Merge pull request #251 from HYY1116/debug

fix: 尝试修复所有图片都被保存为jpg的问题，并以正确的格式请求识图api

src/plugins/chat/emoji_manager.py

@@ -6,6 +6,8 @@ import random
 import time
 import traceback
 from typing import Optional, Tuple
+from PIL import Image
+import io
 
 from loguru import logger
 from nonebot import get_driver
@@ -192,11 +194,11 @@ class EmojiManager:
             logger.error(f"获取标签失败: {str(e)}")
             return None
 
-    async def _check_emoji(self, image_base64: str) -> str:
+    async def _check_emoji(self, image_base64: str, image_format: str) -> str:
         try:
             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'
 
-            content, _ = await self.vlm.generate_response_for_image(prompt, image_base64)
+            content, _ = await self.vlm.generate_response_for_image(prompt, image_base64, image_format)
             logger.debug(f"输出描述: {content}")
             return content
 
@@ -237,7 +239,7 @@ class EmojiManager:
                 
                 image_bytes = base64.b64decode(image_base64)
                 image_hash = hashlib.md5(image_bytes).hexdigest()
-                
+                image_format = Image.open(io.BytesIO(image_bytes)).format.lower()
                 # 检查是否已经注册过
                 existing_emoji = self.db['emoji'].find_one({'filename': filename})
                 description = None
@@ -278,7 +280,7 @@ class EmojiManager:
 
 
                 if global_config.EMOJI_CHECK:
-                    check = await self._check_emoji(image_base64)
+                    check = await self._check_emoji(image_base64, image_format)
                     if '是' not in check:
                         os.remove(image_path)
                         logger.info(f"描述: {description}")

src/plugins/chat/utils_image.py

@@ -4,6 +4,8 @@ import time
 import aiohttp
 import hashlib
 from typing import Optional, Union
+from PIL import Image
+import io
 
 from loguru import logger
 from nonebot import get_driver
@@ -119,6 +121,7 @@ class ImageManager:
                     
             # 计算哈希值
             image_hash = hashlib.md5(image_bytes).hexdigest()
+            image_format = Image.open(io.BytesIO(image_bytes)).format.lower()
             
             # 查重
             existing = self.db.images.find_one({'hash': image_hash})
@@ -127,7 +130,7 @@ class ImageManager:
                 
             # 生成文件名和路径
             timestamp = int(time.time())
-            filename = f"{timestamp}_{image_hash[:8]}.jpg"
+            filename = f"{timestamp}_{image_hash[:8]}.{image_format}"
             file_path = os.path.join(self.IMAGE_DIR, filename)
             
             # 保存文件
@@ -238,6 +241,7 @@ class ImageManager:
             # 计算图片哈希
             image_bytes = base64.b64decode(image_base64)
             image_hash = hashlib.md5(image_bytes).hexdigest()
+            image_format = Image.open(io.BytesIO(image_bytes)).format.lower()
 
             # 查询缓存的描述
             cached_description = self._get_description_from_db(image_hash, 'emoji')
@@ -247,13 +251,13 @@ class ImageManager:
 
             # 调用AI获取描述
             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"
-            description, _ = await self._llm.generate_response_for_image(prompt, image_base64)
+            description, _ = await self._llm.generate_response_for_image(prompt, image_base64, image_format)
             
             # 根据配置决定是否保存图片
             if global_config.EMOJI_SAVE:
                 # 生成文件名和路径
                 timestamp = int(time.time())
-                filename = f"{timestamp}_{image_hash[:8]}.jpg"
+                filename = f"{timestamp}_{image_hash[:8]}.{image_format}"
                 file_path = os.path.join(self.IMAGE_DIR, 'emoji',filename)
                 
                 try:
@@ -293,6 +297,7 @@ class ImageManager:
             # 计算图片哈希
             image_bytes = base64.b64decode(image_base64)
             image_hash = hashlib.md5(image_bytes).hexdigest()
+            image_format = Image.open(io.BytesIO(image_bytes)).format.lower()
 
             # 查询缓存的描述
             cached_description = self._get_description_from_db(image_hash, 'image')
@@ -302,7 +307,7 @@ class ImageManager:
 
             # 调用AI获取描述
             prompt = "请用中文描述这张图片的内容。如果有文字，请把文字都描述出来。并尝试猜测这个图片的含义。最多200个字。"
-            description, _ = await self._llm.generate_response_for_image(prompt, image_base64)
+            description, _ = await self._llm.generate_response_for_image(prompt, image_base64, image_format)
             
             print(f"描述是{description}")
             
@@ -314,7 +319,7 @@ class ImageManager:
             if global_config.EMOJI_SAVE:
                 # 生成文件名和路径
                 timestamp = int(time.time())
-                filename = f"{timestamp}_{image_hash[:8]}.jpg"
+                filename = f"{timestamp}_{image_hash[:8]}.{image_format}"
                 file_path = os.path.join(self.IMAGE_DIR,'image', filename)
                 
                 try:

src/plugins/models/utils_model.py

@@ -104,6 +104,7 @@ class LLM_request:
             endpoint: str,
             prompt: str = None,
             image_base64: str = None,
+            image_format: str = None,
             payload: dict = None,
             retry_policy: dict = None,
             response_handler: callable = None,
@@ -115,6 +116,7 @@ class LLM_request:
             endpoint: API端点路径 (如 "chat/completions")
             prompt: prompt文本
             image_base64: 图片的base64编码
+            image_format: 图片格式
             payload: 请求体数据
             retry_policy: 自定义重试策略
             response_handler: 自定义响应处理器
@@ -151,7 +153,7 @@ class LLM_request:
 
         # 构建请求体
         if image_base64:
-            payload = await self._build_payload(prompt, image_base64)
+            payload = await self._build_payload(prompt, image_base64, image_format)
         elif payload is None:
             payload = await self._build_payload(prompt)
 
@@ -172,7 +174,7 @@ class LLM_request:
                             if response.status == 413:
                                 logger.warning("请求体过大，尝试压缩...")
                                 image_base64 = compress_base64_image_by_scale(image_base64)
-                                payload = await self._build_payload(prompt, image_base64)
+                                payload = await self._build_payload(prompt, image_base64, image_format)
                             elif response.status in [500, 503]:
                                 logger.error(f"错误码: {response.status} - {error_code_mapping.get(response.status)}")
                                 raise RuntimeError("服务器负载过高，模型恢复失败QAQ")
@@ -294,7 +296,7 @@ class LLM_request:
                 new_params["max_completion_tokens"] = new_params.pop("max_tokens")
         return new_params
 
-    async def _build_payload(self, prompt: str, image_base64: str = None) -> dict:
+    async def _build_payload(self, prompt: str, image_base64: str = None, image_format: str = None) -> dict:
         """构建请求体"""
         # 复制一份参数，避免直接修改 self.params
         params_copy = await self._transform_parameters(self.params)
@@ -306,7 +308,7 @@ class LLM_request:
                         "role": "user",
                         "content": [
                             {"type": "text", "text": prompt},
-                            {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_base64}"}}
+                            {"type": "image_url", "image_url": {"url": f"data:image/{image_format.lower()};base64,{image_base64}"}}
                         ]
                     }
                 ],
@@ -391,13 +393,14 @@ class LLM_request:
         )
         return content, reasoning_content
 
-    async def generate_response_for_image(self, prompt: str, image_base64: str) -> Tuple[str, str]:
+    async def generate_response_for_image(self, prompt: str, image_base64: str, image_format: str) -> Tuple[str, str]:
         """根据输入的提示和图片生成模型的异步响应"""
 
         content, reasoning_content = await self._execute_request(
             endpoint="/chat/completions",
             prompt=prompt,
-            image_base64=image_base64
+            image_base64=image_base64,
+            image_format=image_format
         )
         return content, reasoning_content