""" Internationalization (i18n) module for kt-cli. Supports English and Chinese languages, with automatic detection based on system locale or KT_LANG environment variable. """ import os from typing import Any # Message definitions for all supported languages MESSAGES: dict[str, dict[str, str]] = { "en": { # General "welcome": "Welcome to KTransformers!", "goodbye": "Goodbye!", "error": "Error", "warning": "Warning", "success": "Success", "info": "Info", "yes": "Yes", "no": "No", "cancel": "Cancel", "confirm": "Confirm", "done": "Done", "failed": "Failed", "skip": "Skip", "back": "Back", "next": "Next", "retry": "Retry", "abort": "Abort", # Version command "version_info": "KTransformers CLI", "version_python": "Python", "version_platform": "Platform", "version_cuda": "CUDA", "version_cuda_not_found": "Not found", "version_kt_kernel": "kt-kernel", "version_ktransformers": "ktransformers", "version_sglang": "sglang", "version_llamafactory": "llamafactory", "version_not_installed": "Not installed", # Install command "install_detecting_env": "Detecting environment managers...", "install_found": "Found {name} (version {version})", "install_not_found": "Not found: {name}", "install_checking_env": "Checking existing environments...", "install_env_exists": "Found existing 'kt' environment", "install_env_not_exists": "No 'kt' environment found", "install_no_env_manager": "No virtual environment manager detected", "install_select_method": "Please select installation method:", "install_method_conda": "Create new conda environment 'kt' (Recommended)", "install_method_venv": "Create new venv environment", "install_method_uv": "Create new uv environment (Fast)", "install_method_docker": "Use Docker container", "install_method_system": "Install to system Python (Not recommended)", "install_select_mode": "Please select installation mode:", "install_mode_inference": "Inference - Install kt-kernel + SGLang", "install_mode_sft": "Training - Install kt-sft + LlamaFactory", "install_mode_full": "Full - Install all components", "install_creating_env": "Creating {type} environment '{name}'...", "install_env_created": "Environment created successfully", "install_installing_deps": "Installing dependencies...", "install_checking_deps": "Checking dependency versions...", "install_dep_ok": "OK", "install_dep_outdated": "Needs update", "install_dep_missing": "Missing", "install_installing_pytorch": "Installing PyTorch...", "install_installing_from_requirements": "Installing from requirements file...", "install_deps_outdated": "Found {count} package(s) that need updating. Continue?", "install_updating": "Updating packages...", "install_complete": "Installation complete!", "install_activate_hint": "Activate environment: {command}", "install_start_hint": "Get started: kt run --help", "install_docker_pulling": "Pulling Docker image...", "install_docker_complete": "Docker image ready!", "install_docker_run_hint": "Run with: docker run --gpus all -p 30000:30000 {image} kt run {model}", "install_in_venv": "Running in virtual environment: {name}", "install_continue_without_venv": "Continue installing to system Python?", "install_already_installed": "All dependencies are already installed!", "install_confirm": "Install {count} package(s)?", # Install - System dependencies "install_checking_system_deps": "Checking system dependencies...", "install_dep_name": "Dependency", "install_dep_status": "Status", "install_deps_all_installed": "All system dependencies are installed", "install_deps_install_prompt": "Install missing dependencies?", "install_installing_system_deps": "Installing system dependencies...", "install_installing_dep": "Installing {name}", "install_dep_no_install_cmd": "No install command available for {name} on {os}", "install_dep_install_failed": "Failed to install {name}", "install_deps_skipped": "Skipping dependency installation", "install_deps_failed": "Failed to install system dependencies", # Install - CPU detection "install_auto_detect_cpu": "Auto-detecting CPU capabilities...", "install_cpu_features": "Detected CPU features: {features}", "install_cpu_no_features": "No advanced CPU features detected", # Install - Build configuration "install_build_config": "Build Configuration:", "install_native_warning": "Note: Binary optimized for THIS CPU only (not portable)", "install_building_from_source": "Building kt-kernel from source...", "install_build_failed": "Build failed", "install_build_success": "Build completed successfully", # Install - Verification "install_verifying": "Verifying installation...", "install_verify_success": "kt-kernel {version} ({variant} variant) installed successfully", "install_verify_failed": "Verification failed: {error}", # Install - Docker "install_docker_guide_title": "Docker Installation", "install_docker_guide_desc": "For Docker installation, please refer to the official guide:", # Config command "config_show_title": "Current Configuration", "config_set_success": "Configuration updated: {key} = {value}", "config_get_value": "{key} = {value}", "config_get_not_found": "Configuration key '{key}' not found", "config_reset_confirm": "This will reset all configurations to default. Continue?", "config_reset_success": "Configuration reset to default", "config_file_location": "Configuration file: {path}", # Doctor command "doctor_title": "KTransformers Environment Diagnostics", "doctor_checking": "Running diagnostics...", "doctor_check_python": "Python version", "doctor_check_cuda": "CUDA availability", "doctor_check_gpu": "GPU detection", "doctor_check_cpu": "CPU", "doctor_check_cpu_isa": "CPU Instructions", "doctor_check_numa": "NUMA Topology", "doctor_check_memory": "System memory", "doctor_check_disk": "Disk space", "doctor_check_packages": "Required packages", "doctor_check_env": "Environment variables", "doctor_status_ok": "OK", "doctor_status_warning": "Warning", "doctor_status_error": "Error", "doctor_gpu_found": "Found {count} GPU(s): {names}", "doctor_gpu_not_found": "No GPU detected", "doctor_cpu_info": "{name} ({cores} cores / {threads} threads)", "doctor_cpu_isa_info": "{isa_list}", "doctor_cpu_isa_missing": "Missing recommended: {missing}", "doctor_numa_info": "{nodes} node(s)", "doctor_numa_detail": "{node}: CPUs {cpus}", "doctor_memory_info": "{available} available / {total} total", "doctor_memory_freq": "{available} available / {total} total ({freq}MHz {type})", "doctor_disk_info": "{available} available at {path}", "doctor_all_ok": "All checks passed! Your environment is ready.", "doctor_has_issues": "Some issues were found. Please review the warnings/errors above.", # Run command "run_detecting_hardware": "Detecting hardware configuration...", "run_gpu_info": "GPU: {name} ({vram}GB VRAM)", "run_cpu_info": "CPU: {name} ({cores} cores, {numa} NUMA nodes)", "run_ram_info": "RAM: {total}GB", "run_checking_model": "Checking model status...", "run_model_path": "Model path: {path}", "run_weights_not_found": "Quantized weights not found", "run_quant_prompt": "Quantize model now? (This may take a while)", "run_quantizing": "Quantizing model...", "run_starting_server": "Starting server...", "run_server_mode": "Mode: SGLang + kt-kernel", "run_server_port": "Port: {port}", "run_gpu_experts": "GPU experts: {count}/layer", "run_cpu_threads": "CPU threads: {count}", "run_server_started": "Server started!", "run_api_url": "API URL: http://{host}:{port}", "run_docs_url": "Docs URL: http://{host}:{port}/docs", "run_stop_hint": "Press Ctrl+C to stop the server", "run_model_not_found": "Model '{name}' not found. Run 'kt download' first.", "run_multiple_matches": "Multiple models found. Please select:", "run_select_model": "Select model", "run_select_model_title": "Select a model to run", "run_select_model_prompt": "Enter number", "run_local_models": "Local Models (Downloaded)", "run_registered_models": "Registered Models", # Download command "download_list_title": "Available Models", "download_searching": "Searching for model '{name}'...", "download_found": "Found: {name}", "download_multiple_found": "Multiple matches found:", "download_select": "Select model to download:", "download_destination": "Destination: {path}", "download_starting": "Starting download...", "download_progress": "Downloading {name}...", "download_complete": "Download complete!", "download_already_exists": "Model already exists at {path}", "download_overwrite_prompt": "Overwrite existing files?", # Quant command "quant_input_path": "Input path: {path}", "quant_output_path": "Output path: {path}", "quant_method": "Quantization method: {method}", "quant_starting": "Starting quantization...", "quant_progress": "Quantizing...", "quant_complete": "Quantization complete!", "quant_input_not_found": "Input model not found at {path}", # SFT command "sft_mode_train": "Training mode", "sft_mode_chat": "Chat mode", "sft_mode_export": "Export mode", "sft_config_path": "Config file: {path}", "sft_starting": "Starting {mode}...", "sft_complete": "{mode} complete!", "sft_config_not_found": "Config file not found: {path}", # Bench command "bench_starting": "Starting benchmark...", "bench_type": "Benchmark type: {type}", "bench_complete": "Benchmark complete!", "bench_results_title": "Benchmark Results", # Common prompts "prompt_continue": "Continue?", "prompt_select": "Please select:", "prompt_enter_value": "Enter value:", "prompt_confirm_action": "Confirm this action?", # First-run setup - Model path selection "setup_model_path_title": "Model Storage Location", "setup_model_path_desc": "LLM models are large (50-200GB+). Please select a storage location with sufficient space:", "setup_scanning_disks": "Scanning available storage locations...", "setup_disk_option": "{path} ({available} available / {total} total)", "setup_disk_option_recommended": "{path} ({available} available / {total} total) [Recommended]", "setup_custom_path": "Enter custom path", "setup_enter_custom_path": "Enter the path for model storage", "setup_path_not_exist": "Path does not exist. Create it?", "setup_path_no_write": "No write permission for this path. Please choose another.", "setup_path_low_space": "Warning: Less than 100GB available. Large models may not fit.", "setup_model_path_set": "Model storage path set to: {path}", "setup_no_large_disk": "No large storage locations found. Using default path.", "setup_scanning_models": "Scanning for existing models...", "setup_found_models": "Found {count} model(s):", "setup_model_info": "{name} ({size}, {type})", "setup_no_models_found": "No existing models found in this location.", "setup_location_has_models": "{count} model(s) found", "setup_installing_completion": "Installing shell completion for {shell}...", "setup_completion_installed": "Shell completion installed! Restart terminal to enable.", "setup_completion_failed": "Failed to install shell completion. Run 'kt --install-completion' manually.", # Auto completion "completion_installed_title": "Tab Completion", "completion_installed_for": "Shell completion installed for {shell}", "completion_activate_now": "To enable completion in this terminal session, run:", "completion_next_session": "Completion will be automatically enabled in new terminal sessions.", # SGLang "sglang_not_found": "SGLang not found", "sglang_pypi_warning": "SGLang from PyPI may not be compatible with kt-kernel", "sglang_pypi_hint": 'SGLang from PyPI may not be compatible. Install from source: git clone https://github.com/kvcache-ai/sglang && cd sglang && pip install -e "python[all]"', "sglang_install_hint": 'Install SGLang: git clone https://github.com/kvcache-ai/sglang && cd sglang && pip install -e "python[all]"', "sglang_recommend_source": 'Recommend reinstalling from source: git clone https://github.com/kvcache-ai/sglang && cd sglang && pip install -e "python[all]"', "sglang_kt_kernel_not_supported": "SGLang does not support kt-kernel (missing --kt-gpu-prefill-token-threshold parameter)", "sglang_checking_kt_kernel_support": "Checking SGLang kt-kernel support...", "sglang_kt_kernel_supported": "SGLang kt-kernel support verified", # Chat "chat_proxy_detected": "Proxy detected in environment", "chat_proxy_confirm": "Use proxy for connection?", "chat_proxy_disabled": "Proxy disabled for this session", # Model command "model_supported_title": "KTransformers Supported Models", "model_column_model": "Model", "model_column_status": "Status", "model_column_local_path": "Local Path", "model_status_local": "Local", "model_status_not_downloaded": "Not downloaded", "model_usage_title": "Usage", "model_usage_download": "Download a model:", "model_usage_list_local": "List local models:", "model_usage_search": "Search models:", "model_storage_paths_title": "Model Storage Paths", "model_local_models_title": "Locally Downloaded Models", "model_available_models_title": "Available Models", "model_no_local_models": "No locally downloaded models found", "model_download_hint": "Download a model with:", "model_download_usage_hint": "Usage: kt model download ", "model_download_list_hint": "Use 'kt model download --list' to see available models.", "model_download_hf_hint": "Or specify a HuggingFace repo directly: kt model download org/model-name", "model_saved_to": "Model saved to: {path}", "model_start_with": "Start with: kt run {name}", "model_download_failed": "Download failed: {error}", "model_hf_cli_not_found": "huggingface-cli not found. Install with: pip install huggingface-hub", "model_path_not_exist": "Path does not exist: {path}", "model_create_directory": "Create directory {path}?", "model_created_directory": "Created directory: {path}", "model_create_dir_failed": "Failed to create directory: {error}", "model_path_added": "Added model path: {path}", "model_path_removed": "Removed model path: {path}", "model_path_not_found": "Path not found in configuration or cannot remove last path: {path}", "model_search_no_results": "No models found matching '{query}'", "model_search_results_title": "Search Results for '{query}'", "model_column_name": "Name", "model_column_hf_repo": "HuggingFace Repo", "model_column_aliases": "Aliases", # Coming soon "feature_coming_soon": "This feature is coming soon...", }, "zh": { # General "welcome": "欢迎使用 KTransformers！", "goodbye": "再见！", "error": "错误", "warning": "警告", "success": "成功", "info": "信息", "yes": "是", "no": "否", "cancel": "取消", "confirm": "确认", "done": "完成", "failed": "失败", "skip": "跳过", "back": "返回", "next": "下一步", "retry": "重试", "abort": "中止", # Version command "version_info": "KTransformers CLI", "version_python": "Python", "version_platform": "平台", "version_cuda": "CUDA", "version_cuda_not_found": "未找到", "version_kt_kernel": "kt-kernel", "version_ktransformers": "ktransformers", "version_sglang": "sglang", "version_llamafactory": "llamafactory", "version_not_installed": "未安装", # Install command "install_detecting_env": "检测环境管理工具...", "install_found": "发现 {name} (版本 {version})", "install_not_found": "未找到: {name}", "install_checking_env": "检查现有环境...", "install_env_exists": "发现现有 'kt' 环境", "install_env_not_exists": "未发现 'kt' 环境", "install_no_env_manager": "未检测到虚拟环境管理工具", "install_select_method": "请选择安装方式:", "install_method_conda": "创建新的 conda 环境 'kt' (推荐)", "install_method_venv": "创建新的 venv 环境", "install_method_uv": "创建新的 uv 环境 (快速)", "install_method_docker": "使用 Docker 容器", "install_method_system": "安装到系统 Python (不推荐)", "install_select_mode": "请选择安装模式:", "install_mode_inference": "推理模式 - 安装 kt-kernel + SGLang", "install_mode_sft": "训练模式 - 安装 kt-sft + LlamaFactory", "install_mode_full": "完整安装 - 安装所有组件", "install_creating_env": "正在创建 {type} 环境 '{name}'...", "install_env_created": "环境创建成功", "install_installing_deps": "正在安装依赖...", "install_checking_deps": "检查依赖版本...", "install_dep_ok": "正常", "install_dep_outdated": "需更新", "install_dep_missing": "缺失", "install_installing_pytorch": "正在安装 PyTorch...", "install_installing_from_requirements": "从依赖文件安装...", "install_deps_outdated": "发现 {count} 个包需要更新，是否继续？", "install_updating": "正在更新包...", "install_complete": "安装完成！", "install_activate_hint": "激活环境: {command}", "install_start_hint": "开始使用: kt run --help", "install_docker_pulling": "正在拉取 Docker 镜像...", "install_docker_complete": "Docker 镜像已就绪！", "install_docker_run_hint": "运行: docker run --gpus all -p 30000:30000 {image} kt run {model}", "install_in_venv": "当前在虚拟环境中: {name}", "install_continue_without_venv": "继续安装到系统 Python？", "install_already_installed": "所有依赖已安装！", "install_confirm": "安装 {count} 个包？", # Install - System dependencies "install_checking_system_deps": "检查系统依赖...", "install_dep_name": "依赖项", "install_dep_status": "状态", "install_deps_all_installed": "所有系统依赖已安装", "install_deps_install_prompt": "是否安装缺失的依赖？", "install_installing_system_deps": "正在安装系统依赖...", "install_installing_dep": "正在安装 {name}", "install_dep_no_install_cmd": "{os} 系统上没有 {name} 的安装命令", "install_dep_install_failed": "安装 {name} 失败", "install_deps_skipped": "跳过依赖安装", "install_deps_failed": "系统依赖安装失败", # Install - CPU detection "install_auto_detect_cpu": "正在自动检测 CPU 能力...", "install_cpu_features": "检测到的 CPU 特性: {features}", "install_cpu_no_features": "未检测到高级 CPU 特性", # Install - Build configuration "install_build_config": "构建配置:", "install_native_warning": "注意: 二进制文件仅针对当前 CPU 优化（不可移植）", "install_building_from_source": "正在从源码构建 kt-kernel...", "install_build_failed": "构建失败", "install_build_success": "构建成功", # Install - Verification "install_verifying": "正在验证安装...", "install_verify_success": "kt-kernel {version} ({variant} 变体) 安装成功", "install_verify_failed": "验证失败: {error}", # Install - Docker "install_docker_guide_title": "Docker 安装", "install_docker_guide_desc": "有关 Docker 安装，请参阅官方指南:", # Config command "config_show_title": "当前配置", "config_set_success": "配置已更新: {key} = {value}", "config_get_value": "{key} = {value}", "config_get_not_found": "未找到配置项 '{key}'", "config_reset_confirm": "这将重置所有配置为默认值。是否继续？", "config_reset_success": "配置已重置为默认值", "config_file_location": "配置文件: {path}", # Doctor command "doctor_title": "KTransformers 环境诊断", "doctor_checking": "正在运行诊断...", "doctor_check_python": "Python 版本", "doctor_check_cuda": "CUDA 可用性", "doctor_check_gpu": "GPU 检测", "doctor_check_cpu": "CPU", "doctor_check_cpu_isa": "CPU 指令集", "doctor_check_numa": "NUMA 拓扑", "doctor_check_memory": "系统内存", "doctor_check_disk": "磁盘空间", "doctor_check_packages": "必需的包", "doctor_check_env": "环境变量", "doctor_status_ok": "正常", "doctor_status_warning": "警告", "doctor_status_error": "错误", "doctor_gpu_found": "发现 {count} 个 GPU: {names}", "doctor_gpu_not_found": "未检测到 GPU", "doctor_cpu_info": "{name} ({cores} 核心 / {threads} 线程)", "doctor_cpu_isa_info": "{isa_list}", "doctor_cpu_isa_missing": "缺少推荐指令集: {missing}", "doctor_numa_info": "{nodes} 个节点", "doctor_numa_detail": "{node}: CPU {cpus}", "doctor_memory_info": "{available} 可用 / {total} 总计", "doctor_memory_freq": "{available} 可用 / {total} 总计 ({freq}MHz {type})", "doctor_disk_info": "{path} 有 {available} 可用空间", "doctor_all_ok": "所有检查通过！您的环境已就绪。", "doctor_has_issues": "发现一些问题，请查看上方的警告/错误信息。", # Run command "run_detecting_hardware": "检测硬件配置...", "run_gpu_info": "GPU: {name} ({vram}GB 显存)", "run_cpu_info": "CPU: {name} ({cores} 核心, {numa} NUMA 节点)", "run_ram_info": "内存: {total}GB", "run_checking_model": "检查模型状态...", "run_model_path": "模型路径: {path}", "run_weights_not_found": "未找到量化权重", "run_quant_prompt": "是否现在量化模型？(这可能需要一些时间)", "run_quantizing": "正在量化模型...", "run_starting_server": "正在启动服务器...", "run_server_mode": "模式: SGLang + kt-kernel", "run_server_port": "端口: {port}", "run_gpu_experts": "GPU 专家: {count}/层", "run_cpu_threads": "CPU 线程: {count}", "run_server_started": "服务器已启动！", "run_api_url": "API 地址: http://{host}:{port}", "run_docs_url": "文档地址: http://{host}:{port}/docs", "run_stop_hint": "按 Ctrl+C 停止服务器", "run_model_not_found": "未找到模型 '{name}'。请先运行 'kt download'。", "run_multiple_matches": "找到多个匹配的模型，请选择:", "run_select_model": "选择模型", "run_select_model_title": "选择要运行的模型", "run_select_model_prompt": "输入编号", "run_local_models": "本地模型 (已下载)", "run_registered_models": "注册模型", # Download command "download_list_title": "可用模型", "download_searching": "正在搜索模型 '{name}'...", "download_found": "找到: {name}", "download_multiple_found": "找到多个匹配:", "download_select": "选择要下载的模型:", "download_destination": "目标路径: {path}", "download_starting": "开始下载...", "download_progress": "正在下载 {name}...", "download_complete": "下载完成！", "download_already_exists": "模型已存在于 {path}", "download_overwrite_prompt": "是否覆盖现有文件？", # Quant command "quant_input_path": "输入路径: {path}", "quant_output_path": "输出路径: {path}", "quant_method": "量化方法: {method}", "quant_starting": "开始量化...", "quant_progress": "正在量化...", "quant_complete": "量化完成！", "quant_input_not_found": "未找到输入模型: {path}", # SFT command "sft_mode_train": "训练模式", "sft_mode_chat": "聊天模式", "sft_mode_export": "导出模式", "sft_config_path": "配置文件: {path}", "sft_starting": "正在启动 {mode}...", "sft_complete": "{mode} 完成！", "sft_config_not_found": "未找到配置文件: {path}", # Bench command "bench_starting": "开始基准测试...", "bench_type": "测试类型: {type}", "bench_complete": "基准测试完成！", "bench_results_title": "基准测试结果", # Common prompts "prompt_continue": "是否继续？", "prompt_select": "请选择:", "prompt_enter_value": "请输入:", "prompt_confirm_action": "确认此操作？", # First-run setup - Model path selection "setup_model_path_title": "模型存储位置", "setup_model_path_desc": "大语言模型体积较大（50-200GB+）。请选择一个有足够空间的存储位置：", "setup_scanning_disks": "正在扫描可用存储位置...", "setup_disk_option": "{path} (可用 {available} / 总共 {total})", "setup_disk_option_recommended": "{path} (可用 {available} / 总共 {total}) [推荐]", "setup_custom_path": "输入自定义路径", "setup_enter_custom_path": "请输入模型存储路径", "setup_path_not_exist": "路径不存在，是否创建？", "setup_path_no_write": "没有该路径的写入权限，请选择其他路径。", "setup_path_low_space": "警告：可用空间不足 100GB，可能无法存储大型模型。", "setup_model_path_set": "模型存储路径已设置为: {path}", "setup_no_large_disk": "未发现大容量存储位置，使用默认路径。", "setup_scanning_models": "正在扫描已有模型...", "setup_found_models": "发现 {count} 个模型:", "setup_model_info": "{name} ({size}, {type})", "setup_no_models_found": "该位置未发现已有模型。", "setup_location_has_models": "发现 {count} 个模型", "setup_installing_completion": "正在为 {shell} 安装命令补全...", "setup_completion_installed": "命令补全已安装！重启终端后生效。", "setup_completion_failed": "命令补全安装失败。请手动运行 'kt --install-completion'。", # Auto completion "completion_installed_title": "命令补全", "completion_installed_for": "已为 {shell} 安装命令补全", "completion_activate_now": "在当前终端会话中启用补全，请运行：", "completion_next_session": "新的终端会话将自动启用补全。", # SGLang "sglang_not_found": "未找到 SGLang", "sglang_pypi_warning": "PyPI 版本的 SGLang 可能与 kt-kernel 不兼容", "sglang_pypi_hint": 'PyPI 版本可能不兼容。从源码安装: git clone https://github.com/kvcache-ai/sglang && cd sglang && pip install -e "python[all]"', "sglang_install_hint": '安装 SGLang: git clone https://github.com/kvcache-ai/sglang && cd sglang && pip install -e "python[all]"', "sglang_recommend_source": '建议从源码重新安装: git clone https://github.com/kvcache-ai/sglang && cd sglang && pip install -e "python[all]"', "sglang_kt_kernel_not_supported": "SGLang 不支持 kt-kernel (缺少 --kt-gpu-prefill-token-threshold 参数)", "sglang_checking_kt_kernel_support": "正在检查 SGLang kt-kernel 支持...", "sglang_kt_kernel_supported": "SGLang kt-kernel 支持已验证", # Chat "chat_proxy_detected": "检测到环境中存在代理设置", "chat_proxy_confirm": "是否使用代理连接？", "chat_proxy_disabled": "已在本次会话中禁用代理", # Model command "model_supported_title": "KTransformers 支持的模型", "model_column_model": "模型", "model_column_status": "状态", "model_column_local_path": "本地路径", "model_status_local": "本地", "model_status_not_downloaded": "未下载", "model_usage_title": "使用方法", "model_usage_download": "下载模型:", "model_usage_list_local": "列出本地模型:", "model_usage_search": "搜索模型:", "model_storage_paths_title": "模型存储路径", "model_local_models_title": "本地已下载的模型", "model_available_models_title": "可用模型", "model_no_local_models": "未找到本地已下载的模型", "model_download_hint": "下载模型:", "model_download_usage_hint": "用法: kt model download <模型名称>", "model_download_list_hint": "使用 'kt model download --list' 查看可用模型。", "model_download_hf_hint": "或直接指定 HuggingFace 仓库: kt model download org/model-name", "model_saved_to": "模型已保存到: {path}", "model_start_with": "启动命令: kt run {name}", "model_download_failed": "下载失败: {error}", "model_hf_cli_not_found": "未找到 huggingface-cli。请安装: pip install huggingface-hub", "model_path_not_exist": "路径不存在: {path}", "model_create_directory": "创建目录 {path}？", "model_created_directory": "已创建目录: {path}", "model_create_dir_failed": "创建目录失败: {error}", "model_path_added": "已添加模型路径: {path}", "model_path_removed": "已移除模型路径: {path}", "model_path_not_found": "路径未找到或无法移除最后一个路径: {path}", "model_search_no_results": "未找到匹配 '{query}' 的模型", "model_search_results_title": "'{query}' 的搜索结果", "model_column_name": "名称", "model_column_hf_repo": "HuggingFace 仓库", "model_column_aliases": "别名", # Coming soon "feature_coming_soon": "此功能即将推出...", }, } # Cache for language detection to avoid repeated I/O _lang_cache: str | None = None def get_lang() -> str: """ Detect the current language setting. Priority: 1. KT_LANG environment variable 2. Config file general.language setting 3. LANG environment variable (if config is "auto") 4. Default to English Returns: Language code: "zh" for Chinese, "en" for English """ global _lang_cache # 1. Check KT_LANG environment variable (highest priority) kt_lang = os.environ.get("KT_LANG", "").lower() if kt_lang: return "zh" if kt_lang.startswith("zh") else "en" # 2. Return cached value if available (avoids I/O on every call) if _lang_cache is not None: return _lang_cache # 3. Check config file setting (with caching) # Import here to avoid circular imports from kt_kernel.cli.config.settings import get_settings try: settings = get_settings() config_lang = settings.get("general.language", "auto") if config_lang and config_lang != "auto": lang = "zh" if config_lang.lower().startswith("zh") else "en" _lang_cache = lang return lang except Exception: # If settings fail to load, continue with system detection pass # 4. Check system LANG environment variable system_lang = os.environ.get("LANG", "").lower() lang = "zh" if system_lang.startswith("zh") else "en" _lang_cache = lang return lang def t(msg_key: str, **kwargs: Any) -> str: """ Translate a message key to the current language. Args: msg_key: Message key to translate **kwargs: Format arguments for the message Returns: Translated and formatted message string Example: >>> t("welcome") "Welcome to KTransformers!" # or "欢迎使用 KTransformers！" in Chinese >>> t("install_found", name="conda", version="24.1.0") "Found conda (version 24.1.0)" """ lang = get_lang() messages = MESSAGES.get(lang, MESSAGES["en"]) message = messages.get(msg_key, MESSAGES["en"].get(msg_key, msg_key)) if kwargs: try: return message.format(**kwargs) except KeyError: return message return message def set_lang(lang: str) -> None: """ Set the language for the current session. Args: lang: Language code ("en" or "zh") """ global _lang_cache os.environ["KT_LANG"] = lang _lang_cache = lang # Update cache when language is explicitly set