mirror of
https://github.com/linyqh/NarratoAI.git
synced 2025-12-10 18:02:51 +00:00
338 lines
11 KiB
Python
338 lines
11 KiB
Python
"""
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迁移适配器
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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
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"""
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import asyncio
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import json
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from typing import List, Dict, Any, Optional, Union
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from pathlib import Path
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import PIL.Image
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from loguru import logger
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from .unified_service import UnifiedLLMService
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from .exceptions import LLMServiceError
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# 导入新的提示词管理系统
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from app.services.prompts import PromptManager
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# 提供商注册由 webui.py:main() 显式调用（见 LLM 提供商注册机制重构）
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# 这样更可靠，错误也更容易调试
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def _run_async_safely(coro_func, *args, **kwargs):
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"""
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安全地运行异步协程，处理各种事件循环情况
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Args:
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coro_func: 协程函数（不是协程对象）
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*args: 协程函数的位置参数
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**kwargs: 协程函数的关键字参数
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Returns:
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协程的执行结果
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"""
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def run_in_new_loop():
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"""在新的事件循环中运行协程"""
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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try:
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return loop.run_until_complete(coro_func(*args, **kwargs))
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finally:
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loop.close()
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asyncio.set_event_loop(None)
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try:
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# 尝试获取当前事件循环
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try:
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loop = asyncio.get_running_loop()
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# 如果有运行中的事件循环，使用线程池执行
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import concurrent.futures
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with concurrent.futures.ThreadPoolExecutor() as executor:
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future = executor.submit(run_in_new_loop)
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return future.result()
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except RuntimeError:
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# 没有运行中的事件循环，直接运行
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return run_in_new_loop()
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except Exception as e:
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logger.error(f"异步执行失败: {str(e)}")
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raise LLMServiceError(f"异步执行失败: {str(e)}")
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class LegacyLLMAdapter:
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"""传统LLM接口适配器"""
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@staticmethod
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def create_vision_analyzer(provider: str, api_key: str, model: str, base_url: str = None):
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"""
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创建视觉分析器实例 - 兼容原有接口
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Args:
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provider: 提供商名称
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api_key: API密钥
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model: 模型名称
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base_url: API基础URL
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Returns:
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适配器实例
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"""
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return VisionAnalyzerAdapter(provider, api_key, model, base_url)
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@staticmethod
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def generate_narration(markdown_content: str, api_key: str, base_url: str, model: str) -> str:
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"""
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生成解说文案 - 兼容原有接口
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Args:
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markdown_content: Markdown格式的视频帧分析内容
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api_key: API密钥
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base_url: API基础URL
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model: 模型名称
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Returns:
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生成的解说文案JSON字符串
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"""
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try:
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# 使用新的提示词管理系统
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prompt = PromptManager.get_prompt(
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category="documentary",
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name="narration_generation",
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parameters={
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"video_frame_description": markdown_content
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}
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)
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# 使用统一服务生成文案
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result = _run_async_safely(
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UnifiedLLMService.generate_text,
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prompt=prompt,
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system_prompt="你是一名专业的短视频解说文案撰写专家。",
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temperature=1.5,
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response_format="json"
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)
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# 使用增强的JSON解析器
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from webui.tools.generate_short_summary import parse_and_fix_json
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parsed_result = parse_and_fix_json(result)
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if not parsed_result:
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logger.error("无法解析LLM返回的JSON数据")
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# 返回一个基本的JSON结构而不是错误字符串
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return json.dumps({
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"items": [
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{
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"_id": 1,
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"timestamp": "00:00:00-00:00:10",
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"picture": "解析失败，请检查LLM输出",
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"narration": "解说文案生成失败，请重试"
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}
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]
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}, ensure_ascii=False)
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# 确保返回的是JSON字符串
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return json.dumps(parsed_result, ensure_ascii=False)
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except Exception as e:
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logger.error(f"生成解说文案失败: {str(e)}")
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# 返回一个基本的JSON结构而不是错误字符串
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return json.dumps({
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"items": [
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{
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"_id": 1,
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"timestamp": "00:00:00-00:00:10",
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"picture": "生成失败",
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"narration": f"解说文案生成失败: {str(e)}"
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}
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]
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}, ensure_ascii=False)
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class VisionAnalyzerAdapter:
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"""视觉分析器适配器"""
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def __init__(self, provider: str, api_key: str, model: str, base_url: str = None):
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self.provider = provider
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self.api_key = api_key
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self.model = model
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self.base_url = base_url
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async def analyze_images(self,
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images: List[Union[str, Path, PIL.Image.Image]],
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prompt: str,
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batch_size: int = 10) -> List[Dict[str, Any]]:
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"""
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分析图片 - 兼容原有接口
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Args:
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images: 图片列表
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prompt: 分析提示词
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batch_size: 批处理大小
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Returns:
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分析结果列表，格式与旧实现兼容
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"""
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try:
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# 使用统一服务分析图片
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results = await UnifiedLLMService.analyze_images(
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images=images,
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prompt=prompt,
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provider=self.provider,
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batch_size=batch_size
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)
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# 转换为旧格式以保持向后兼容性
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# 新实现返回 List[str]，需要转换为 List[Dict]
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compatible_results = []
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for i, result in enumerate(results):
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# 计算这个批次处理的图片数量
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start_idx = i * batch_size
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end_idx = min(start_idx + batch_size, len(images))
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images_processed = end_idx - start_idx
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compatible_results.append({
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'batch_index': i,
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'images_processed': images_processed,
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'response': result,
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'model_used': self.model
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})
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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")
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return compatible_results
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except Exception as e:
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logger.error(f"图片分析失败: {str(e)}")
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raise
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class SubtitleAnalyzerAdapter:
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"""字幕分析器适配器"""
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def __init__(self, api_key: str, model: str, base_url: str, provider: str = None):
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self.api_key = api_key
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self.model = model
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self.base_url = base_url
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self.provider = provider or "openai"
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def _run_async_safely(self, coro_func, *args, **kwargs):
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"""安全地运行异步协程"""
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return _run_async_safely(coro_func, *args, **kwargs)
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def _clean_json_output(self, output: str) -> str:
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"""清理JSON输出，移除markdown标记等"""
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import re
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# 移除可能的markdown代码块标记
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output = re.sub(r'^```json\s*', '', output, flags=re.MULTILINE)
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output = re.sub(r'^```\s*$', '', output, flags=re.MULTILINE)
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output = re.sub(r'^```.*$', '', output, flags=re.MULTILINE)
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# 移除开头和结尾的```标记
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output = re.sub(r'^```', '', output)
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output = re.sub(r'```$', '', output)
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# 移除前后空白字符
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output = output.strip()
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return output
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def analyze_subtitle(self, subtitle_content: str) -> Dict[str, Any]:
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"""
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分析字幕内容 - 兼容原有接口
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Args:
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subtitle_content: 字幕内容
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Returns:
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分析结果字典
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"""
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try:
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# 使用统一服务分析字幕
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result = self._run_async_safely(
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UnifiedLLMService.analyze_subtitle,
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subtitle_content=subtitle_content,
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provider=self.provider,
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temperature=1.0
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)
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return {
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"status": "success",
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"analysis": result,
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"model": self.model,
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"temperature": 1.0
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}
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except Exception as e:
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logger.error(f"字幕分析失败: {str(e)}")
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return {
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"status": "error",
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"message": str(e),
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"temperature": 1.0
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}
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def generate_narration_script(self, short_name: str, plot_analysis: str, subtitle_content: str = "", temperature: float = 0.7) -> Dict[str, Any]:
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"""
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生成解说文案 - 兼容原有接口
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Args:
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short_name: 短剧名称
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plot_analysis: 剧情分析内容
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subtitle_content: 原始字幕内容，用于提供准确的时间戳信息
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temperature: 生成温度
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Returns:
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生成结果字典
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"""
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try:
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# 使用新的提示词管理系统构建提示词
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prompt = PromptManager.get_prompt(
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category="short_drama_narration",
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name="script_generation",
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parameters={
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"drama_name": short_name,
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"plot_analysis": plot_analysis,
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"subtitle_content": subtitle_content
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}
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)
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# 使用统一服务生成文案
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result = self._run_async_safely(
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UnifiedLLMService.generate_text,
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prompt=prompt,
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system_prompt="你是一位专业的短视频解说脚本撰写专家。",
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provider=self.provider,
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temperature=temperature,
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response_format="json"
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)
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# 清理JSON输出
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cleaned_result = self._clean_json_output(result)
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# 新的提示词系统返回的是包含items数组的JSON格式
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# 为了保持向后兼容，我们需要直接返回这个JSON字符串
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# 调用方会期望这是一个包含items数组的JSON字符串
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return {
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"status": "success",
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"narration_script": cleaned_result,
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"model": self.model,
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"temperature": temperature
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}
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except Exception as e:
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logger.error(f"解说文案生成失败: {str(e)}")
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return {
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"status": "error",
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"message": str(e),
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"temperature": temperature
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}
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# 为了向后兼容，提供一些全局函数
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def create_vision_analyzer(provider: str, api_key: str, model: str, base_url: str = None):
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"""创建视觉分析器 - 全局函数"""
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return LegacyLLMAdapter.create_vision_analyzer(provider, api_key, model, base_url)
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def generate_narration(markdown_content: str, api_key: str, base_url: str, model: str) -> str:
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"""生成解说文案 - 全局函数"""
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return LegacyLLMAdapter.generate_narration(markdown_content, api_key, base_url, model)
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