""" 脚本生成服务 ============ """ import asyncio import logging import re import time from collections.abc import AsyncIterator from pathlib import Path from app.ai.model_router import get_model_router from app.ai.prompts import load_script_user_prompt, load_system_prompt from app.schemas.script import ScriptGenerationEvent, ScriptShot from app.services.ai_response_utils import ( safe_parse_ai_json_response, validate_and_normalize_shots, ) logger = logging.getLogger(__name__) class ScriptService: """脚本生成服务""" def __init__(self): self.prompts_dir = Path(__file__).parent.parent / "ai" / "prompts" def _load_prompt(self, name: str) -> str: """加载 Prompt 模板""" prompt_file = self.prompts_dir / f"{name}.txt" if prompt_file.exists(): return prompt_file.read_text(encoding="utf-8") return "" @staticmethod def _extract_json(content: str) -> str: """ 从 Markdown 代码块中提取 JSON，或返回原始内容 支持格式： - ```json {...} ``` - ``` {...} ``` - 纯 JSON 文本 """ if not content: return "" content = content.strip() # 匹配 ```json ... ``` 或 ``` ... ``` pattern = r"```(?:json)?\s*([\s\S]*?)\s*```" matches = re.findall(pattern, content) if matches: # 取最后一个匹配（避免前面有示例代码） return matches[-1].strip() # 如果没有代码块，返回原始内容 return content async def generate_script( self, category: str, subcategory: str, duration: int, script_type: str, model: str | None = None, ) -> list[ScriptShot]: """ 同步生成脚本 Args: category: 大类代码，如 "bk" subcategory: 小类代码，如 "ht" duration: 视频时长（秒） script_type: 脚本类型 model: 指定模型 Returns: 分镜列表 """ # 获取 model_router model_router = await get_model_router() # 加载 Prompt system_prompt = load_system_prompt(category, subcategory) if not system_prompt: raise ValueError(f"未找到提示词: category={category}, subcategory={subcategory}") # 用户提示词 user_prompt = load_script_user_prompt( topic=f"{category}/{subcategory}", duration=duration, ) # 调用 AI 生成 result = await model_router.generate( prompt=user_prompt, system_prompt=system_prompt, model_id=model, task_type="script", temperature=0.7, response_format="json_object", ) if not result.content or not result.content.strip(): raise ValueError("AI 返回内容为空，请检查模型配置或重试") success, parsed_data, error_msg = safe_parse_ai_json_response(result.content) if not success: raise ValueError(error_msg or "AI 返回格式错误，无法解析为 JSON") try: shots_data = validate_and_normalize_shots(parsed_data) if not shots_data: raise ValueError("AI 返回的分镜数据为空或格式不正确") shots = [ScriptShot(**shot) for shot in shots_data] return shots except Exception as e: raise ValueError(f"分镜数据处理失败: {str(e)}") async def generate_script_stream( self, category: str, subcategory: str, duration: int, script_type: str, model: str | None = None, ) -> AsyncIterator[ScriptGenerationEvent]: """流式生成脚本（SSE）""" model_router = await get_model_router() try: async with asyncio.timeout(60): # 加载 Prompt system_prompt = load_system_prompt(category, subcategory) if not system_prompt: yield ScriptGenerationEvent( type="error", message=f"未找到提示词: category={category}, subcategory={subcategory}", ) return user_prompt = load_script_user_prompt( topic=f"{category}/{subcategory}", duration=duration, ) yield ScriptGenerationEvent( type="start", message="准备生成...", ) full_content = "" has_shown_generating = False async for chunk in model_router.generate_stream_with_progress( prompt=user_prompt, system_prompt=system_prompt, model_id=model, task_type="script", temperature=0.7, response_format="json_object", ): if chunk["type"] == "chunk": chunk_content = chunk.get("content", "") if not chunk_content: continue full_content += chunk_content if not has_shown_generating: yield ScriptGenerationEvent( type="generating", message="正在创作脚本...", ) has_shown_generating = True if not full_content or not full_content.strip(): yield ScriptGenerationEvent( type="error", message="AI 返回内容为空，请检查模型配置或重试", ) return success, parsed_data, error_msg = await asyncio.to_thread( safe_parse_ai_json_response, full_content ) if not success: yield ScriptGenerationEvent( type="error", message=f"脚本解析失败: {error_msg or '无法解析 AI 返回的内容'}", ) return try: shots_data = await asyncio.to_thread( validate_and_normalize_shots, parsed_data ) if not shots_data: yield ScriptGenerationEvent( type="error", message="AI 返回的分镜数据为空或格式不正确", ) return shots = [ScriptShot(**shot) for shot in shots_data] yield ScriptGenerationEvent( type="complete", message="脚本生成成功", result=shots, ) except Exception as e: yield ScriptGenerationEvent( type="error", message=f"分镜数据处理失败: {str(e)}", ) except asyncio.TimeoutError: yield ScriptGenerationEvent( type="error", message="脚本生成超时，请重试", ) except Exception as e: yield ScriptGenerationEvent( type="error", message=f"生成失败: {str(e)}", ) async def polish_content( self, content: str, polish_type: str = "voiceover", shot_type: str = "segment", ) -> str: """ 润色内容 Args: content: 待润色内容 polish_type: 润色类型，可选 "scene"（画面描述）或 "voiceover"（配音文案） shot_type: 镜头类型，可选 "segment"（分镜）或 "empty_shot"（空镜），仅用于画面润色 Returns: 润色后的内容 """ # 获取 model_router model_router = await get_model_router() # 从文件加载提示词模板 if polish_type == "scene": # 画面润色需要根据镜头类型选择不同提示词 if shot_type == "empty_shot": prompt_template = self._load_prompt("polish/scene_empty_shot") else: prompt_template = self._load_prompt("polish/scene_segment") # 如果特定类型的提示词不存在，回退到通用 scene 提示词 if not prompt_template: prompt_template = self._load_prompt("polish/scene") else: # 配音文案润色 prompt_template = self._load_prompt("polish/voiceover") if not prompt_template: # 最终回退 prompt_template = "请润色以下内容：\n\n{content}" prompt = prompt_template.format(content=content) result = await model_router.generate( prompt=prompt, task_type="polish", temperature=0.5, max_tokens=300, ) return result.content.strip() async def check_model_health(self) -> dict: """检查模型健康状态""" model_router = await get_model_router() health_results = await model_router.health_check() models = [] available_count = 0 recommended = None for provider_id, health in health_results.items(): model_info = { "id": health.id, "name": health.name, "is_available": health.is_available, "response_time": health.response_time, "last_error": health.last_error, } models.append(model_info) if health.is_available: available_count += 1 if recommended is None or health.response_time < recommended.get( "response_time", float("inf") ): recommended = model_info total = len(models) return { "status": "healthy" if available_count > 0 else "unhealthy", "models": models, "recommended_model": recommended, "total_models": total, "available_models": available_count, } async def test_model(self, model_id: str | None = None) -> dict: """测试指定模型连接""" model_router = await get_model_router() import time start_time = time.time() try: result = await model_router.generate( prompt="你好", model_id=model_id, max_tokens=5, ) response_time = (time.time() - start_time) * 1000 return { "success": True, "model": result.model, "response_time": round(response_time, 2), "checked_at": time.strftime("%Y-%m-%dT%H:%M:%S"), } except Exception as e: return { "success": False, "model": model_id or "default", "error": str(e), "checked_at": time.strftime("%Y-%m-%dT%H:%M:%S"), } # 全局单例 _script_service: ScriptService | None = None def get_script_service() -> ScriptService: """获取 ScriptService 单例""" global _script_service if _script_service is None: _script_service = ScriptService() return _script_service