""" 迁移适配器 为现有代码提供向后兼容的接口，方便逐步迁移到新的LLM服务架构 """ import asyncio from typing import List, Dict, Any, Optional, Union from pathlib import Path import PIL.Image from loguru import logger from .unified_service import UnifiedLLMService from .exceptions import LLMServiceError from .manager import LLMServiceManager # 导入新的提示词管理系统 from app.services.prompts import PromptManager # 提供商注册由 webui.py:main() 显式调用（见 LLM 提供商注册机制重构） # 这样更可靠，错误也更容易调试 def _run_async_safely(coro_func, *args, **kwargs): """ 安全地运行异步协程，处理各种事件循环情况 Args: coro_func: 协程函数（不是协程对象） *args: 协程函数的位置参数 **kwargs: 协程函数的关键字参数 Returns: 协程的执行结果 """ def run_in_new_loop(): """在新的事件循环中运行协程""" loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) try: return loop.run_until_complete(coro_func(*args, **kwargs)) finally: loop.close() asyncio.set_event_loop(None) try: # 尝试获取当前事件循环 try: loop = asyncio.get_running_loop() # 如果有运行中的事件循环，使用线程池执行 import concurrent.futures with concurrent.futures.ThreadPoolExecutor() as executor: future = executor.submit(run_in_new_loop) return future.result() except RuntimeError: # 没有运行中的事件循环，直接运行 return run_in_new_loop() except Exception as e: logger.error(f"异步执行失败: {str(e)}") raise LLMServiceError(f"异步执行失败: {str(e)}") class LegacyLLMAdapter: """传统LLM接口适配器""" @staticmethod def create_vision_analyzer(provider: str, api_key: str, model: str, base_url: str = None): """ 创建视觉分析器实例 - 兼容原有接口 Args: provider: 提供商名称 api_key: API密钥 model: 模型名称 base_url: API基础URL Returns: 适配器实例 """ return VisionAnalyzerAdapter(provider, api_key, model, base_url) @staticmethod def generate_narration(markdown_content: str, api_key: str, base_url: str, model: str) -> str: """ 生成解说文案 - 兼容原有接口 Args: markdown_content: Markdown格式的视频帧分析内容 api_key: API密钥 base_url: API基础URL model: 模型名称 Returns: 生成的解说文案JSON字符串 """ try: # 使用新的提示词管理系统 prompt = PromptManager.get_prompt( category="documentary", name="narration_generation", parameters={ "video_frame_description": markdown_content } ) # 使用统一服务生成文案 result = _run_async_safely( UnifiedLLMService.generate_text, prompt=prompt, system_prompt="你是一名专业的短视频解说文案撰写专家。", temperature=1.5, response_format="json" ) return result if isinstance(result, str) else str(result) except Exception as e: logger.error(f"生成解说文案失败: {str(e)}") raise class VisionAnalyzerAdapter: """视觉分析器适配器""" def __init__(self, provider: str, api_key: str, model: str, base_url: str = None): self.provider = provider self.api_key = api_key self.model = model self.base_url = base_url def _build_provider_with_explicit_settings(self): provider_name = (self.provider or "").lower() if not LLMServiceManager.is_registered(): from .providers import register_all_providers register_all_providers() provider_class = LLMServiceManager._vision_providers.get(provider_name) if provider_class is None: raise LLMServiceError(f"视觉模型提供商未注册: {provider_name}") return provider_class( api_key=self.api_key, model_name=self.model, base_url=self.base_url, ) async def analyze_images(self, images: List[Union[str, Path, PIL.Image.Image]], prompt: str, batch_size: int = 10, max_concurrency: int = 1) -> List[Dict[str, Any]]: """ 分析图片 - 兼容原有接口 Args: images: 图片列表 prompt: 分析提示词 batch_size: 批处理大小 max_concurrency: 最大并发批次数 Returns: 分析结果列表，格式与旧实现兼容 """ try: provider = self._build_provider_with_explicit_settings() results = await provider.analyze_images( images=images, prompt=prompt, batch_size=batch_size, max_concurrency=max_concurrency, api_key=self.api_key, api_base=self.base_url, ) # 转换为旧格式以保持向后兼容性 # 新实现返回 List[str]，需要转换为 List[Dict] compatible_results = [] for i, result in enumerate(results): # 计算这个批次处理的图片数量 start_idx = i * batch_size end_idx = min(start_idx + batch_size, len(images)) images_processed = end_idx - start_idx compatible_results.append({ 'batch_index': i, 'images_processed': images_processed, 'response': result, 'model_used': self.model }) logger.info(f"图片分析完成，共处理 {len(images)} 张图片，生成 {len(compatible_results)} 个批次结果") return compatible_results except Exception as e: logger.error(f"图片分析失败: {str(e)}") raise class SubtitleAnalyzerAdapter: """字幕分析器适配器""" def __init__( self, api_key: str, model: str, base_url: str, provider: str = None, prompt_category: str = "short_drama_narration", ): self.api_key = api_key self.model = model self.base_url = base_url self.provider = provider or "openai" self.prompt_category = prompt_category or "short_drama_narration" def _run_async_safely(self, coro_func, *args, **kwargs): """安全地运行异步协程""" return _run_async_safely(coro_func, *args, **kwargs) def _clean_json_output(self, output: str) -> str: """清理JSON输出，移除markdown标记等""" import re # 移除可能的markdown代码块标记 output = re.sub(r'^```json\s*', '', output, flags=re.MULTILINE) output = re.sub(r'^```\s*$', '', output, flags=re.MULTILINE) output = re.sub(r'^```.*$', '', output, flags=re.MULTILINE) # 移除开头和结尾的```标记 output = re.sub(r'^```', '', output) output = re.sub(r'```$', '', output) # 移除前后空白字符 output = output.strip() return output def _render_prompt(self, name: str, parameters: Dict[str, Any]) -> tuple[str, Optional[str]]: prompt = PromptManager.get_prompt( category=self.prompt_category, name=name, parameters=parameters, ) prompt_object = PromptManager.get_prompt_object( category=self.prompt_category, name=name, ) return prompt, prompt_object.get_system_prompt() def _generate_json_text( self, prompt: str, system_prompt: Optional[str], temperature: float, stream_callback=None, ) -> str: generate_func = ( UnifiedLLMService.generate_text_stream if stream_callback else UnifiedLLMService.generate_text ) kwargs = { "prompt": prompt, "system_prompt": system_prompt, "provider": self.provider, "temperature": temperature, "response_format": "json", "api_key": self.api_key, "api_base": self.base_url, } if stream_callback: kwargs["on_chunk"] = stream_callback result = self._run_async_safely(generate_func, **kwargs) return self._clean_json_output(result) def _generate_plain_text(self, prompt: str, system_prompt: Optional[str], temperature: float) -> str: result = self._run_async_safely( UnifiedLLMService.generate_text, prompt=prompt, system_prompt=system_prompt, provider=self.provider, temperature=temperature, api_key=self.api_key, api_base=self.base_url, ) return str(result or "").strip() def generate_narration_copy( self, short_name: str, plot_analysis: str, subtitle_content: str = "", temperature: float = 0.7, narration_language: str = "简体中文（中国）", drama_genre: str = "逆袭/复仇", ) -> Dict[str, Any]: """Generate editable narration copy before timeline matching.""" try: prompt, system_prompt = self._render_prompt( "narration_copy", { "drama_name": short_name, "drama_genre": drama_genre, "plot_analysis": plot_analysis, "subtitle_content": subtitle_content, "narration_language": narration_language, }, ) narration_copy = self._generate_plain_text(prompt, system_prompt, temperature) return { "status": "success", "narration_copy": narration_copy, "model": self.model, "temperature": temperature, } except Exception as e: logger.error(f"解说文案正文生成失败: {str(e)}") return { "status": "error", "message": str(e), "temperature": temperature, } def match_narration_copy_to_script( self, short_name: str, plot_analysis: str, subtitle_content: str, narration_copy: str, temperature: float = 0.3, narration_language: str = "简体中文（中国）", drama_genre: str = "逆袭/复仇", original_sound_ratio: int = 30, stream_callback=None, ) -> Dict[str, Any]: """Match reviewed narration copy to source footage and return JSON script.""" try: prompt, system_prompt = self._render_prompt( "script_matching", { "drama_name": short_name, "drama_genre": drama_genre, "plot_analysis": plot_analysis, "subtitle_content": subtitle_content, "narration_copy": narration_copy, "narration_language": narration_language, "original_sound_ratio": int(original_sound_ratio), }, ) narration_script = self._generate_json_text( prompt, system_prompt, min(float(temperature), 0.3), stream_callback=stream_callback, ) return { "status": "success", "narration_script": narration_script, "model": self.model, "temperature": temperature, } except Exception as e: logger.error(f"解说文案画面匹配失败: {str(e)}") return { "status": "error", "message": str(e), "temperature": temperature, } def plan_narration_segments( self, short_name: str, plot_analysis: str, subtitle_content: str = "", temperature: float = 0.3, narration_language: str = "简体中文（中国）", drama_genre: str = "逆袭/复仇", ) -> str: """Plan source segments before generating final copy.""" prompt, system_prompt = self._render_prompt( "segment_planning", { "drama_name": short_name, "drama_genre": drama_genre, "plot_analysis": plot_analysis, "subtitle_content": subtitle_content, "narration_language": narration_language, }, ) return self._generate_json_text(prompt, system_prompt, min(float(temperature), 0.3)) def generate_narration_script_from_plan( self, short_name: str, plot_analysis: str, subtitle_content: str, segment_plan: str, temperature: float = 0.7, narration_language: str = "简体中文（中国）", drama_genre: str = "逆袭/复仇", ) -> str: prompt, system_prompt = self._render_prompt( "script_generation", { "drama_name": short_name, "drama_genre": drama_genre, "plot_analysis": plot_analysis, "subtitle_content": subtitle_content, "segment_plan": segment_plan, "narration_language": narration_language, }, ) return self._generate_json_text(prompt, system_prompt, temperature) def repair_narration_script( self, short_name: str, plot_analysis: str, subtitle_content: str, invalid_script: str, validation_errors: str, temperature: float = 0.3, narration_language: str = "简体中文（中国）", drama_genre: str = "逆袭/复仇", stream_callback=None, ) -> Dict[str, Any]: """Repair a generated script once after deterministic validation fails.""" try: prompt, system_prompt = self._render_prompt( "script_repair", { "drama_name": short_name, "drama_genre": drama_genre, "plot_analysis": plot_analysis, "subtitle_content": subtitle_content, "invalid_script": invalid_script, "validation_errors": validation_errors, "narration_language": narration_language, }, ) repaired_script = self._generate_json_text( prompt, system_prompt, min(float(temperature), 0.3), stream_callback=stream_callback, ) return { "status": "success", "narration_script": repaired_script, "model": self.model, "temperature": temperature, } except Exception as e: logger.error(f"解说文案修复失败: {str(e)}") return { "status": "error", "message": str(e), "temperature": temperature, } def analyze_subtitle(self, subtitle_content: str) -> Dict[str, Any]: """ 分析字幕内容 - 兼容原有接口 Args: subtitle_content: 字幕内容 Returns: 分析结果字典 """ try: # 使用统一服务分析字幕 result = self._run_async_safely( UnifiedLLMService.analyze_subtitle, subtitle_content=subtitle_content, provider=self.provider, temperature=1.0, prompt_category=self.prompt_category, api_key=self.api_key, api_base=self.base_url ) return { "status": "success", "analysis": result, "model": self.model, "temperature": 1.0 } except Exception as e: logger.error(f"字幕分析失败: {str(e)}") return { "status": "error", "message": str(e), "temperature": 1.0 } def generate_narration_script( self, short_name: str, plot_analysis: str, subtitle_content: str = "", temperature: float = 0.7, narration_language: str = "简体中文（中国）", drama_genre: str = "逆袭/复仇", ) -> Dict[str, Any]: """ 生成解说文案 - 兼容原有接口 Args: short_name: 短剧名称 plot_analysis: 剧情分析内容 subtitle_content: 原始字幕内容，用于提供准确的时间戳信息 temperature: 生成温度 narration_language: 解说台词目标语言 Returns: 生成结果字典 """ try: segment_plan = self.plan_narration_segments( short_name=short_name, plot_analysis=plot_analysis, subtitle_content=subtitle_content, temperature=temperature, narration_language=narration_language, drama_genre=drama_genre, ) cleaned_result = self.generate_narration_script_from_plan( short_name=short_name, plot_analysis=plot_analysis, subtitle_content=subtitle_content, segment_plan=segment_plan, temperature=temperature, narration_language=narration_language, drama_genre=drama_genre, ) # 新的提示词系统返回的是包含items数组的JSON格式 # 为了保持向后兼容，我们需要直接返回这个JSON字符串 # 调用方会期望这是一个包含items数组的JSON字符串 return { "status": "success", "narration_script": cleaned_result, "model": self.model, "temperature": temperature } except Exception as e: logger.error(f"解说文案生成失败: {str(e)}") return { "status": "error", "message": str(e), "temperature": temperature } # 为了向后兼容，提供一些全局函数 def create_vision_analyzer(provider: str, api_key: str, model: str, base_url: str = None): """创建视觉分析器 - 全局函数""" return LegacyLLMAdapter.create_vision_analyzer(provider, api_key, model, base_url) def generate_narration(markdown_content: str, api_key: str, base_url: str, model: str) -> str: """生成解说文案 - 全局函数""" return LegacyLLMAdapter.generate_narration(markdown_content, api_key, base_url, model)