import traceback import streamlit as st import os from app.config import config from app.utils import utils from loguru import logger from app.services.llm.unified_service import UnifiedLLMService # 需要用户手动填写 Base URL 的 OpenAI 兼容网关及其默认接口 OPENAI_COMPATIBLE_GATEWAY_BASE_URLS = { "siliconflow": "https://api.siliconflow.cn/v1", "openrouter": "https://openrouter.ai/api/v1", "moonshot": "https://api.moonshot.cn/v1", "gemini(openai)": "", } def build_base_url_help(provider: str, model_type: str) -> tuple[str, bool, str]: """ 根据 provider 返回 Base URL 的帮助文案 Returns: help_text: 显示在输入框的帮助内容 requires_base: 是否强制提示必须填写 Base URL placeholder: 推荐的默认值（可为空字符串） """ default_help = "自定义 API 端点（可选），当使用自建或第三方代理时需要填写" provider_key = (provider or "").lower() example_url = OPENAI_COMPATIBLE_GATEWAY_BASE_URLS.get(provider_key) if example_url is not None: extra = f"\n推荐接口地址: {example_url}" if example_url else "" help_text = ( f"{model_type} 选择的提供商基于 OpenAI 兼容网关，必须填写完整的接口地址。" f"{extra}" ) return help_text, True, example_url return default_help, False, "" def validate_api_key(api_key: str, provider: str) -> tuple[bool, str]: """验证API密钥格式""" if not api_key or not api_key.strip(): return False, f"{provider} API密钥不能为空" # 基本长度检查 if len(api_key.strip()) < 10: return False, f"{provider} API密钥长度过短，请检查是否正确" return True, "" def validate_base_url(base_url: str, provider: str) -> tuple[bool, str]: """验证Base URL格式""" if not base_url or not base_url.strip(): return True, "" # base_url可以为空 base_url = base_url.strip() if not (base_url.startswith('http://') or base_url.startswith('https://')): return False, f"{provider} Base URL必须以http://或https://开头" return True, "" def validate_model_name(model_name: str, provider: str) -> tuple[bool, str]: """验证模型名称""" if not model_name or not model_name.strip(): return False, f"{provider} 模型名称不能为空" return True, "" def validate_litellm_model_name(model_name: str, model_type: str) -> tuple[bool, str]: """验证 LiteLLM 模型名称格式 Args: model_name: 模型名称，应为 provider/model 格式 model_type: 模型类型（如"视频分析"、"文案生成"） Returns: (是否有效, 错误消息) """ if not model_name or not model_name.strip(): return False, f"{model_type} 模型名称不能为空" model_name = model_name.strip() # LiteLLM 推荐格式：provider/model（如 gemini/gemini-2.0-flash-lite） # 但也支持直接的模型名称（如 gpt-4o，LiteLLM 会自动推断 provider） # 检查是否包含 provider 前缀（推荐格式） if "/" in model_name: parts = model_name.split("/") if len(parts) < 2 or not parts[0] or not parts[1]: return False, f"{model_type} 模型名称格式错误。推荐格式: provider/model （如 gemini/gemini-2.0-flash-lite）" # 验证 provider 名称（只允许字母、数字、下划线、连字符） provider = parts[0] if not provider.replace("-", "").replace("_", "").isalnum(): return False, f"{model_type} Provider 名称只能包含字母、数字、下划线和连字符" else: # 直接模型名称也是有效的（LiteLLM 会自动推断） # 但给出警告建议使用完整格式 logger.debug(f"{model_type} 模型名称未包含 provider 前缀，LiteLLM 将自动推断") # 基本长度检查 if len(model_name) < 3: return False, f"{model_type} 模型名称过短" if len(model_name) > 200: return False, f"{model_type} 模型名称过长" return True, "" def show_config_validation_errors(errors: list): """显示配置验证错误""" if errors: for error in errors: st.error(error) def render_basic_settings(tr): """渲染基础设置面板""" with st.expander(tr("Basic Settings"), expanded=False): config_panels = st.columns(3) left_config_panel = config_panels[0] middle_config_panel = config_panels[1] right_config_panel = config_panels[2] with left_config_panel: render_language_settings(tr) render_proxy_settings(tr) with middle_config_panel: render_vision_llm_settings(tr) # 视频分析模型设置 with right_config_panel: render_text_llm_settings(tr) # 文案生成模型设置 def render_language_settings(tr): st.subheader(tr("Proxy Settings")) """渲染语言设置""" system_locale = utils.get_system_locale() i18n_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), "i18n") locales = utils.load_locales(i18n_dir) display_languages = [] selected_index = 0 for i, code in enumerate(locales.keys()): display_languages.append(f"{code} - {locales[code].get('Language')}") if code == st.session_state.get('ui_language', system_locale): selected_index = i selected_language = st.selectbox( tr("Language"), options=display_languages, index=selected_index ) if selected_language: code = selected_language.split(" - ")[0].strip() st.session_state['ui_language'] = code config.ui['language'] = code def render_proxy_settings(tr): """渲染代理设置""" # 获取当前代理状态 proxy_enabled = config.proxy.get("enabled", False) proxy_url_http = config.proxy.get("http") proxy_url_https = config.proxy.get("https") # 添加代理开关 proxy_enabled = st.checkbox(tr("Enable Proxy"), value=proxy_enabled) # 保存代理开关状态 # config.proxy["enabled"] = proxy_enabled # 只有在代理启用时才显示代理设置输入框 if proxy_enabled: HTTP_PROXY = st.text_input(tr("HTTP_PROXY"), value=proxy_url_http) HTTPS_PROXY = st.text_input(tr("HTTPs_PROXY"), value=proxy_url_https) if HTTP_PROXY and HTTPS_PROXY: config.proxy["http"] = HTTP_PROXY config.proxy["https"] = HTTPS_PROXY os.environ["HTTP_PROXY"] = HTTP_PROXY os.environ["HTTPS_PROXY"] = HTTPS_PROXY # logger.debug(f"代理已启用: {HTTP_PROXY}") else: # 当代理被禁用时，清除环境变量和配置 os.environ.pop("HTTP_PROXY", None) os.environ.pop("HTTPS_PROXY", None) # config.proxy["http"] = "" # config.proxy["https"] = "" def test_vision_model_connection(api_key, base_url, model_name, provider, tr): """测试视觉模型连接 Args: api_key: API密钥 base_url: 基础URL model_name: 模型名称 provider: 提供商名称 Returns: bool: 连接是否成功 str: 测试结果消息 """ import requests logger.debug(f"大模型连通性测试: {base_url} 模型: {model_name} apikey: {api_key}") if provider.lower() == 'gemini': # 原生Gemini API测试 try: # 构建请求数据 request_data = { "contents": [{ "parts": [{"text": "直接回复我文本'当前网络可用'"}] }] } # 构建请求URL api_base_url = base_url url = f"{api_base_url}/models/{model_name}:generateContent" # 发送请求 response = requests.post( url, json=request_data, headers={ "x-goog-api-key": api_key, "Content-Type": "application/json" }, timeout=10 ) if response.status_code == 200: return True, tr("原生Gemini模型连接成功") else: return False, f"{tr('原生Gemini模型连接失败')}: HTTP {response.status_code}" except Exception as e: return False, f"{tr('原生Gemini模型连接失败')}: {str(e)}" elif provider.lower() == 'gemini(openai)': # OpenAI兼容的Gemini代理测试 try: headers = { "Authorization": f"Bearer {api_key}", "Content-Type": "application/json" } test_url = f"{base_url.rstrip('/')}/chat/completions" test_data = { "model": model_name, "messages": [ {"role": "user", "content": "直接回复我文本'当前网络可用'"} ], "stream": False } response = requests.post(test_url, headers=headers, json=test_data, timeout=10) if response.status_code == 200: return True, tr("OpenAI兼容Gemini代理连接成功") else: return False, f"{tr('OpenAI兼容Gemini代理连接失败')}: HTTP {response.status_code}" except Exception as e: return False, f"{tr('OpenAI兼容Gemini代理连接失败')}: {str(e)}" else: from openai import OpenAI try: client = OpenAI( api_key=api_key, base_url=base_url, ) response = client.chat.completions.create( model=model_name, messages=[ { "role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}], }, { "role": "user", "content": [ { "type": "image_url", "image_url": { "url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg" }, }, {"type": "text", "text": "回复我网络可用即可"}, ], }, ], ) if response and response.choices: return True, tr("QwenVL model is available") else: return False, tr("QwenVL model returned invalid response") except Exception as e: # logger.debug(api_key) # logger.debug(base_url) # logger.debug(model_name) return False, f"{tr('QwenVL model is not available')}: {str(e)}" def test_litellm_vision_model(api_key: str, base_url: str, model_name: str, tr) -> tuple[bool, str]: """测试 LiteLLM 视觉模型连接 Args: api_key: API 密钥 base_url: 基础 URL（可选） model_name: 模型名称（LiteLLM 格式：provider/model） tr: 翻译函数 Returns: (连接是否成功, 测试结果消息) """ try: import litellm import os import base64 import io from PIL import Image logger.debug(f"LiteLLM 视觉模型连通性测试: model={model_name}, api_key={api_key[:10]}..., base_url={base_url}") # 提取 provider 名称 provider = model_name.split("/")[0] if "/" in model_name else "unknown" # 设置 API key 到环境变量 env_key_mapping = { "gemini": "GEMINI_API_KEY", "google": "GEMINI_API_KEY", "openai": "OPENAI_API_KEY", "qwen": "QWEN_API_KEY", "dashscope": "DASHSCOPE_API_KEY", "siliconflow": "SILICONFLOW_API_KEY", } env_var = env_key_mapping.get(provider.lower(), f"{provider.upper()}_API_KEY") old_key = os.environ.get(env_var) os.environ[env_var] = api_key # SiliconFlow 特殊处理：使用 OpenAI 兼容模式 test_model_name = model_name if provider.lower() == "siliconflow": # 替换 provider 为 openai if "/" in model_name: test_model_name = f"openai/{model_name.split('/', 1)[1]}" else: test_model_name = f"openai/{model_name}" # 确保设置了 base_url if not base_url: base_url = "https://api.siliconflow.cn/v1" # 设置 OPENAI_API_KEY (SiliconFlow 使用 OpenAI 协议) os.environ["OPENAI_API_KEY"] = api_key os.environ["OPENAI_API_BASE"] = base_url try: # 创建测试图片（64x64 白色像素，避免某些模型对极小图片的限制） test_image = Image.new('RGB', (64, 64), color='white') img_buffer = io.BytesIO() test_image.save(img_buffer, format='JPEG') img_bytes = img_buffer.getvalue() base64_image = base64.b64encode(img_bytes).decode('utf-8') # 构建测试请求 messages = [{ "role": "user", "content": [ {"type": "text", "text": "请直接回复'连接成功'"}, { "type": "image_url", "image_url": { "url": f"data:image/jpeg;base64,{base64_image}" } } ] }] # 准备参数 completion_kwargs = { "model": test_model_name, "messages": messages, "temperature": 0.1, "max_tokens": 50 } if base_url: completion_kwargs["api_base"] = base_url # 调用 LiteLLM（同步调用用于测试） response = litellm.completion(**completion_kwargs) if response and response.choices and len(response.choices) > 0: return True, f"LiteLLM 视觉模型连接成功 ({model_name})" else: return False, f"LiteLLM 视觉模型返回空响应" finally: # 恢复原始环境变量 if old_key: os.environ[env_var] = old_key else: os.environ.pop(env_var, None) # 清理临时设置的 OpenAI 环境变量 if provider.lower() == "siliconflow": os.environ.pop("OPENAI_API_KEY", None) os.environ.pop("OPENAI_API_BASE", None) except Exception as e: error_msg = str(e) logger.error(f"LiteLLM 视觉模型测试失败: {error_msg}") # 提供更友好的错误信息 if "authentication" in error_msg.lower() or "api_key" in error_msg.lower(): return False, f"认证失败，请检查 API Key 是否正确" elif "not found" in error_msg.lower() or "404" in error_msg: return False, f"模型不存在，请检查模型名称是否正确" elif "rate limit" in error_msg.lower(): return False, f"超出速率限制，请稍后重试" else: return False, f"连接失败: {error_msg}" def test_litellm_text_model(api_key: str, base_url: str, model_name: str, tr) -> tuple[bool, str]: """测试 LiteLLM 文本模型连接 Args: api_key: API 密钥 base_url: 基础 URL（可选） model_name: 模型名称（LiteLLM 格式：provider/model） tr: 翻译函数 Returns: (连接是否成功, 测试结果消息) """ try: import litellm import os logger.debug(f"LiteLLM 文本模型连通性测试: model={model_name}, api_key={api_key[:10]}..., base_url={base_url}") # 提取 provider 名称 provider = model_name.split("/")[0] if "/" in model_name else "unknown" # 设置 API key 到环境变量 env_key_mapping = { "gemini": "GEMINI_API_KEY", "google": "GEMINI_API_KEY", "openai": "OPENAI_API_KEY", "qwen": "QWEN_API_KEY", "dashscope": "DASHSCOPE_API_KEY", "siliconflow": "SILICONFLOW_API_KEY", "deepseek": "DEEPSEEK_API_KEY", "moonshot": "MOONSHOT_API_KEY", } env_var = env_key_mapping.get(provider.lower(), f"{provider.upper()}_API_KEY") old_key = os.environ.get(env_var) os.environ[env_var] = api_key # SiliconFlow 特殊处理：使用 OpenAI 兼容模式 test_model_name = model_name if provider.lower() == "siliconflow": # 替换 provider 为 openai if "/" in model_name: test_model_name = f"openai/{model_name.split('/', 1)[1]}" else: test_model_name = f"openai/{model_name}" # 确保设置了 base_url if not base_url: base_url = "https://api.siliconflow.cn/v1" # 设置 OPENAI_API_KEY (SiliconFlow 使用 OpenAI 协议) os.environ["OPENAI_API_KEY"] = api_key os.environ["OPENAI_API_BASE"] = base_url try: # 构建测试请求 messages = [ {"role": "user", "content": "请直接回复'连接成功'"} ] # 准备参数 completion_kwargs = { "model": test_model_name, "messages": messages, "temperature": 0.1, "max_tokens": 20 } if base_url: completion_kwargs["api_base"] = base_url # 调用 LiteLLM（同步调用用于测试） response = litellm.completion(**completion_kwargs) if response and response.choices and len(response.choices) > 0: return True, f"LiteLLM 文本模型连接成功 ({model_name})" else: return False, f"LiteLLM 文本模型返回空响应" finally: # 恢复原始环境变量 if old_key: os.environ[env_var] = old_key else: os.environ.pop(env_var, None) # 清理临时设置的 OpenAI 环境变量 if provider.lower() == "siliconflow": os.environ.pop("OPENAI_API_KEY", None) os.environ.pop("OPENAI_API_BASE", None) except Exception as e: error_msg = str(e) logger.error(f"LiteLLM 文本模型测试失败: {error_msg}") # 提供更友好的错误信息 if "authentication" in error_msg.lower() or "api_key" in error_msg.lower(): return False, f"认证失败，请检查 API Key 是否正确" elif "not found" in error_msg.lower() or "404" in error_msg: return False, f"模型不存在，请检查模型名称是否正确" elif "rate limit" in error_msg.lower(): return False, f"超出速率限制，请稍后重试" else: return False, f"连接失败: {error_msg}" def render_vision_llm_settings(tr): """渲染视频分析模型设置（LiteLLM 统一配置）""" st.subheader(tr("Vision Model Settings")) # 固定使用 LiteLLM 提供商 config.app["vision_llm_provider"] = "litellm" # 获取已保存的 LiteLLM 配置 full_vision_model_name = config.app.get("vision_litellm_model_name", "gemini/gemini-2.0-flash-lite") vision_api_key = config.app.get("vision_litellm_api_key", "") vision_base_url = config.app.get("vision_litellm_base_url", "") # 解析 provider 和 model default_provider = "gemini" default_model = "gemini-2.0-flash-lite" if "/" in full_vision_model_name: parts = full_vision_model_name.split("/", 1) current_provider = parts[0] current_model = parts[1] else: current_provider = default_provider current_model = full_vision_model_name # 定义支持的 provider 列表 LITELLM_PROVIDERS = [ "openai", "gemini", "deepseek", "qwen", "siliconflow", "moonshot", "anthropic", "azure", "ollama", "vertex_ai", "mistral", "codestral", "volcengine", "groq", "cohere", "together_ai", "fireworks_ai", "openrouter", "replicate", "huggingface", "xai", "deepgram", "vllm", "bedrock", "cloudflare" ] # 如果当前 provider 不在列表中，添加到列表头部 if current_provider not in LITELLM_PROVIDERS: LITELLM_PROVIDERS.insert(0, current_provider) # 渲染配置输入框 col1, col2 = st.columns([1, 2]) with col1: selected_provider = st.selectbox( tr("Vision Model Provider"), options=LITELLM_PROVIDERS, index=LITELLM_PROVIDERS.index(current_provider) if current_provider in LITELLM_PROVIDERS else 0, key="vision_provider_select" ) with col2: model_name_input = st.text_input( tr("Vision Model Name"), value=current_model, help="输入模型名称（不包含 provider 前缀）\n\n" "常用示例:\n" "• gemini-2.0-flash-lite\n" "• gpt-4o\n" "• qwen-vl-max\n" "• Qwen/Qwen2.5-VL-32B-Instruct (SiliconFlow)\n\n" "支持 100+ providers，详见: https://docs.litellm.ai/docs/providers", key="vision_model_input" ) # 组合完整的模型名称 st_vision_model_name = f"{selected_provider}/{model_name_input}" if selected_provider and model_name_input else "" st_vision_api_key = st.text_input( tr("Vision API Key"), value=vision_api_key, type="password", help="对应 provider 的 API 密钥\n\n" "获取地址:\n" "• Gemini: https://makersuite.google.com/app/apikey\n" "• OpenAI: https://platform.openai.com/api-keys\n" "• Qwen: https://bailian.console.aliyun.com/\n" "• SiliconFlow: https://cloud.siliconflow.cn/account/ak" ) vision_base_help, vision_base_required, vision_placeholder = build_base_url_help( selected_provider, "视频分析模型" ) st_vision_base_url = st.text_input( tr("Vision Base URL"), value=vision_base_url, help=vision_base_help, placeholder=vision_placeholder or None ) if vision_base_required and not st_vision_base_url: info_example = vision_placeholder or "https://your-openai-compatible-endpoint/v1" st.info(f"请在上方填写 OpenAI 兼容网关地址，例如：{info_example}") # 添加测试连接按钮 if st.button(tr("Test Connection"), key="test_vision_connection"): test_errors = [] if not st_vision_api_key: test_errors.append("请先输入 API 密钥") if not model_name_input: test_errors.append("请先输入模型名称") if test_errors: for error in test_errors: st.error(error) else: with st.spinner(tr("Testing connection...")): try: success, message = test_litellm_vision_model( api_key=st_vision_api_key, base_url=st_vision_base_url, model_name=st_vision_model_name, tr=tr ) if success: st.success(message) else: st.error(message) except Exception as e: st.error(f"测试连接时发生错误: {str(e)}") logger.error(f"LiteLLM 视频分析模型连接测试失败: {str(e)}") # 验证和保存配置 validation_errors = [] config_changed = False # 验证模型名称 if st_vision_model_name: # 这里的验证逻辑可能需要微调，因为我们现在是自动组合的 is_valid, error_msg = validate_litellm_model_name(st_vision_model_name, "视频分析") if is_valid: config.app["vision_litellm_model_name"] = st_vision_model_name st.session_state["vision_litellm_model_name"] = st_vision_model_name config_changed = True else: validation_errors.append(error_msg) # 验证 API 密钥 if st_vision_api_key: is_valid, error_msg = validate_api_key(st_vision_api_key, "视频分析") if is_valid: config.app["vision_litellm_api_key"] = st_vision_api_key st.session_state["vision_litellm_api_key"] = st_vision_api_key config_changed = True else: validation_errors.append(error_msg) # 验证 Base URL（可选） if st_vision_base_url: is_valid, error_msg = validate_base_url(st_vision_base_url, "视频分析") if is_valid: config.app["vision_litellm_base_url"] = st_vision_base_url st.session_state["vision_litellm_base_url"] = st_vision_base_url config_changed = True else: validation_errors.append(error_msg) # 显示验证错误 show_config_validation_errors(validation_errors) # 保存配置 if config_changed and not validation_errors: try: config.save_config() # 清除缓存，确保下次使用新配置 UnifiedLLMService.clear_cache() if st_vision_api_key or st_vision_base_url or st_vision_model_name: st.success(f"视频分析模型配置已保存（LiteLLM）") except Exception as e: st.error(f"保存配置失败: {str(e)}") logger.error(f"保存视频分析配置失败: {str(e)}") def test_text_model_connection(api_key, base_url, model_name, provider, tr): """测试文本模型连接 Args: api_key: API密钥 base_url: 基础URL model_name: 模型名称 provider: 提供商名称 Returns: bool: 连接是否成功 str: 测试结果消息 """ import requests logger.debug(f"大模型连通性测试: {base_url} 模型: {model_name} apikey: {api_key}") try: # 构建统一的测试请求（遵循OpenAI格式） headers = { "Authorization": f"Bearer {api_key}", "Content-Type": "application/json" } # 特殊处理Gemini if provider.lower() == 'gemini': # 原生Gemini API测试 try: # 构建请求数据 request_data = { "contents": [{ "parts": [{"text": "直接回复我文本'当前网络可用'"}] }] } # 构建请求URL api_base_url = base_url url = f"{api_base_url}/models/{model_name}:generateContent" # 发送请求 response = requests.post( url, json=request_data, headers={ "x-goog-api-key": api_key, "Content-Type": "application/json" }, timeout=10 ) if response.status_code == 200: return True, tr("原生Gemini模型连接成功") else: return False, f"{tr('原生Gemini模型连接失败')}: HTTP {response.status_code}" except Exception as e: return False, f"{tr('原生Gemini模型连接失败')}: {str(e)}" elif provider.lower() == 'gemini(openai)': # OpenAI兼容的Gemini代理测试 test_url = f"{base_url.rstrip('/')}/chat/completions" test_data = { "model": model_name, "messages": [ {"role": "user", "content": "直接回复我文本'当前网络可用'"} ], "stream": False } response = requests.post(test_url, headers=headers, json=test_data, timeout=10) if response.status_code == 200: return True, tr("OpenAI兼容Gemini代理连接成功") else: return False, f"{tr('OpenAI兼容Gemini代理连接失败')}: HTTP {response.status_code}" else: test_url = f"{base_url.rstrip('/')}/chat/completions" # 构建测试消息 test_data = { "model": model_name, "messages": [ {"role": "user", "content": "直接回复我文本'当前网络可用'"} ], "stream": False } # 发送测试请求 response = requests.post( test_url, headers=headers, json=test_data, ) # logger.debug(model_name) # logger.debug(api_key) # logger.debug(test_url) if response.status_code == 200: return True, tr("Text model is available") else: return False, f"{tr('Text model is not available')}: HTTP {response.status_code}" except Exception as e: logger.error(traceback.format_exc()) return False, f"{tr('Connection failed')}: {str(e)}" def render_text_llm_settings(tr): """渲染文案生成模型设置（LiteLLM 统一配置）""" st.subheader(tr("Text Generation Model Settings")) # 固定使用 LiteLLM 提供商 config.app["text_llm_provider"] = "litellm" # 获取已保存的 LiteLLM 配置 full_text_model_name = config.app.get("text_litellm_model_name", "deepseek/deepseek-chat") text_api_key = config.app.get("text_litellm_api_key", "") text_base_url = config.app.get("text_litellm_base_url", "") # 解析 provider 和 model default_provider = "deepseek" default_model = "deepseek-chat" if "/" in full_text_model_name: parts = full_text_model_name.split("/", 1) current_provider = parts[0] current_model = parts[1] else: current_provider = default_provider current_model = full_text_model_name # 定义支持的 provider 列表 LITELLM_PROVIDERS = [ "openai", "gemini", "deepseek", "qwen", "siliconflow", "moonshot", "anthropic", "azure", "ollama", "vertex_ai", "mistral", "codestral", "volcengine", "groq", "cohere", "together_ai", "fireworks_ai", "openrouter", "replicate", "huggingface", "xai", "deepgram", "vllm", "bedrock", "cloudflare" ] # 如果当前 provider 不在列表中，添加到列表头部 if current_provider not in LITELLM_PROVIDERS: LITELLM_PROVIDERS.insert(0, current_provider) # 渲染配置输入框 col1, col2 = st.columns([1, 2]) with col1: selected_provider = st.selectbox( tr("Text Model Provider"), options=LITELLM_PROVIDERS, index=LITELLM_PROVIDERS.index(current_provider) if current_provider in LITELLM_PROVIDERS else 0, key="text_provider_select" ) with col2: model_name_input = st.text_input( tr("Text Model Name"), value=current_model, help="输入模型名称（不包含 provider 前缀）\n\n" "常用示例:\n" "• deepseek-chat\n" "• gpt-4o\n" "• gemini-2.0-flash\n" "• deepseek-ai/DeepSeek-R1 (SiliconFlow)\n\n" "支持 100+ providers，详见: https://docs.litellm.ai/docs/providers", key="text_model_input" ) # 组合完整的模型名称 st_text_model_name = f"{selected_provider}/{model_name_input}" if selected_provider and model_name_input else "" st_text_api_key = st.text_input( tr("Text API Key"), value=text_api_key, type="password", help="对应 provider 的 API 密钥\n\n" "获取地址:\n" "• DeepSeek: https://platform.deepseek.com/api_keys\n" "• Gemini: https://makersuite.google.com/app/apikey\n" "• OpenAI: https://platform.openai.com/api-keys\n" "• Qwen: https://bailian.console.aliyun.com/\n" "• SiliconFlow: https://cloud.siliconflow.cn/account/ak\n" "• Moonshot: https://platform.moonshot.cn/console/api-keys" ) text_base_help, text_base_required, text_placeholder = build_base_url_help( selected_provider, "文案生成模型" ) st_text_base_url = st.text_input( tr("Text Base URL"), value=text_base_url, help=text_base_help, placeholder=text_placeholder or None ) if text_base_required and not st_text_base_url: info_example = text_placeholder or "https://your-openai-compatible-endpoint/v1" st.info(f"请在上方填写 OpenAI 兼容网关地址，例如：{info_example}") # 添加测试连接按钮 if st.button(tr("Test Connection"), key="test_text_connection"): test_errors = [] if not st_text_api_key: test_errors.append("请先输入 API 密钥") if not model_name_input: test_errors.append("请先输入模型名称") if test_errors: for error in test_errors: st.error(error) else: with st.spinner(tr("Testing connection...")): try: success, message = test_litellm_text_model( api_key=st_text_api_key, base_url=st_text_base_url, model_name=st_text_model_name, tr=tr ) if success: st.success(message) else: st.error(message) except Exception as e: st.error(f"测试连接时发生错误: {str(e)}") logger.error(f"LiteLLM 文案生成模型连接测试失败: {str(e)}") # 验证和保存配置 text_validation_errors = [] text_config_changed = False # 验证模型名称 if st_text_model_name: is_valid, error_msg = validate_litellm_model_name(st_text_model_name, "文案生成") if is_valid: config.app["text_litellm_model_name"] = st_text_model_name st.session_state["text_litellm_model_name"] = st_text_model_name text_config_changed = True else: text_validation_errors.append(error_msg) # 验证 API 密钥 if st_text_api_key: is_valid, error_msg = validate_api_key(st_text_api_key, "文案生成") if is_valid: config.app["text_litellm_api_key"] = st_text_api_key st.session_state["text_litellm_api_key"] = st_text_api_key text_config_changed = True else: text_validation_errors.append(error_msg) # 验证 Base URL（可选） if st_text_base_url: is_valid, error_msg = validate_base_url(st_text_base_url, "文案生成") if is_valid: config.app["text_litellm_base_url"] = st_text_base_url st.session_state["text_litellm_base_url"] = st_text_base_url text_config_changed = True else: text_validation_errors.append(error_msg) # 显示验证错误 show_config_validation_errors(text_validation_errors) # 保存配置 if text_config_changed and not text_validation_errors: try: config.save_config() # 清除缓存，确保下次使用新配置 UnifiedLLMService.clear_cache() if st_text_api_key or st_text_base_url or st_text_model_name: st.success(f"文案生成模型配置已保存（LiteLLM）") except Exception as e: st.error(f"保存配置失败: {str(e)}") logger.error(f"保存文案生成配置失败: {str(e)}") # # Cloudflare 特殊配置 # if text_provider == 'cloudflare': # st_account_id = st.text_input( # tr("Account ID"), # value=config.app.get(f"text_{text_provider}_account_id", "") # ) # if st_account_id: # config.app[f"text_{text_provider}_account_id"] = st_account_id