diff --git a/.env.example b/.env.example index 9d3b64d..80d821a 100644 --- a/.env.example +++ b/.env.example @@ -1,3 +1,26 @@ +# ----------------------------------------------------------------------------- +# Video Note Generator - 环境变量示例 +# ----------------------------------------------------------------------------- +# +# 请将此文件复制为 .env 并填入必要的值。 +# +# ----------------------------------------------------------------------------- +# AI 提供者配置 +# ----------------------------------------------------------------------------- +# 选择您的 AI 提供者。有效值: "google" 或 "openrouter"。 +# 如果未设置或值无效，脚本将默认尝试使用 "openrouter"。 +AI_PROVIDER=openrouter + +# ----------------------------------------------------------------------------- +# Google AI Gemini API 配置 (仅当 AI_PROVIDER="google" 时需要) +# ----------------------------------------------------------------------------- +# 您的 Google AI Studio API 密钥或 Google Cloud AI Platform API 密钥 (用于 Gemini)。 +GOOGLE_API_KEY=YOUR_GOOGLE_API_KEY_HERE + +# 可选: 指定自定义的 Google Gemini 模型名称。 +# 如果AI_PROVIDER="google"且此项未设置，脚本中默认为 "gemini-pro"。 +GOOGLE_GEMINI_MODEL=gemini-pro + # OpenRouter API 配置（必需） OPENROUTER_API_KEY=your-api-key-here OPENROUTER_API_URL=https://openrouter.ai/api/v1 @@ -12,7 +35,7 @@ UNSPLASH_SECRET_KEY=your-unsplash-secret-key-here OUTPUT_DIR=generated_notes # Whisper 配置 -WHISPER_MODEL=medium # 可选: tiny, base, small, medium, large-v2 +WHISPER_MODEL=large-v2 # 可选: tiny, base, small, medium, large-v2 WHISPER_LANGUAGE=zh # 默认语言，可选：zh, en, ja 等 # FFmpeg 配置 diff --git a/video_note_generator.py b/video_note_generator.py index 106f3fb..618424f 100644 --- a/video_note_generator.py +++ b/video_note_generator.py @@ -24,28 +24,62 @@ # 加载环境变量 load_dotenv() +# AI 提供者配置 +# 用户可以在 .env 文件中设置 AI_PROVIDER 来选择 AI 服务 +# 可选值为 "google" 或 "openrouter" (默认为 "openrouter" 如果未指定) +AI_PROVIDER = os.getenv('AI_PROVIDER', 'openrouter').lower() + # 检查必要的环境变量 -required_env_vars = { - 'OPENROUTER_API_KEY': '用于OpenRouter API', - 'OPENROUTER_API_URL': '用于OpenRouter API', - 'OPENROUTER_APP_NAME': '用于OpenRouter API', - 'OPENROUTER_HTTP_REFERER': '用于OpenRouter API', - 'UNSPLASH_ACCESS_KEY': '用于图片搜索', - 'UNSPLASH_SECRET_KEY': '用于Unsplash认证', - 'UNSPLASH_REDIRECT_URI': '用于Unsplash回调' +base_required_env_vars = { + 'UNSPLASH_ACCESS_KEY': '用于图片搜索 (必须)', + 'UNSPLASH_SECRET_KEY': '用于Unsplash认证 (必须)', + 'UNSPLASH_REDIRECT_URI': '用于Unsplash回调 (必须)' } +provider_specific_env_vars = {} +if AI_PROVIDER == 'openrouter': + provider_specific_env_vars = { + 'OPENROUTER_API_KEY': '用于OpenRouter API', + # 'OPENROUTER_API_URL': '用于OpenRouter API (通常默认为 https://openrouter.ai/api/v1)', + # 'OPENROUTER_APP_NAME': '用于OpenRouter API (可选)', + # 'OPENROUTER_HTTP_REFERER': '用于OpenRouter API (可选)', + } + # 确保 OPENROUTER_API_URL 有默认值 + os.environ.setdefault('OPENROUTER_API_URL', 'https://openrouter.ai/api/v1') +elif AI_PROVIDER == 'google': + provider_specific_env_vars = { + 'GOOGLE_API_KEY': '用于 Google AI Gemini API' + } +else: + # This case should ideally not be reached if AI_PROVIDER has a default and is validated. + # However, as a fallback, assume openrouter if AI_PROVIDER is somehow invalid at this stage. + print(f"⚠️ AI_PROVIDER 设置为 '{AI_PROVIDER}'，这是一个无效的值。请在 .env 文件中将其设置为 'google' 或 'openrouter'。将默认使用 OpenRouter (如果已配置)。") + AI_PROVIDER = 'openrouter' + provider_specific_env_vars = { + 'OPENROUTER_API_KEY': '用于OpenRouter API', + } + os.environ.setdefault('OPENROUTER_API_URL', 'https://openrouter.ai/api/v1') + + +required_env_vars = {**base_required_env_vars, **provider_specific_env_vars} + missing_env_vars = [] for var, desc in required_env_vars.items(): if not os.getenv(var): missing_env_vars.append(f" - {var} ({desc})") if missing_env_vars: - print("注意：以下环境变量未设置：") + print("错误：以下必要的环境变量未设置：") print("\n".join(missing_env_vars)) - print("\n将使用基本功能继续运行（无AI优化和图片）。") - print("如需完整功能，请在 .env 文件中设置相应的 API 密钥。") - print("继续处理...\n") + print(f"\n请根据您选择的 AI 提供者 ({AI_PROVIDER}) 在 .env 文件中设置相应的 API 密钥。") + if AI_PROVIDER == 'google' and 'GOOGLE_API_KEY' in [v.split(' ')[0] for v in missing_env_vars]: + print("您选择了 AI_PROVIDER='google'，但 GOOGLE_API_KEY 未设置。") + elif AI_PROVIDER == 'openrouter' and 'OPENROUTER_API_KEY' in [v.split(' ')[0] for v in missing_env_vars]: + print("您选择了 AI_PROVIDER='openrouter' (或默认)，但 OPENROUTER_API_KEY 未设置。") + print("程序将退出。") + sys.exit(1) + +print(f"✅ AI Provider 已选择: {AI_PROVIDER.upper()}") # 配置代理 http_proxy = os.getenv('HTTP_PROXY') @@ -59,35 +93,71 @@ import ssl ssl._create_default_https_context = ssl._create_unverified_context -# OpenRouter configuration -openrouter_api_key = os.getenv('OPENROUTER_API_KEY') -openrouter_app_name = os.getenv('OPENROUTER_APP_NAME', 'video_note_generator') -openrouter_http_referer = os.getenv('OPENROUTER_HTTP_REFERER', 'https://github.com') -openrouter_available = False - -# 配置 OpenAI API -client = openai.OpenAI( - api_key=openrouter_api_key, - base_url="https://openrouter.ai/api/v1", - default_headers={ - "HTTP-Referer": openrouter_http_referer, - "X-Title": openrouter_app_name, - } -) +# AI Client and Model Configuration +openrouter_client = None +google_gemini_client = None +AI_MODEL_NAME = None +ai_client_available = False + +if AI_PROVIDER == 'openrouter': + openrouter_api_key = os.getenv('OPENROUTER_API_KEY') + openrouter_app_name = os.getenv('OPENROUTER_APP_NAME', 'video_note_generator') + openrouter_http_referer = os.getenv('OPENROUTER_HTTP_REFERER', 'https://github.com') + openrouter_api_url = os.getenv('OPENROUTER_API_URL') # Already has default from above + + if openrouter_api_key: + openrouter_client = openai.OpenAI( + api_key=openrouter_api_key, + base_url=openrouter_api_url, + default_headers={ + "HTTP-Referer": openrouter_http_referer, + "X-Title": openrouter_app_name, + } + ) + try: + print(f"正在测试 OpenRouter API 连接 (模型列表)...") + openrouter_client.models.list() + print("✅ OpenRouter API 连接测试成功") + ai_client_available = True + AI_MODEL_NAME = os.getenv('OPENROUTER_MODEL', "openai/gpt-3.5-turbo") # Default OpenRouter model + print(f"✅ OpenRouter 模型已设置为: {AI_MODEL_NAME}") + except Exception as e: + print(f"⚠️ OpenRouter API 连接测试失败: {str(e)}") + print("如果您希望使用OpenRouter，请检查您的API密钥和网络连接。") + # Proceeding, but AI functions relying on OpenRouter might fail. + else: + print("⚠️ OpenRouter API Key 未设置。如果选择OpenRouter作为AI Provider，相关功能将不可用。") + +elif AI_PROVIDER == 'google': + google_api_key = os.getenv('GOOGLE_API_KEY') + if google_api_key: + try: + import google.generativeai as genai + genai.configure(api_key=google_api_key) + # Test connection by listing models (or a similar lightweight call if available) + # For Gemini, model listing might require specific permissions or might not be the best test. + # We'll assume configuration is successful if no immediate error. + # Actual model usage will confirm. + google_gemini_client = genai # Store the configured module + print("✅ Google AI Gemini API 配置初步成功 (SDK已加载)") + ai_client_available = True + AI_MODEL_NAME = os.getenv('GOOGLE_GEMINI_MODEL', "gemini-pro") # Default Google Gemini model + print(f"✅ Google Gemini 模型已设置为: {AI_MODEL_NAME}") + except ImportError: + print("⚠️ Google AI SDK (google-generativeai) 未安装。") + print("请运行 'pip install google-generativeai' 来安装它。") + print("Google AI Gemini 功能将不可用。") + except Exception as e: + print(f"⚠️ Google AI Gemini API 配置失败: {str(e)}") + print("请检查您的 GOOGLE_API_KEY 和网络连接。") + # Proceeding, but AI functions relying on Google Gemini might fail. + else: + print("⚠️ Google API Key 未设置。如果选择Google作为AI Provider，相关功能将不可用。") -# 选择要使用的模型 -AI_MODEL = "google/gemini-pro" # 使用 Gemini Pro 模型 +if not ai_client_available: + print("⚠️ AI客户端未能成功初始化。AI相关功能（内容整理、小红书版本生成等）将不可用。") + print("请检查您的 .env 文件中的 API 密钥配置和网络连接。") -# Test OpenRouter connection -if openrouter_api_key: - try: - print(f"正在测试 OpenRouter API 连接...") - response = client.models.list() # 使用更简单的API调用来测试连接 - print("✅ OpenRouter API 连接测试成功") - openrouter_available = True - except Exception as e: - print(f"⚠️ OpenRouter API 连接测试失败: {str(e)}") - print("将继续尝试使用API，但可能会遇到问题") # 检查Unsplash配置 unsplash_access_key = os.getenv('UNSPLASH_ACCESS_KEY') @@ -137,7 +207,7 @@ def __init__(self, output_dir: str = "temp_notes"): self.output_dir = output_dir os.makedirs(output_dir, exist_ok=True) - self.openrouter_available = openrouter_available + self.ai_client_available = ai_client_available # Use the global ai_client_available self.unsplash_client = unsplash_client self.ffmpeg_path = ffmpeg_path @@ -153,6 +223,58 @@ def __init__(self, output_dir: str = "temp_notes"): # 日志目录 self.log_dir = os.path.join(self.output_dir, 'logs') + + def _call_gemini_api(self, system_prompt: str, user_prompt: str) -> Optional[str]: + """Helper function to call Google Gemini API.""" + if not google_gemini_client or not AI_MODEL_NAME: + print("⚠️ Google Gemini client or model name not configured.") + return None + try: + print(f"🤖 Calling Google Gemini API (model: {AI_MODEL_NAME})...") + # Gemini API typically takes a list of Parts or string content. + # For system prompt like behavior, you might prepend it or use specific model features if available. + # Simple concatenation for now, or structured input if the model supports it well. + # Google's newer models might prefer a structured {role: "user", parts: [{text: "..."}]} + # For gemini-pro, a direct content generation with a combined prompt is common. + + model = google_gemini_client.GenerativeModel(AI_MODEL_NAME) + + # Constructing the prompt for Gemini. + # Gemini's API is slightly different. It doesn't have a direct "system" role in the same way as OpenAI. + # Often, instructions are part of the user prompt or handled by model tuning. + # We can prepend the system prompt to the user prompt. + full_prompt = f"{system_prompt}\n\n{user_prompt}" + + # Ensure the prompt is passed as a list of content parts if that's what the SDK expects + # For simple text, just passing the string might work. + # response = model.generate_content(full_prompt) + + # A more robust way, mimicking chat, would be: + # chat = model.start_chat(history=[ + # {"role": "user", "parts": [{"text": system_prompt}]}, # Or treat system prompt as preamble + # {"role": "model", "parts": [{"text": "Okay, I understand my role."}]} # Dummy model response + # ]) + # response = chat.send_message(user_prompt) + + # Let's try a direct generation approach, combining prompts + # This is a common way for models that don't explicitly differentiate system/user roles in API calls. + response = model.generate_content(full_prompt) + + if response and response.text: + return response.text.strip() + elif response and response.candidates and response.candidates[0].content and response.candidates[0].content.parts: + # Handle cases where response.text might be empty but candidates are available + return response.candidates[0].content.parts[0].text.strip() + else: + print(f"⚠️ Google Gemini API returned an empty response or unexpected format.") + if response: + print(f"Full response object: {response}") + return None + except Exception as e: + print(f"⚠️ Google Gemini API call failed: {str(e)}") + import traceback + print(traceback.format_exc()) + return None os.makedirs(self.log_dir, exist_ok=True) # cookie目录 @@ -424,13 +546,12 @@ def _transcribe_audio(self, audio_path: str) -> str: def _organize_content(self, content: str) -> str: """使用AI整理内容""" - try: - if not self.openrouter_available: - print("⚠️ OpenRouter API 未配置，将返回原始内容") - return content + if not ai_client_available: + print("⚠️ AI client not available. Returning original content.") + return content - # 构建系统提示词 - system_prompt = """你是一位著名的科普作家和博客作者，著作等身，屡获殊荣，尤其在内容创作领域有深厚的造诣。 + # 构建系统提示词 + system_prompt = """你是一位著名的科普作家和博客作者，著作等身，屡获殊荣，尤其在内容创作领域有深厚的造诣。 请使用 4C 模型（建立联系 Connection、展示冲突 Conflict、强调改变 Change、即时收获 Catch）为转录的文字内容创建结构。 @@ -452,31 +573,53 @@ def _organize_content(self, content: str) -> str: - 如有来源URL，使用文内链接形式 - 保留原文中的Markdown格式图片链接""" - # 构建用户提示词 - final_prompt = f"""请根据以下转录文字内容，创作一篇结构清晰、易于理解的博客文章。 + # 构建用户提示词 + user_prompt = f"""请根据以下转录文字内容，创作一篇结构清晰、易于理解的博客文章。 转录文字内容： {content}""" - # 调用API - response = client.chat.completions.create( - model=AI_MODEL, - messages=[ - {"role": "system", "content": system_prompt}, - {"role": "user", "content": final_prompt} - ], - temperature=0.7, - max_tokens=4000 - ) - - if response.choices: - return response.choices[0].message.content.strip() + try: + if AI_PROVIDER == 'google': + if not google_gemini_client: + print("⚠️ Google AI Provider selected, but client not initialized. Returning original content.") + return content + organized_text = self._call_gemini_api(system_prompt, user_prompt) + if organized_text: + return organized_text + print("⚠️ _call_gemini_api returned None. Returning original content for _organize_content.") + return content - return content + elif AI_PROVIDER == 'openrouter': + if not openrouter_client: + print("⚠️ OpenRouter AI Provider selected, but client not initialized. Returning original content.") + return content + + print(f"🤖 Calling OpenRouter API (model: {AI_MODEL_NAME})...") + response = openrouter_client.chat.completions.create( + model=AI_MODEL_NAME, + messages=[ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt} + ], + temperature=0.7, + max_tokens=4000 # Consider if this needs adjustment for different models + ) + if response.choices and response.choices[0].message and response.choices[0].message.content: + return response.choices[0].message.content.strip() + else: + print(f"⚠️ OpenRouter API returned an empty or unexpected response: {response}") + return content + else: + # Should not happen due to checks at the beginning, but as a safeguard: + print(f"⚠️ Unknown AI_PROVIDER '{AI_PROVIDER}'. Returning original content.") + return content except Exception as e: - print(f"⚠️ 内容整理失败: {str(e)}") + print(f"⚠️ 内容整理失败 ({AI_PROVIDER} API): {str(e)}") + import traceback + print(traceback.format_exc()) return content def split_content(self, text: str, max_chars: int = 2000) -> List[str]: @@ -568,8 +711,8 @@ def _organize_long_content(self, content: str, duration: int = 0) -> str: if not content.strip(): return "" - if not self.openrouter_available: - print("⚠️ OpenRouter API 不可用，将返回原始内容") + if not ai_client_available: # Check generic AI availability first + print("⚠️ AI client not available for long content organization. Returning original content.") return content content_chunks = self.split_content(content) @@ -586,13 +729,12 @@ def _organize_long_content(self, content: str, duration: int = 0) -> str: def convert_to_xiaohongshu(self, content: str) -> Tuple[str, List[str], List[str], List[str]]: """将博客文章转换为小红书风格的笔记，并生成标题和标签""" - try: - if not self.openrouter_available: - print("⚠️ OpenRouter API 未配置，将返回原始内容") - return content, [], [], [] + if not ai_client_available: + print("⚠️ AI client not available for Xiaohongshu conversion. Returning original content.") + return content, [], [], [] - # 构建系统提示词 - system_prompt = """你是一位专业的小红书爆款文案写作大师，擅长将普通内容转换为刷屏级爆款笔记。 + # 构建系统提示词 (This prompt is quite long and detailed) + system_prompt = """你是一位专业的小红书爆款文案写作大师，擅长将普通内容转换为刷屏级爆款笔记。 请将输入的内容转换为小红书风格的笔记，需要满足以下要求： 1. 标题创作（重要‼️）： @@ -671,28 +813,52 @@ def convert_to_xiaohongshu(self, content: str) -> Tuple[str, List[str], List[str """ - # 调用API - response = client.chat.completions.create( - model=AI_MODEL, - messages=[ - {"role": "system", "content": system_prompt}, - {"role": "user", "content": user_prompt} - ], - temperature=0.7, - max_tokens=2000 - ) + try: + xiaohongshu_text_from_api = None + if AI_PROVIDER == 'google': + if not google_gemini_client: + print("⚠️ Google AI Provider selected, but client not initialized for Xiaohongshu conversion.") + return content, [], [], [] + xiaohongshu_text_from_api = self._call_gemini_api(system_prompt, user_prompt) - if not response.choices: - raise Exception("API 返回结果为空") + elif AI_PROVIDER == 'openrouter': + if not openrouter_client: + print("⚠️ OpenRouter AI Provider selected, but client not initialized for Xiaohongshu conversion.") + return content, [], [], [] + + print(f"🤖 Calling OpenRouter API for Xiaohongshu (model: {AI_MODEL_NAME})...") + response = openrouter_client.chat.completions.create( + model=AI_MODEL_NAME, + messages=[ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt} + ], + temperature=0.7, + max_tokens=2000 # Consider if this needs adjustment + ) + if response.choices and response.choices[0].message and response.choices[0].message.content: + xiaohongshu_text_from_api = response.choices[0].message.content.strip() + else: + print(f"⚠️ OpenRouter API returned an empty or unexpected response for Xiaohongshu: {response}") + return content, [], [], [] + else: + print(f"⚠️ Unknown AI_PROVIDER '{AI_PROVIDER}' for Xiaohongshu conversion.") + return content, [], [], [] - # 处理返回的内容 - xiaohongshu_content = response.choices[0].message.content.strip() - print(f"\n📝 API返回内容：\n{xiaohongshu_content}\n") + if not xiaohongshu_text_from_api: + print("⚠️ AI API call returned no content for Xiaohongshu conversion.") + return content, [], [], [] + + print(f"\n📝 API返回内容 (Xiaohongshu)：\n{xiaohongshu_text_from_api}\n") + + # Process the API response to extract title, tags, etc. + # (The existing logic for splitting and extracting should largely remain the same, + # operating on xiaohongshu_text_from_api) # 提取标题（第一行） - content_lines = xiaohongshu_content.split('\n') + # content_lines = xiaohongshu_text_from_api.split('\n') # Corrected: remove duplicate below titles = [] - for line in content_lines: + for line in xiaohongshu_text_from_api.split('\n'): # Use the new variable line = line.strip() if line and not line.startswith('#') and '：' not in line and '。' not in line: titles = [line] @@ -701,7 +867,7 @@ def convert_to_xiaohongshu(self, content: str) -> Tuple[str, List[str], List[str if not titles: print("⚠️ 未找到标题，尝试其他方式提取...") # 尝试其他方式提取标题 - title_match = re.search(r'^[^#\n]+', xiaohongshu_content) + title_match = re.search(r'^[^#\n]+', xiaohongshu_text_from_api) # Use the new variable if title_match: titles = [title_match.group(0).strip()] @@ -712,7 +878,7 @@ def convert_to_xiaohongshu(self, content: str) -> Tuple[str, List[str], List[str # 提取标签（查找所有#开头的标签） tags = [] - tag_matches = re.findall(r'#([^\s#]+)', xiaohongshu_content) + tag_matches = re.findall(r'#([^\s#]+)', xiaohongshu_text_from_api) # Use the new variable if tag_matches: tags = tag_matches print(f"✅ 提取到{len(tags)}个标签") @@ -748,21 +914,7 @@ def _get_unsplash_images(self, query: str, count: int = 3) -> List[str]: try: # 将查询词翻译成英文以获得更好的结果 - if self.openrouter_available: - try: - response = client.chat.completions.create( - model=AI_MODEL, - messages=[ - {"role": "system", "content": "你是一个翻译助手。请将输入的中文关键词翻译成最相关的1-3个英文关键词，用逗号分隔。直接返回翻译结果，不要加任何解释。例如：\n输入：'保险理财知识'\n输出：insurance,finance,investment"}, - {"role": "user", "content": query} - ], - temperature=0.3, - max_tokens=50 - ) - if response.choices: - query = response.choices[0].message.content.strip() - except Exception as e: - print(f"⚠️ 翻译关键词失败: {str(e)}") + translated_query = self._translate_text_for_image_search(query) # 使用httpx直接调用Unsplash API headers = { @@ -771,7 +923,7 @@ def _get_unsplash_images(self, query: str, count: int = 3) -> List[str]: # 对每个关键词分别搜索 all_photos = [] - for keyword in query.split(','): + for keyword in translated_query.split(','): # Use translated_query response = httpx.get( 'https://api.unsplash.com/search/photos', params={ @@ -793,11 +945,11 @@ def _get_unsplash_images(self, query: str, count: int = 3) -> List[str]: all_photos.extend(photos) # 如果收集到的图片不够，用最后一个关键词继续搜索 - while len(all_photos) < count and query: + while len(all_photos) < count and translated_query: # Use translated_query response = httpx.get( 'https://api.unsplash.com/search/photos', params={ - 'query': query.split(',')[-1].strip(), + 'query': translated_query.split(',')[-1].strip(), # Use translated_query 'per_page': count - len(all_photos), 'orientation': 'portrait', 'content_filter': 'high', @@ -825,6 +977,55 @@ def _get_unsplash_images(self, query: str, count: int = 3) -> List[str]: print(f"⚠️ 获取图片失败: {str(e)}") return [] + def _translate_text_for_image_search(self, query: str) -> str: + """Helper function to translate text using the configured AI provider for image search.""" + if not ai_client_available or not query: + print("⚠️ AI client not available for translation or empty query.") + return query # Return original query + + system_prompt = "你是一个翻译助手。请将输入的中文关键词翻译成最相关的1-3个英文关键词，用逗号分隔。直接返回翻译结果，不要加任何解释。例如：\n输入：'保险理财知识'\n输出：insurance,finance,investment" + user_prompt = query + + try: + translated_query = None + if AI_PROVIDER == 'google': + if not google_gemini_client: + print("⚠️ Google AI Provider selected, but client not initialized for translation.") + return query + translated_query = self._call_gemini_api(system_prompt, user_prompt) + + elif AI_PROVIDER == 'openrouter': + if not openrouter_client: + print("⚠️ OpenRouter AI Provider selected, but client not initialized for translation.") + return query + + print(f"🤖 Calling OpenRouter API for translation (model: {AI_MODEL_NAME})...") # Consider a smaller/cheaper model for translation + response = openrouter_client.chat.completions.create( + model=AI_MODEL_NAME, # Ideally, a model suitable for translation + messages=[ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt} + ], + temperature=0.3, + max_tokens=50 + ) + if response.choices and response.choices[0].message and response.choices[0].message.content: + translated_query = response.choices[0].message.content.strip() + else: + print(f"⚠️ Unknown AI_PROVIDER '{AI_PROVIDER}' for translation.") + return query + + if translated_query: + print(f"📝 Translated image search query from '{query}' to '{translated_query}'") + return translated_query + else: + print(f"⚠️ Translation failed, using original query: '{query}'") + return query + + except Exception as e: + print(f"⚠️ 翻译关键词失败 ({AI_PROVIDER} API): {str(e)}") + return query # Fallback to original query + def process_video(self, url: str) -> List[str]: """处理视频链接，生成笔记 @@ -898,7 +1099,10 @@ def process_video(self, url: str) -> List[str]: # 写入文件 with open(xiaohongshu_file, "w", encoding="utf-8") as f: # 写入标题 - f.write(f"# {titles[0]}\n\n") + if titles: + f.write(f"# {titles[0]}\n\n") + else: + f.write(f"# 未能生成标题\n\n") # 提供一个默认标题或错误提示 # 如果有图片，先写入第一张作为封面 if images: