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feat(llm): 增强解说文案生成和图片分析功能，优化JSON解析
在migration_adapter.py和generate_script_docu.py文件中，集成了增强的JSON解析器以提高解说文案生成的稳定性和兼容性。更新了生成解说文案的提示词管理系统，确保返回的JSON格式有效，并在图片分析中保持向后兼容性，提升了系统的灵活性和用户体验。
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linyq
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@ -5,6 +5,7 @@
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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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@ -94,47 +95,26 @@ class LegacyLLMAdapter:
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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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# 构建提示词
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prompt = f"""
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我是一名荒野建造解说的博主，以下是一些同行的对标文案，请你深度学习并总结这些文案的风格特点跟内容特点：
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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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<video_frame_description>
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{markdown_content}
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</video_frame_description>
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请根据以上视频帧描述，生成引人入胜的解说文案。
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<output>
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{{
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"items": [
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{{
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"_id": 1,
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"timestamp": "00:00:05,390-00:00:10,430",
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"picture": "画面描述",
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"narration": "解说文案",
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}}
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]
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}}
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</output>
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<restriction>
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1. 只输出 json 内容，不要输出其他任何说明性的文字
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2. 解说文案的语言使用 简体中文
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3. 严禁虚构画面，所有画面只能从 <video_frame_description> 中摘取
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</restriction>
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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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@ -143,12 +123,41 @@ class LegacyLLMAdapter:
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temperature=1.5,
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response_format="json"
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)
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return result
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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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return 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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@ -163,17 +172,17 @@ class VisionAnalyzerAdapter:
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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[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
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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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@ -183,9 +192,26 @@ class VisionAnalyzerAdapter:
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provider=self.provider,
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batch_size=batch_size
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)
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return results
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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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@ -368,7 +368,16 @@ def generate_script_docu(params):
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base_url=text_base_url,
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model=text_model
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)
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narration_dict = json.loads(narration)['items']
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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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narration_data = parse_and_fix_json(narration)
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if not narration_data or 'items' not in narration_data:
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logger.error(f"解说文案JSON解析失败，原始内容: {narration[:200]}...")
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raise Exception("解说文案格式错误，无法解析JSON或缺少items字段")
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narration_dict = narration_data['items']
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# 为 narration_dict 中每个 item 新增一个 OST: 2 的字段, 代表保留原声和配音
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narration_dict = [{**item, "OST": 2} for item in narration_dict]
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logger.debug(f"解说文案创作完成:\n{"\n".join([item['narration'] for item in narration_dict])}")
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