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Merge pull request #356 from SaigyoujiYusora/debug-fix-log

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fix: 优化发送错误时图片大喷射
This commit is contained in:
SengokuCola
2025-03-14 08:35:12 +08:00
committed by GitHub
parent de94c754f5 50ce8cf521
commit c2b31b4f1c
1 changed files with 30 additions and 25 deletions
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55
src/plugins/models/utils_model.py
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@@ -185,9 +185,9 @@ class LLM_request:
elif response.status in policy["abort_codes"]: elif response.status in policy["abort_codes"]:
logger.error(f"错误码: {response.status} - {error_code_mapping.get(response.status)}") logger.error(f"错误码: {response.status} - {error_code_mapping.get(response.status)}")
if response.status == 403: if response.status == 403:
#只针对硅基流动的V3和R1进行降级处理 # 只针对硅基流动的V3和R1进行降级处理
if self.model_name.startswith( if self.model_name.startswith(
"Pro/deepseek-ai") and self.base_url == "https://api.siliconflow.cn/v1/": "Pro/deepseek-ai") and self.base_url == "https://api.siliconflow.cn/v1/":
old_model_name = self.model_name old_model_name = self.model_name
self.model_name = self.model_name[4:] # 移除"Pro/"前缀 self.model_name = self.model_name[4:] # 移除"Pro/"前缀
logger.warning(f"检测到403错误，模型从 {old_model_name} 降级为 {self.model_name}") logger.warning(f"检测到403错误，模型从 {old_model_name} 降级为 {self.model_name}")
@@ -228,7 +228,7 @@ class LLM_request:
try: try:
chunk = json.loads(data_str) chunk = json.loads(data_str)
if flag_delta_content_finished: if flag_delta_content_finished:
usage = chunk.get("usage", None) # 获取tokn用量 usage = chunk.get("usage", None) # 获取tokn用量
else: else:
delta = chunk["choices"][0]["delta"] delta = chunk["choices"][0]["delta"]
delta_content = delta.get("content") delta_content = delta.get("content")
@@ -236,14 +236,14 @@ class LLM_request:
delta_content = "" delta_content = ""
accumulated_content += delta_content accumulated_content += delta_content
# 检测流式输出文本是否结束 # 检测流式输出文本是否结束
finish_reason = chunk["choices"][0].get("finish_reason") finish_reason = chunk["choices"][0].get("finish_reason")
if finish_reason == "stop": if finish_reason == "stop":
usage = chunk.get("usage", None) usage = chunk.get("usage", None)
if usage: if usage:
break break
# 部分平台在文本输出结束前不会返回token用量，此时需要再获取一次chunk # 部分平台在文本输出结束前不会返回token用量，此时需要再获取一次chunk
flag_delta_content_finished = True flag_delta_content_finished = True
except Exception: except Exception:
logger.exception("解析流式输出错误") logger.exception("解析流式输出错误")
content = accumulated_content content = accumulated_content
@@ -254,7 +254,8 @@ class LLM_request:
content = re.sub(r'<think>.*?</think>', '', content, flags=re.DOTALL).strip() content = re.sub(r'<think>.*?</think>', '', content, flags=re.DOTALL).strip()
# 构造一个伪result以便调用自定义响应处理器或默认处理器 # 构造一个伪result以便调用自定义响应处理器或默认处理器
result = { result = {
"choices": [{"message": {"content": content, "reasoning_content": reasoning_content}}], "usage": usage} "choices": [{"message": {"content": content, "reasoning_content": reasoning_content}}],
"usage": usage}
return response_handler(result) if response_handler else self._default_response_handler( return response_handler(result) if response_handler else self._default_response_handler(
result, user_id, request_type, endpoint) result, user_id, request_type, endpoint)
else: else:
@@ -270,6 +271,9 @@ class LLM_request:
await asyncio.sleep(wait_time) await asyncio.sleep(wait_time)
else: else:
logger.critical(f"请求失败: {str(e)}") logger.critical(f"请求失败: {str(e)}")
if image_base64:
payload["messages"][0]["content"][1]["image_url"][
"url"] = f"data:image/{image_format.lower()};base64,{image_base64[:10]}...{image_base64[-10:]}"
logger.critical(f"请求头: {await self._build_headers(no_key=True)} 请求体: {payload}") logger.critical(f"请求头: {await self._build_headers(no_key=True)} 请求体: {payload}")
raise RuntimeError(f"API请求失败: {str(e)}") raise RuntimeError(f"API请求失败: {str(e)}")
@@ -307,7 +311,8 @@ class LLM_request:
"role": "user", "role": "user",
"content": [ "content": [
{"type": "text", "text": prompt}, {"type": "text", "text": prompt},
{"type": "image_url", "image_url": {"url": f"data:image/{image_format.lower()};base64,{image_base64}"}} {"type": "image_url",
"image_url": {"url": f"data:image/{image_format.lower()};base64,{image_base64}"}}
] ]
} }
], ],
@@ -452,6 +457,7 @@ class LLM_request:
) )
return embedding return embedding
def compress_base64_image_by_scale(base64_data: str, target_size: int = 0.8 * 1024 * 1024) -> str: def compress_base64_image_by_scale(base64_data: str, target_size: int = 0.8 * 1024 * 1024) -> str:
"""压缩base64格式的图片到指定大小 """压缩base64格式的图片到指定大小
Args: Args:
@@ -463,36 +469,36 @@ def compress_base64_image_by_scale(base64_data: str, target_size: int = 0.8 * 10
try: try:
# 将base64转换为字节数据 # 将base64转换为字节数据
image_data = base64.b64decode(base64_data) image_data = base64.b64decode(base64_data)
# 如果已经小于目标大小，直接返回原图 # 如果已经小于目标大小，直接返回原图
if len(image_data) <= 2*1024*1024: if len(image_data) <= 2 * 1024 * 1024:
return base64_data return base64_data
# 将字节数据转换为图片对象 # 将字节数据转换为图片对象
img = Image.open(io.BytesIO(image_data)) img = Image.open(io.BytesIO(image_data))
# 获取原始尺寸 # 获取原始尺寸
original_width, original_height = img.size original_width, original_height = img.size
# 计算缩放比例 # 计算缩放比例
scale = min(1.0, (target_size / len(image_data)) ** 0.5) scale = min(1.0, (target_size / len(image_data)) ** 0.5)
# 计算新的尺寸 # 计算新的尺寸
new_width = int(original_width * scale) new_width = int(original_width * scale)
new_height = int(original_height * scale) new_height = int(original_height * scale)
# 创建内存缓冲区 # 创建内存缓冲区
output_buffer = io.BytesIO() output_buffer = io.BytesIO()
# 如果是GIF，处理所有帧 # 如果是GIF，处理所有帧
if getattr(img, "is_animated", False): if getattr(img, "is_animated", False):
frames = [] frames = []
for frame_idx in range(img.n_frames): for frame_idx in range(img.n_frames):
img.seek(frame_idx) img.seek(frame_idx)
new_frame = img.copy() new_frame = img.copy()
new_frame = new_frame.resize((new_width//2, new_height//2), Image.Resampling.LANCZOS) # 动图折上折 new_frame = new_frame.resize((new_width // 2, new_height // 2), Image.Resampling.LANCZOS) # 动图折上折
frames.append(new_frame) frames.append(new_frame)
# 保存到缓冲区 # 保存到缓冲区
frames[0].save( frames[0].save(
output_buffer, output_buffer,
@@ -506,23 +512,22 @@ def compress_base64_image_by_scale(base64_data: str, target_size: int = 0.8 * 10
else: else:
# 处理静态图片 # 处理静态图片
resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS) resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
# 保存到缓冲区，保持原始格式 # 保存到缓冲区，保持原始格式
if img.format == 'PNG' and img.mode in ('RGBA', 'LA'): if img.format == 'PNG' and img.mode in ('RGBA', 'LA'):
resized_img.save(output_buffer, format='PNG', optimize=True) resized_img.save(output_buffer, format='PNG', optimize=True)
else: else:
resized_img.save(output_buffer, format='JPEG', quality=95, optimize=True) resized_img.save(output_buffer, format='JPEG', quality=95, optimize=True)
# 获取压缩后的数据并转换为base64 # 获取压缩后的数据并转换为base64
compressed_data = output_buffer.getvalue() compressed_data = output_buffer.getvalue()
logger.success(f"压缩图片: {original_width}x{original_height} -> {new_width}x{new_height}") logger.success(f"压缩图片: {original_width}x{original_height} -> {new_width}x{new_height}")
logger.info(f"压缩前大小: {len(image_data)/1024:.1f}KB, 压缩后大小: {len(compressed_data)/1024:.1f}KB") logger.info(f"压缩前大小: {len(image_data) / 1024:.1f}KB, 压缩后大小: {len(compressed_data) / 1024:.1f}KB")
return base64.b64encode(compressed_data).decode('utf-8') return base64.b64encode(compressed_data).decode('utf-8')
except Exception as e: except Exception as e:
logger.error(f"压缩图片失败: {str(e)}") logger.error(f"压缩图片失败: {str(e)}")
import traceback import traceback
logger.error(traceback.format_exc()) logger.error(traceback.format_exc())
return base64_data return base64_data