小鱼开发
7640dc23ba
- script_service: start文案"准备生成...", generating文案"正在创作脚本..." - ScriptCreation: 添加 requestLock ref，防止网络延迟期间快速点击发起多个请求 - 锁在请求开始时设置，完成后释放，与 generating 状态互补
362 lines
11 KiB
Python
362 lines
11 KiB
Python
"""
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脚本生成服务
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============
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"""
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import asyncio
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import logging
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import re
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import time
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from collections.abc import AsyncIterator
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from pathlib import Path
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from app.ai.model_router import get_model_router
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from app.ai.prompts import load_script_user_prompt, load_system_prompt
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from app.schemas.script import ScriptGenerationEvent, ScriptShot
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from app.services.ai_response_utils import (
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safe_parse_ai_json_response,
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validate_and_normalize_shots,
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)
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logger = logging.getLogger(__name__)
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class ScriptService:
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"""脚本生成服务"""
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def __init__(self):
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self.prompts_dir = Path(__file__).parent.parent / "ai" / "prompts"
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def _load_prompt(self, name: str) -> str:
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"""加载 Prompt 模板"""
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prompt_file = self.prompts_dir / f"{name}.txt"
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if prompt_file.exists():
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return prompt_file.read_text(encoding="utf-8")
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return ""
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@staticmethod
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def _extract_json(content: str) -> str:
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"""
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从 Markdown 代码块中提取 JSON，或返回原始内容
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支持格式：
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- ```json {...} ```
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- ``` {...} ```
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- 纯 JSON 文本
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"""
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if not content:
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return ""
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content = content.strip()
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# 匹配 ```json ... ``` 或 ``` ... ```
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pattern = r"```(?:json)?\s*([\s\S]*?)\s*```"
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matches = re.findall(pattern, content)
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if matches:
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# 取最后一个匹配（避免前面有示例代码）
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return matches[-1].strip()
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# 如果没有代码块，返回原始内容
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return content
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async def generate_script(
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self,
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category: str,
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subcategory: str,
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duration: int,
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script_type: str,
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model: str | None = None,
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) -> list[ScriptShot]:
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"""
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同步生成脚本
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Args:
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category: 大类代码，如 "bk"
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subcategory: 小类代码，如 "ht"
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duration: 视频时长（秒）
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script_type: 脚本类型
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model: 指定模型
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Returns:
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分镜列表
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"""
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# 获取 model_router
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model_router = await get_model_router()
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# 加载 Prompt
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system_prompt = load_system_prompt(category, subcategory)
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if not system_prompt:
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raise ValueError(f"未找到提示词: category={category}, subcategory={subcategory}")
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# 用户提示词
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user_prompt = load_script_user_prompt(
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topic=f"{category}/{subcategory}",
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duration=duration,
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)
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# 调用 AI 生成
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result = await model_router.generate(
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prompt=user_prompt,
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system_prompt=system_prompt,
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model_id=model,
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task_type="script",
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temperature=0.7,
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response_format="json_object",
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)
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if not result.content or not result.content.strip():
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raise ValueError("AI 返回内容为空，请检查模型配置或重试")
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success, parsed_data, error_msg = safe_parse_ai_json_response(result.content)
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if not success:
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raise ValueError(error_msg or "AI 返回格式错误，无法解析为 JSON")
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try:
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shots_data = validate_and_normalize_shots(parsed_data)
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if not shots_data:
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raise ValueError("AI 返回的分镜数据为空或格式不正确")
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shots = [ScriptShot(**shot) for shot in shots_data]
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return shots
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except Exception as e:
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raise ValueError(f"分镜数据处理失败: {str(e)}")
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async def generate_script_stream(
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self,
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category: str,
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subcategory: str,
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duration: int,
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script_type: str,
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model: str | None = None,
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) -> AsyncIterator[ScriptGenerationEvent]:
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"""流式生成脚本（SSE）"""
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model_router = await get_model_router()
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try:
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# 加载 Prompt
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system_prompt = load_system_prompt(category, subcategory)
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if not system_prompt:
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yield ScriptGenerationEvent(
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type="error",
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message=f"未找到提示词: category={category}, subcategory={subcategory}",
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)
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return
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user_prompt = load_script_user_prompt(
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topic=f"{category}/{subcategory}",
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duration=duration,
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)
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yield ScriptGenerationEvent(
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type="start",
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message="准备生成...",
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)
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full_content = ""
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has_shown_generating = False
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async for chunk in model_router.generate_stream_with_progress(
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prompt=user_prompt,
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system_prompt=system_prompt,
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model_id=model,
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task_type="script",
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temperature=0.7,
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response_format="json_object",
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):
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if chunk["type"] == "chunk":
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chunk_content = chunk.get("content", "")
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if not chunk_content:
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continue
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full_content += chunk_content
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if not has_shown_generating:
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yield ScriptGenerationEvent(
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type="generating",
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message="正在创作脚本...",
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)
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has_shown_generating = True
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if not full_content or not full_content.strip():
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yield ScriptGenerationEvent(
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type="error",
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message="AI 返回内容为空，请检查模型配置或重试",
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)
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return
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success, parsed_data, error_msg = safe_parse_ai_json_response(full_content)
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if not success:
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yield ScriptGenerationEvent(
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type="error",
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message=f"脚本解析失败: {error_msg or '无法解析 AI 返回的内容'}",
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)
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return
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try:
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shots_data = validate_and_normalize_shots(parsed_data)
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if not shots_data:
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yield ScriptGenerationEvent(
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type="error",
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message="AI 返回的分镜数据为空或格式不正确",
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)
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return
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shots = [ScriptShot(**shot) for shot in shots_data]
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yield ScriptGenerationEvent(
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type="complete",
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message="脚本生成成功",
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result=shots,
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)
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except Exception as e:
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yield ScriptGenerationEvent(
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type="error",
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message=f"分镜数据处理失败: {str(e)}",
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)
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except Exception as e:
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yield ScriptGenerationEvent(
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type="error",
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message=f"生成失败: {str(e)}",
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)
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async def polish_content(
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self,
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content: str,
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polish_type: str = "voiceover",
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shot_type: str = "segment",
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) -> str:
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"""
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润色内容
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Args:
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content: 待润色内容
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polish_type: 润色类型，可选 "scene"（画面描述）或 "voiceover"（配音文案）
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shot_type: 镜头类型，可选 "segment"（分镜）或 "empty_shot"（空镜），仅用于画面润色
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Returns:
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润色后的内容
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"""
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# 获取 model_router
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model_router = await get_model_router()
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# 从文件加载提示词模板
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if polish_type == "scene":
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# 画面润色需要根据镜头类型选择不同提示词
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if shot_type == "empty_shot":
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prompt_template = self._load_prompt("polish/scene_empty_shot")
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else:
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prompt_template = self._load_prompt("polish/scene_segment")
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# 如果特定类型的提示词不存在，回退到通用 scene 提示词
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if not prompt_template:
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prompt_template = self._load_prompt("polish/scene")
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else:
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# 配音文案润色
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prompt_template = self._load_prompt("polish/voiceover")
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if not prompt_template:
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# 最终回退
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prompt_template = "请润色以下内容：\n\n{content}"
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prompt = prompt_template.format(content=content)
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result = await model_router.generate(
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prompt=prompt,
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task_type="polish",
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temperature=0.5,
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max_tokens=300,
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)
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return result.content.strip()
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async def check_model_health(self) -> dict:
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"""检查模型健康状态"""
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model_router = await get_model_router()
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health_results = await model_router.health_check()
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models = []
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available_count = 0
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recommended = None
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for provider_id, health in health_results.items():
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model_info = {
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"id": health.id,
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"name": health.name,
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"is_available": health.is_available,
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"response_time": health.response_time,
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"last_error": health.last_error,
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}
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models.append(model_info)
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if health.is_available:
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available_count += 1
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if recommended is None or health.response_time < recommended.get(
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"response_time", float("inf")
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):
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recommended = model_info
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total = len(models)
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return {
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"status": "healthy" if available_count > 0 else "unhealthy",
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"models": models,
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"recommended_model": recommended,
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"total_models": total,
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"available_models": available_count,
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}
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async def test_model(self, model_id: str | None = None) -> dict:
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"""测试指定模型连接"""
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model_router = await get_model_router()
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import time
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start_time = time.time()
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try:
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result = await model_router.generate(
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prompt="你好",
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model_id=model_id,
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max_tokens=5,
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)
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response_time = (time.time() - start_time) * 1000
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return {
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"success": True,
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"model": result.model,
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"response_time": round(response_time, 2),
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"checked_at": time.strftime("%Y-%m-%dT%H:%M:%S"),
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}
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except Exception as e:
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return {
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"success": False,
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"model": model_id or "default",
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"error": str(e),
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"checked_at": time.strftime("%Y-%m-%dT%H:%M:%S"),
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}
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# 全局单例
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_script_service: ScriptService | None = None
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def get_script_service() -> ScriptService:
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"""获取 ScriptService 单例"""
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global _script_service
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if _script_service is None:
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_script_service = ScriptService()
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return _script_service
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