866 lines
29 KiB
Python
866 lines
29 KiB
Python
"""
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工具函数模块
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整合错误处理、图像处理、常量定义等通用工具
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"""
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import json
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import base64
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import sys
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import os
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import shutil
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import re
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import io
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from io import BytesIO
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from PIL import Image
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from typing import Optional, Dict, Any
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import time
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import random
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import threading
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# 修复 Windows 终端编码问题
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# 解决 GBK 编码导致的 emoji 和特殊字符输出错误
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if sys.platform == 'win32' and sys.stdout.encoding != 'utf-8':
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try:
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sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', errors='replace')
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sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8', errors='replace')
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except Exception:
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pass # 静默失败，保持原有编码
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# ==================== 统一显示宽度计算 ====================
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def get_display_width(text: str) -> int:
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"""
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计算字符串在终端中的显示宽度（中文及部分 emoji 占2格，ASCII 占1格）
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"""
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width = 0
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for char in text:
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# 常见的中文字符编码范围
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if ord(char) > 0x7F:
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width += 2
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else:
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width += 1
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return width
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# ==================== 统一日志前缀常量 ====================
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# 所有模块从此处导入，确保日志格式一致
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PREFIX = "✨"
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ERROR_PREFIX = "✨-❌"
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PROCESS_PREFIX = "✨"
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REQUEST_PREFIX = "✨"
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WARN_PREFIX = "✨-⚠️"
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# ==================== 任务类型常量 ====================
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TASK_TRANSLATE = "翻译"
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TASK_EXPAND = "提示词优化"
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TASK_IMAGE_CAPTION = "图像反推"
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TASK_VIDEO_CAPTION = "视频反推"
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# ==================== 请求来源常量 ====================
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SOURCE_NODE = "节点-"
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SOURCE_FRONTEND = "前端-"
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# ==================== 统一日志消息函数 ====================
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def log_prepare(
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task_type: str,
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request_id: str,
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source: str,
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service_name: str,
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model_name: str = None,
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rule_name: str = None,
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extra: dict = None
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) -> None:
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"""
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输出统一格式的准备日志（换行输出）
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格式: ✨ 🟡 {来源}{任务}准备 | 服务:{service} | 模型:{model} | 规则:{rule} | ID:{id}
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"""
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# 强制回到行首并清除当前行，确保不与之前的 progress 冲突
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print(f"\r{_ANSI_CLEAR_EOL}", end="")
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parts = [f"{PREFIX} 🟡 {source}{task_type}准备"]
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parts.append(f"服务:{service_name}")
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if model_name:
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parts.append(f"模型:{model_name}")
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if rule_name:
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parts.append(f"规则:{rule_name}")
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parts.append(f"ID:{request_id}")
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# 处理额外字段
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if extra:
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for key, value in extra.items():
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parts.append(f"{key}:{value}")
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print(f"{parts[0]} | {' | '.join(parts[1:])}", flush=True)
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def log_complete(
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task_type: str,
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request_id: str,
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service_name: str,
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char_count: int,
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elapsed_ms: int,
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model_unloaded: bool = None,
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source: str = None
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) -> None:
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"""
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输出统一格式的完成日志（换行输出）
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格式: ✨ ✅ {来源}{任务}完成 | 服务:{service} | ID:{id} | 字符:{count} | 耗时:{time}
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"""
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# 强制回到行首且不换行清空当前行，然后输出新消息
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print(f"\r{_ANSI_CLEAR_EOL}", end="")
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elapsed_str = format_elapsed_time(elapsed_ms)
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source_str = source if source else ""
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parts = [f"{PREFIX} ✅ {source_str}{task_type}完成"]
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parts.append(f"服务:{service_name}")
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parts.append(f"ID:{request_id}")
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parts.append(f"字符:{char_count}")
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parts.append(f"耗时:{elapsed_str}")
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# Ollama 模型卸载状态
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if model_unloaded is not None:
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unload_text = "模型已卸载" if model_unloaded else "模型保留"
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parts.append(unload_text)
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print(f"{parts[0]} | {' | '.join(parts[1:])}", flush=True)
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def log_error(
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task_type: str,
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request_id: str,
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error_msg: str,
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source: str = None
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) -> None:
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"""
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输出统一格式的错误日志（换行输出）
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"""
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# 强制回到行首并清除当前行
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print(f"\r{_ANSI_CLEAR_EOL}", end="")
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source_str = source if source else ""
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print(f"{PREFIX} ❌ {source_str}{task_type}失败 | ID:{request_id} | 错误:{error_msg}", flush=True)
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def generate_request_id(req_type: str, service_type: Optional[str] = None, node_id: str = "0") -> str:
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"""
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生成统一格式的请求ID
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格式: 请求类型_服务类型(可选)_NodeID_四位时间戳
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示例: trans_llm_12_3456
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"""
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timestamp = str(int(time.time()))[-4:]
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parts = [req_type]
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if service_type:
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parts.append(service_type)
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parts.append(str(node_id))
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parts.append(timestamp)
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return "_".join(parts)
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# ---日志格式化辅助函数---
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def simplify_model_name(model: str) -> str:
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"""
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简化模型名称显示
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示例:
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huihui_ai/qwen3-vl-abliterated:8b -> qwen3-vl-8b
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huihui_ai/qwen3-abliterated:14b -> qwen3-14b
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参数:
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model: 完整模型名称
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返回:
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简化后的模型名称
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"""
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if '/' in model:
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model = model.split('/')[-1]
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if ':' in model:
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name, size = model.split(':')
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# 移除常见后缀
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name = name.replace('-abliterated', '').replace('-instruct', '').replace('-chat', '')
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return f"{name}-{size}"
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return model
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def format_model_with_thinking(model: str, thinking_disabled: bool = False) -> str:
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"""
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格式化模型名称，如果关闭思维链则添加🗯标识
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参数:
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model: 模型名称
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thinking_disabled: 是否关闭思维链
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返回:
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格式化后的模型名称
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"""
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simplified = simplify_model_name(model)
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if thinking_disabled:
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return f"{simplified}💭"
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return simplified
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def format_elapsed_time(elapsed_ms: int) -> str:
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"""
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格式化耗时显示
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参数:
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elapsed_ms: 毫秒数
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返回:
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格式化后的时间字符串（如 "6.5s"）
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"""
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return f"{elapsed_ms/1000:.1f}s"
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# ====================进度日志系统====================
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# 统一的进度条管理器，支持单行覆盖刷新
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# ---ANSI 控制序列---
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_ANSI_CLEAR_EOL = "\033[K" # 清除从光标位置到行末的内容
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# ---全局状态：追踪上一次输出长度（使用锁保护以支持并发）---
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_global_last_output_len = 0
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_progress_lock = threading.Lock()
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# ---Windows 虚拟终端初始化---
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def _enable_windows_vt():
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"""
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启用 Windows 虚拟终端处理
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解决 Windows CMD/PowerShell 中 ANSI 转义序列兼容性问题
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"""
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if os.name == 'nt':
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try:
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import ctypes
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kernel32 = ctypes.windll.kernel32
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handle = kernel32.GetStdHandle(-11) # STD_OUTPUT_HANDLE
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mode = ctypes.c_ulong()
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kernel32.GetConsoleMode(handle, ctypes.byref(mode))
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kernel32.SetConsoleMode(handle, mode.value | 0x0004) # ENABLE_VIRTUAL_TERMINAL_PROCESSING
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except Exception:
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pass
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_enable_windows_vt()
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class ProgressBar:
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"""
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统一进度条管理器
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管理请求的完整生命周期：等待 → 生成 → 完成
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通过 streaming 参数控制刷新频率：
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- streaming=True: 高频刷新（每次更新都刷新）
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- streaming=False: 仅在状态变化时刷新（等待→生成→完成）
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两种模式都使用单行覆盖（\r），区别仅在于刷新频率
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"""
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# 状态常量
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STATE_WAITING = "waiting"
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STATE_GENERATING = "generating"
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STATE_DONE = "done"
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def __init__(
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self,
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request_id: str,
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service_name: str,
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extra_info: str = None,
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streaming: bool = True,
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task_type: str = None,
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source: str = None
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):
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"""
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创建进度条
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参数:
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request_id: 请求ID
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service_name: 服务名称（如 Ollama, OpenAI）
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extra_info: 额外信息（如 Context:2048 | Timeout:60s）
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streaming: True=高频刷新，False=仅状态变化时刷新
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task_type: 任务类型（用于统一日志）
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source: 来源（前端/节点）
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"""
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self._request_id = request_id
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self._service_name = service_name
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self._extra_info = extra_info
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self._streaming = streaming
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self._task_type = task_type
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self._source = source
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self._state = self.STATE_WAITING
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self._char_count = 0
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self._start_time = time.perf_counter()
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self._closed = False
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self._stop_event = threading.Event()
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self._timer_thread = None
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# 1. 重置全局长度，开始新一轮进度跟踪
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with _progress_lock:
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global _global_last_output_len
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_global_last_output_len = 0
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# 立即显示"等待响应"
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self._refresh()
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# 仅在流式模式下启动定时刷新线程（非流式模式采用静态日志，无需跳秒刷新）
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if self._streaming:
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self._timer_thread = threading.Thread(target=self._timer_loop, daemon=True)
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self._timer_thread.start()
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def _format_elapsed(self) -> str:
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"""格式化耗时"""
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elapsed_sec = time.perf_counter() - self._start_time
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if elapsed_sec < 60:
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return f"{elapsed_sec:.1f}s"
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else:
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minutes = int(elapsed_sec // 60)
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seconds = int(elapsed_sec % 60)
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return f"{minutes}m{seconds}s"
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def _render(self) -> str:
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"""渲染当前进度条内容"""
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elapsed = self._format_elapsed()
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if self._state == self.STATE_WAITING:
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# 等待响应：✨ 🟠 等待Ollama响应...
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# 流式模式下添加计时，非流式模式保持静态
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base = f"{PREFIX} 🟠 等待{self._service_name}响应..."
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if not self._streaming:
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return base
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if self._extra_info:
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return f"{base} | {self._extra_info} | {elapsed}"
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else:
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return f"{base} | {elapsed}"
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elif self._state == self.STATE_GENERATING:
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# 流式模式：显示字符数和时间
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# 静态模式：只显示简单的 "生成中..."
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if self._streaming:
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return f"{PREFIX} 🔵 生成中 | {self._char_count}字符 | {elapsed}"
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else:
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return f"{PREFIX} 🔵 生成中..."
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else:
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return ""
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def _refresh(self) -> None:
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"""内部刷新方法：单行覆盖输出"""
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if self._closed:
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return
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output = self._render()
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if not output:
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return
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with _progress_lock:
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global _global_last_output_len
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# 计算当前内容的显示宽度（解决中文/emoji 导致的 len() 不准问题）
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current_width = get_display_width(output)
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# 用空格填充以覆盖上一次更长的输出（兜底方案，应对 ANSI 失效）
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padding = ""
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if _global_last_output_len > current_width:
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padding = " " * (_global_last_output_len - current_width)
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# 使用 \r 回到行首，先发一次 ANSI 清行（如果环境支持，瞬间清空）
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# 再输出内容 + 空格填充（应对 ANSI 失效）+ 再次清行（防止尾部残留）
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# 增加 2 个空格缓冲避免与其他日志粘连
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print(f"\r{_ANSI_CLEAR_EOL}{output}{padding}{_ANSI_CLEAR_EOL} ", end='', flush=True)
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# 记录本次显示的宽度（包含缓冲空格）
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_global_last_output_len = current_width + len(padding)
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def _stop_timer(self):
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"""停止计时器线程"""
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self._stop_event.set()
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# 强制将状态标为已关闭，防止重入
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self._closed = True
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def _timer_loop(self):
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"""后台线程：仅在流式模式下定期刷新计时"""
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try:
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while not self._stop_event.is_set() and not self._closed:
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# 定时刷新当前内容（主要用于更新 WAITING 阶段的时间）
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self._refresh()
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# 每 0.1 秒刷新一次
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if self._stop_event.wait(0.1):
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break
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except Exception:
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pass # 守护线程异常不应影响主流程
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def set_generating(self, char_count: int = 0) -> None:
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"""
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切换到"生成中"状态
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参数:
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char_count: 当前字符数
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"""
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if self._closed or self._state == self.STATE_GENERATING:
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return
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self._state = self.STATE_GENERATING
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self._char_count = char_count
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self._refresh() # 状态变化时总是刷新
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def update(self, char_count: int) -> None:
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"""
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更新字符数
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流式模式：每次调用都刷新
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静态模式：不刷新（避免刷屏）
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参数:
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char_count: 当前字符数
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"""
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if self._closed:
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return
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self._char_count = char_count
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# 流式模式：高频刷新
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if self._streaming:
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self._refresh()
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# 静态模式：不在这里刷新，只有状态变化时才刷新
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def done(self, message: str = None, char_count: int = None, elapsed_ms: int = None) -> None:
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"""
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完成请求
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参数:
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message: 自定义完成消息（可选）
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char_count: 最终字符数（可选）
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elapsed_ms: 耗时毫秒（可选，不传则自动计算）
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"""
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if self._closed:
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return
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self._stop_timer()
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self._state = self.STATE_DONE
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# 重置全局长度
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with _progress_lock:
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global _global_last_output_len
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_global_last_output_len = 0
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# 如果提供了 task_type，则使用统一日志
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if hasattr(self, '_task_type') and self._task_type:
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log_complete(
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self._task_type or "任务",
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self._request_id,
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self._service_name,
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char_count if char_count is not None else self._char_count,
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elapsed_ms if elapsed_ms is not None else int((time.perf_counter() - self._start_time) * 1000),
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source=getattr(self, '_source', None)
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)
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return
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# 降级兼容：原始 done 逻辑
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# 计算耗时
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if elapsed_ms is not None:
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elapsed = format_elapsed_time(elapsed_ms)
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else:
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elapsed = self._format_elapsed()
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# 使用传入的字符数或当前记录的字符数
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final_count = char_count if char_count is not None else self._char_count
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# 生成完成消息
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if message:
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final_msg = message
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else:
|
||
final_msg = f"{PREFIX} ✅ 完成 | 服务:{self._service_name} | ID:{self._request_id} | 字符:{final_count} | 耗时:{elapsed}"
|
||
|
||
# 直接调用 log_complete 的思想：换行输出，不覆盖之前的内容
|
||
print(f"\r{_ANSI_CLEAR_EOL}{final_msg}", flush=True)
|
||
|
||
def error(self, message: str) -> None:
|
||
"""
|
||
输出错误消息（换行输出，不覆盖）
|
||
|
||
参数:
|
||
message: 错误消息
|
||
"""
|
||
if self._closed:
|
||
return
|
||
|
||
self._stop_timer()
|
||
|
||
|
||
# 重置全局长度
|
||
with _progress_lock:
|
||
global _global_last_output_len
|
||
_global_last_output_len = 0
|
||
|
||
# 如果提供了 task_type，则使用统一日志
|
||
if hasattr(self, '_task_type') and self._task_type:
|
||
log_error(self._task_type or "任务", self._request_id, message, source=getattr(self, '_source', None))
|
||
return
|
||
|
||
# 降级模式
|
||
print(f"\r{_ANSI_CLEAR_EOL}{message}", flush=True)
|
||
|
||
def cancel(self, message: str = None) -> None:
|
||
"""
|
||
取消请求（换行输出，不覆盖）
|
||
|
||
参数:
|
||
message: 自定义取消消息（可选）
|
||
"""
|
||
if self._closed:
|
||
return
|
||
|
||
self._stop_timer()
|
||
|
||
|
||
# 重置全局长度
|
||
with _progress_lock:
|
||
global _global_last_output_len
|
||
_global_last_output_len = 0
|
||
|
||
cancel_msg = message or "任务被取消"
|
||
|
||
# 如果提供了 task_type，则使用统一日志
|
||
if hasattr(self, '_task_type') and self._task_type:
|
||
log_error(self._task_type or "任务", self._request_id, cancel_msg, source=getattr(self, '_source', None))
|
||
return
|
||
|
||
# 降级模式
|
||
print(f"\r{_ANSI_CLEAR_EOL}{WARN_PREFIX} {cancel_msg} | ID:{self._request_id}", flush=True)
|
||
|
||
def __enter__(self):
|
||
return self
|
||
|
||
def __exit__(self, *args):
|
||
if not self._closed:
|
||
# 退出上下文时，如果没有显式调用 done/error，则视为成功完成
|
||
self.done()
|
||
|
||
def __del__(self):
|
||
"""析构函数：确保对象被回收时停止计时器"""
|
||
try:
|
||
# 仅在计时器还在运行时尝试停止
|
||
if hasattr(self, '_stop_event') and not self._stop_event.is_set():
|
||
self._stop_timer()
|
||
except:
|
||
pass
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
# HTTP状态码到中文错误信息的映射
|
||
HTTP_STATUS_CODE_MESSAGES = {
|
||
400: "请求无效",
|
||
401: "身份验证失败-请检查您的API Key是否正确。",
|
||
403: "无权限访问-您没有权限访问此资源。",
|
||
404: "请求的资源不存在",
|
||
429: "请求频率过高-您已超出速率限制，请稍后再试。",
|
||
500: "服务器内部错误- 服务提供商端发生未知问题。",
|
||
502: "网关错误",
|
||
503: "服务不可用- 服务器当前无法处理请求，请稍后重试。",
|
||
504: "网关超时",
|
||
}
|
||
|
||
# 百度翻译API的错误码映射
|
||
BAIDU_ERROR_CODE_MESSAGES = {
|
||
'52001': '请求超时，请重试',
|
||
'52002': '系统错误，请重试',
|
||
'52003': '未授权用户，请检查appid是否正确或服务是否开通',
|
||
'54000': '必填参数为空，请检查是否少传参数',
|
||
'54001': '签名错误，请检查appid和secret_key是否正确',
|
||
'54003': '访问频率受限，请降低您的调用频率，或进行身份认证后切换为高级版/尊享版',
|
||
'54004': '账户余额不足，请前往管理控制台充值',
|
||
'54005': '长query请求频繁，请降低长query的发送频率，3s后再试',
|
||
'58000': '客户端IP非法，检查个人资料里填写的IP地址是否正确，可前往开发者信息-基本信息修改',
|
||
'58001': '译文语言方向不支持，检查译文语言是否在语言列表里',
|
||
'58002': '服务当前已关闭，请前往百度管理控制台开启服务',
|
||
'58003': '此IP已被封禁',
|
||
'90107': '认证未通过或未生效，请前往我的认证查看认证进度',
|
||
'20003': '请求内容存在安全风险',
|
||
}
|
||
|
||
|
||
# ---错误处理函数---
|
||
|
||
def _is_auth_error(error_text: str) -> bool:
|
||
"""
|
||
检查错误信息是否为认证相关错误
|
||
|
||
参数:
|
||
error_text: 错误文本（小写）
|
||
|
||
返回:
|
||
bool: 是否为认证错误
|
||
"""
|
||
auth_keywords = [
|
||
'invalid token',
|
||
'authorization',
|
||
'authenticate',
|
||
'api key',
|
||
'api_key',
|
||
'unauthorized',
|
||
'auth failed',
|
||
'invalid key',
|
||
'missing key',
|
||
'invalid credentials',
|
||
'身份验证',
|
||
'认证失败',
|
||
'token'
|
||
]
|
||
return any(keyword in error_text for keyword in auth_keywords)
|
||
|
||
def format_api_error(e: Exception, provider_display_name: str) -> str:
|
||
"""
|
||
格式化来自API的错误信息
|
||
纯httpx实现，不依赖openai库
|
||
|
||
参数:
|
||
e: 异常对象
|
||
provider_display_name: 服务商显示名称
|
||
|
||
返回:
|
||
str: 格式化后的错误信息
|
||
"""
|
||
# 处理httpx的HTTP错误
|
||
try:
|
||
import httpx
|
||
if isinstance(e, httpx.HTTPStatusError):
|
||
status_code = e.response.status_code
|
||
message = HTTP_STATUS_CODE_MESSAGES.get(status_code, "未知HTTP错误")
|
||
|
||
error_details_str = ""
|
||
detail_msg = ""
|
||
|
||
try:
|
||
error_details = e.response.json()
|
||
detail_msg = error_details.get("message", "")
|
||
if isinstance(error_details.get("error"), dict):
|
||
detail_msg = error_details["error"].get("message", detail_msg)
|
||
|
||
if detail_msg:
|
||
error_details_str = f" | 详情: {detail_msg}"
|
||
except (json.JSONDecodeError, AttributeError):
|
||
try:
|
||
if hasattr(e.response, 'text') and e.response.text:
|
||
detail_msg = e.response.text[:200]
|
||
error_details_str = f" | 原始响应: {detail_msg}"
|
||
except Exception:
|
||
pass
|
||
|
||
# ---智能识别认证错误并提供友好提示---
|
||
combined_error_text = f"{message} {detail_msg}".lower()
|
||
if status_code == 401 or _is_auth_error(combined_error_text):
|
||
return f"{provider_display_name} 认证失败: 未配置API Key或API Key无效，请在服务商配置中填写正确的API Key"
|
||
|
||
return f"{provider_display_name} API错误: {message} (状态码: {status_code}){error_details_str}"
|
||
except Exception:
|
||
pass
|
||
|
||
# 对于其他类型的异常，返回其类型和基本信息
|
||
return f"{provider_display_name} 服务请求异常: ({type(e).__name__}) {str(e)}"
|
||
|
||
|
||
def format_baidu_translate_error(error_data: dict) -> str:
|
||
"""
|
||
格式化百度翻译API的错误信息
|
||
|
||
参数:
|
||
error_data: 百度API返回的错误数据
|
||
|
||
返回:
|
||
str: 格式化后的错误信息
|
||
"""
|
||
if not isinstance(error_data, dict):
|
||
return "未知的百度翻译错误格式"
|
||
|
||
error_code = str(error_data.get('error_code'))
|
||
if error_code in BAIDU_ERROR_CODE_MESSAGES:
|
||
return f"百度翻译错误: {BAIDU_ERROR_CODE_MESSAGES[error_code]} (代码: {error_code})"
|
||
|
||
error_msg = error_data.get('error_msg', '未知错误')
|
||
return f"百度翻译错误: {error_msg} (代码: {error_code})"
|
||
|
||
|
||
# ---图像处理函数---
|
||
|
||
def get_optimal_image_params(image_count: int = 1) -> tuple:
|
||
"""
|
||
根据图像数量智能计算最佳的分辨率和质量参数
|
||
目标：保证API能返回完整结果，同时尽可能保持图像质量
|
||
|
||
参数:
|
||
image_count: 图像数量 (1-32)
|
||
|
||
返回:
|
||
tuple: (max_size: tuple, quality: int, compression_level: str)
|
||
"""
|
||
if image_count <= 1:
|
||
# 单图：使用中等质量
|
||
return (1024, 1024), 75, "中等"
|
||
elif image_count <= 3:
|
||
# 1-3帧：保持较高质量
|
||
return (1024, 1024), 70, "较高"
|
||
elif image_count <= 6:
|
||
# 4-6帧：降低分辨率，保持中等质量
|
||
return (768, 768), 70, "中等"
|
||
elif image_count <= 10:
|
||
# 7-10帧：进一步降低分辨率和质量
|
||
return (640, 640), 65, "较低"
|
||
elif image_count <= 16:
|
||
# 11-16帧：使用低分辨率
|
||
return (512, 512), 60, "低"
|
||
else:
|
||
# 17-32帧：最大压缩，保证能处理
|
||
return (480, 480), 55, "极低"
|
||
|
||
|
||
def preprocess_image(
|
||
image_data: str,
|
||
max_size: tuple = None, # 改为可选，支持自动计算
|
||
quality: int = None, # 改为可选，支持自动计算
|
||
request_id: Optional[str] = None,
|
||
silent: bool = False,
|
||
image_count: int = 1 # 新增：总图像数量，用于动态调整
|
||
) -> str:
|
||
"""
|
||
预处理图像数据（压缩和调整大小）
|
||
|
||
参数:
|
||
image_data: Base64编码的图像数据
|
||
max_size: 最大尺寸，默认为None（自动计算）
|
||
quality: JPEG压缩质量 (1-100)，默认为None（自动计算）
|
||
request_id: 请求ID，用于日志输出
|
||
silent: 是否静默模式（不输出日志）
|
||
image_count: 总图像数量，用于多图场景的智能优化
|
||
|
||
返回:
|
||
str: 处理后的图像数据
|
||
"""
|
||
try:
|
||
# 智能计算最佳参数
|
||
if max_size is None or quality is None:
|
||
optimal_size, optimal_quality, compression_level = get_optimal_image_params(image_count)
|
||
max_size = max_size or optimal_size
|
||
quality = quality or optimal_quality
|
||
else:
|
||
compression_level = "自定义"
|
||
|
||
# 检查是否为base64编码的图像数据
|
||
if image_data.startswith('data:image'):
|
||
# 提取base64数据
|
||
header, encoded = image_data.split(",", 1)
|
||
image_bytes = base64.b64decode(encoded)
|
||
original_bytes = len(image_bytes)
|
||
|
||
# 打开图像
|
||
img = Image.open(BytesIO(image_bytes))
|
||
original_size = img.size
|
||
|
||
# 计算缩放比例
|
||
if img.size[0] > max_size[0] or img.size[1] > max_size[1]:
|
||
img.thumbnail(max_size, Image.Resampling.LANCZOS)
|
||
|
||
# 转换为RGB（如果是RGBA）
|
||
if img.mode in ('RGBA', 'LA', 'P'):
|
||
background = Image.new('RGB', img.size, (255, 255, 255))
|
||
if img.mode == 'P':
|
||
img = img.convert('RGBA')
|
||
background.paste(img, mask=img.split()[-1] if img.mode in ('RGBA', 'LA') else None)
|
||
img = background
|
||
|
||
# 压缩图像
|
||
buffer = BytesIO()
|
||
img.save(buffer, format="JPEG", quality=quality, optimize=True)
|
||
compressed_bytes = buffer.getvalue()
|
||
compressed_size = len(compressed_bytes)
|
||
|
||
# 编码为base64
|
||
compressed_b64 = base64.b64encode(compressed_bytes).decode('utf-8')
|
||
processed_image_data = f"data:image/jpeg;base64,{compressed_b64}"
|
||
|
||
# 输出日志
|
||
if not silent:
|
||
compression_ratio = (1 - compressed_size / original_bytes) * 100 if original_bytes > 0 else 0
|
||
|
||
# 多图场景显示压缩等级
|
||
if image_count > 1:
|
||
print(
|
||
f"{REQUEST_PREFIX} 🟡 图像预处理 | "
|
||
f"尺寸:{original_size}→{img.size} | "
|
||
f"大小:{original_bytes/1024:.1f}KB→{compressed_size/1024:.1f}KB | "
|
||
f"压缩率:{compression_ratio:.1f}% | "
|
||
f"等级:{compression_level} ({image_count}帧)"
|
||
)
|
||
else:
|
||
print(
|
||
f"{REQUEST_PREFIX} 🟡 图像预处理完成 | "
|
||
f"尺寸:{original_size}→{img.size} | "
|
||
f"大小:{original_bytes/1024:.1f}KB→{compressed_size/1024:.1f}KB | "
|
||
f"压缩率:{compression_ratio:.1f}%"
|
||
)
|
||
|
||
return processed_image_data
|
||
|
||
# 如果不是base64编码的图像数据，直接返回
|
||
return image_data
|
||
|
||
except Exception as e:
|
||
if not silent:
|
||
print(f"{WARN_PREFIX} ❌图像预处理失败 | 请求ID:{request_id} | 错误:{str(e)}")
|
||
# 预处理失败时返回原始图像数据
|
||
return image_data
|
||
|
||
|
||
def check_multi_image_support(provider: str, model: str) -> tuple:
|
||
"""
|
||
检查服务商是否支持多图像分析
|
||
|
||
参数:
|
||
provider: 服务商标识
|
||
model: 模型名称
|
||
|
||
返回:
|
||
tuple: (支持多图像: bool, 最大图像数: int)
|
||
"""
|
||
model_lower = (model or "").lower()
|
||
|
||
# Gemini系列：支持多图像
|
||
if "gemini" in model_lower or "google" in model_lower:
|
||
return (True, 3000)
|
||
|
||
# 智谱GLM系列视觉模型
|
||
# GLM-4.6V系列：128K上下文，支持大量多图（无官方硬限制）
|
||
if "glm" in model_lower and "4.6v" in model_lower:
|
||
return (True, 100)
|
||
|
||
# GLM-4V系列（4V-Plus等）：16K上下文，最多5张
|
||
if "glm" in model_lower and ("4v" in model_lower or "vision" in model_lower):
|
||
return (True, 5)
|
||
|
||
# Qwen系列：支持多图像
|
||
if "qwen" in model_lower and ("vl" in model_lower or "vision" in model_lower):
|
||
return (True, 100)
|
||
|
||
# OpenAI GPT-4V及兼容模型：支持多图像
|
||
if "gpt-4" in model_lower and ("vision" in model_lower or "v" in model_lower or "turbo" in model_lower):
|
||
return (True, 100)
|
||
|
||
# 其他OpenAI兼容的视觉模型
|
||
if any(keyword in model_lower for keyword in ["vision", "visual", "vl", "multimodal"]):
|
||
return (True, 10)
|
||
|
||
# 默认：不支持多图像
|
||
return (False, 0)
|