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async with session.post(
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f"{config.siliconflow_base_url}chat/completions",
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headers=headers,
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json=payload
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) as response:
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if response.status != 200:
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print(f"\033[1;31m[错误]\033[0m API请求失败: {await response.text()}")
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return ['neutral']
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result = json.loads(await response.text())
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if "choices" in result and len(result["choices"]) > 0:
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emotion = result["choices"][0]["message"]["content"].strip().lower()
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# 确保返回的标签是有效的
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if emotion in self.EMOTION_KEYWORDS:
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print(f"\033[1;32m[成功]\033[0m 识别到的情感: {emotion}")
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return [emotion] # 返回单个情感标签的列表
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if emotion in self.EMOTION_KEYWORDS:
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print(f"\033[1;32m[成功]\033[0m 识别到的情感: {emotion}")
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return [emotion]
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return ['neutral'] # 如果无法识别情感，返回neutral
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return ['neutral']
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except Exception as e:
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print(f"\033[1;31m[错误]\033[0m 情感分析失败: {str(e)}")
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@@ -250,52 +221,20 @@ class EmojiManager:
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async def _get_emoji_tag(self, image_base64: str) -> str:
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"""获取表情包的标签"""
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async with aiohttp.ClientSession() as session:
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {config.siliconflow_key}"
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}
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try:
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prompt = '这是一个表情包，请从"happy", "angry", "sad", "surprised", "disgusted", "fearful", "neutral"中选出1个情感标签。只输出标签，不要输出其他任何内容，只输出情感标签就好'
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payload = {
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"model": "deepseek-ai/deepseek-vl2",
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"messages": [
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": '这是一个表情包，请从"happy", "angry", "sad", "surprised", "disgusted", "fearful", "neutral"中选出1个情感标签。只输出标签，不要输出其他任何内容，只输出情感标签就好'
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},
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{image_base64}"
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}
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}
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]
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}
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],
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"max_tokens": 60,
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"temperature": 0.3
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}
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content, _ = await self.llm.generate_response_for_image(prompt, image_base64)
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tag_result = content.strip().lower()
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async with session.post(
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f"{config.siliconflow_base_url}chat/completions",
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headers=headers,
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json=payload
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) as response:
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if response.status == 200:
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result = await response.json()
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if "choices" in result and len(result["choices"]) > 0:
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tag_result = result["choices"][0]["message"]["content"].strip().lower()
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valid_tags = ["happy", "angry", "sad", "surprised", "disgusted", "fearful", "neutral"]
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for tag_match in valid_tags:
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if tag_match in tag_result or tag_match == tag_result:
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return tag_match
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print(f"\033[1;33m[警告]\033[0m 无效的标签: {tag_match}, 跳过")
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else:
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print(f"\033[1;31m[错误]\033[0m 获取标签失败, 状态码: {response.status}")
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valid_tags = ["happy", "angry", "sad", "surprised", "disgusted", "fearful", "neutral"]
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for tag_match in valid_tags:
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if tag_match in tag_result or tag_match == tag_result:
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return tag_match
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print(f"\033[1;33m[警告]\033[0m 无效的标签: {tag_result}, 跳过")
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except Exception as e:
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print(f"\033[1;31m[错误]\033[0m 获取标签失败: {str(e)}")
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print(f"\033[1;32m[调试信息]\033[0m 使用默认标签: neutral")
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return "skip" # 默认标签
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