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https://github.com/linyqh/NarratoAI.git
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7309208282
更新generate_narration_script.py、base.py和generate_short_summary.py文件，重构解说文案生成和视觉分析器的实现，优先使用新的LLM服务架构。添加回退机制以确保兼容性，增强系统的稳定性和用户体验。
255 lines
9.3 KiB
Python
255 lines
9.3 KiB
Python
#!/usr/bin/env python
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# -*- coding: UTF-8 -*-
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'''
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@Project: NarratoAI
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@File : 短剧解说脚本生成
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@Author : 小林同学
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@Date : 2025/5/10 下午10:26
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'''
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import os
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import json
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import time
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import traceback
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import streamlit as st
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from loguru import logger
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from app.config import config
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from app.services.SDE.short_drama_explanation import analyze_subtitle, generate_narration_script
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# 导入新的LLM服务模块 - 确保提供商被注册
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import app.services.llm # 这会触发提供商注册
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from app.services.llm.migration_adapter import SubtitleAnalyzerAdapter
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import re
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def parse_and_fix_json(json_string):
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"""
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解析并修复JSON字符串
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Args:
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json_string: 待解析的JSON字符串
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Returns:
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dict: 解析后的字典，如果解析失败返回None
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"""
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if not json_string or not json_string.strip():
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logger.error("JSON字符串为空")
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return None
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# 清理字符串
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json_string = json_string.strip()
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# 尝试直接解析
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try:
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return json.loads(json_string)
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except json.JSONDecodeError as e:
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logger.warning(f"直接JSON解析失败: {e}")
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# 尝试提取JSON部分
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try:
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# 查找JSON代码块
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json_match = re.search(r'```json\s*(.*?)\s*```', json_string, re.DOTALL)
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if json_match:
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json_content = json_match.group(1).strip()
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logger.info("从代码块中提取JSON内容")
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return json.loads(json_content)
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except json.JSONDecodeError:
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pass
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# 尝试查找大括号包围的内容
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try:
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# 查找第一个 { 到最后一个 } 的内容
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start_idx = json_string.find('{')
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end_idx = json_string.rfind('}')
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if start_idx != -1 and end_idx != -1 and end_idx > start_idx:
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json_content = json_string[start_idx:end_idx+1]
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logger.info("提取大括号包围的JSON内容")
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return json.loads(json_content)
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except json.JSONDecodeError:
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pass
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# 尝试修复常见的JSON格式问题
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try:
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# 移除注释
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json_string = re.sub(r'#.*', '', json_string)
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# 移除多余的逗号
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json_string = re.sub(r',\s*}', '}', json_string)
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json_string = re.sub(r',\s*]', ']', json_string)
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# 修复单引号
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json_string = re.sub(r"'([^']*)':", r'"\1":', json_string)
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logger.info("尝试修复JSON格式问题后解析")
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return json.loads(json_string)
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except json.JSONDecodeError:
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pass
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# 如果所有方法都失败，尝试创建一个基本的结构
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logger.error(f"所有JSON解析方法都失败，原始内容: {json_string[:200]}...")
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# 尝试从文本中提取关键信息创建基本结构
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try:
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# 这是一个简单的回退方案
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return {
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"items": [
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{
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"_id": 1,
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"timestamp": "00:00:00,000-00:00:10,000",
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"picture": "解析失败，使用默认内容",
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"narration": json_string[:100] + "..." if len(json_string) > 100 else json_string,
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"OST": 0
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}
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]
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}
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except Exception:
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return None
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def generate_script_short_sunmmary(params, subtitle_path, video_theme, temperature):
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"""
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生成 短剧解说 视频脚本
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要求: 提供高质量短剧字幕
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适合场景: 短剧
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"""
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progress_bar = st.progress(0)
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status_text = st.empty()
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def update_progress(progress: float, message: str = ""):
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progress_bar.progress(progress)
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if message:
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status_text.text(f"{progress}% - {message}")
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else:
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status_text.text(f"进度: {progress}%")
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try:
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with st.spinner("正在生成脚本..."):
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if not params.video_origin_path:
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st.error("请先选择视频文件")
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return
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"""
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1. 获取字幕
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"""
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update_progress(30, "正在解析字幕...")
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# 判断字幕文件是否存在
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if not os.path.exists(subtitle_path):
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st.error("字幕文件不存在")
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return
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"""
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2. 分析字幕总结剧情 - 使用新的LLM服务架构
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"""
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text_provider = config.app.get('text_llm_provider', 'gemini').lower()
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text_api_key = config.app.get(f'text_{text_provider}_api_key')
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text_model = config.app.get(f'text_{text_provider}_model_name')
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text_base_url = config.app.get(f'text_{text_provider}_base_url')
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try:
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# 优先使用新的LLM服务架构
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logger.info("使用新的LLM服务架构进行字幕分析")
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analyzer = SubtitleAnalyzerAdapter(text_api_key, text_model, text_base_url, text_provider)
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# 读取字幕文件
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with open(subtitle_path, 'r', encoding='utf-8') as f:
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subtitle_content = f.read()
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analysis_result = analyzer.analyze_subtitle(subtitle_content)
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except Exception as e:
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logger.warning(f"使用新LLM服务失败，回退到旧实现: {str(e)}")
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# 回退到旧的实现
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analysis_result = analyze_subtitle(
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subtitle_file_path=subtitle_path,
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api_key=text_api_key,
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model=text_model,
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base_url=text_base_url,
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save_result=True,
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temperature=temperature,
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provider=text_provider
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)
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"""
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3. 根据剧情生成解说文案
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"""
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if analysis_result["status"] == "success":
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logger.info("字幕分析成功！")
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update_progress(60, "正在生成文案...")
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# 根据剧情生成解说文案 - 使用新的LLM服务架构
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try:
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# 优先使用新的LLM服务架构
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logger.info("使用新的LLM服务架构生成解说文案")
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narration_result = analyzer.generate_narration_script(
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short_name=video_theme,
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plot_analysis=analysis_result["analysis"],
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temperature=temperature
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)
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except Exception as e:
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logger.warning(f"使用新LLM服务失败，回退到旧实现: {str(e)}")
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# 回退到旧的实现
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narration_result = generate_narration_script(
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short_name=video_theme,
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plot_analysis=analysis_result["analysis"],
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api_key=text_api_key,
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model=text_model,
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base_url=text_base_url,
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save_result=True,
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temperature=temperature,
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provider=text_provider
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)
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if narration_result["status"] == "success":
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logger.info("\n解说文案生成成功！")
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logger.info(narration_result["narration_script"])
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else:
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logger.info(f"\n解说文案生成失败: {narration_result['message']}")
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st.error("生成脚本失败，请检查日志")
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st.stop()
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else:
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logger.error(f"分析失败: {analysis_result['message']}")
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st.error("生成脚本失败，请检查日志")
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st.stop()
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"""
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4. 生成文案
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"""
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logger.info("开始准备生成解说文案")
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# 结果转换为JSON字符串
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narration_script = narration_result["narration_script"]
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# 增强JSON解析，包含错误处理和修复
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narration_dict = parse_and_fix_json(narration_script)
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if narration_dict is None:
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st.error("生成的解说文案格式错误，无法解析为JSON")
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logger.error(f"JSON解析失败，原始内容: {narration_script}")
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st.stop()
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# 验证JSON结构
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if 'items' not in narration_dict:
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st.error("生成的解说文案缺少必要的'items'字段")
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logger.error(f"JSON结构错误，缺少items字段: {narration_dict}")
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st.stop()
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script = json.dumps(narration_dict['items'], ensure_ascii=False, indent=2)
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if script is None:
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st.error("生成脚本失败，请检查日志")
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st.stop()
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logger.success(f"剪辑脚本生成完成")
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if isinstance(script, list):
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st.session_state['video_clip_json'] = script
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elif isinstance(script, str):
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st.session_state['video_clip_json'] = json.loads(script)
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update_progress(90, "整理输出...")
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time.sleep(0.1)
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progress_bar.progress(100)
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status_text.text("脚本生成完成！")
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st.success("视频脚本生成成功！")
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except Exception as err:
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st.error(f"生成过程中发生错误: {str(err)}")
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logger.exception(f"生成脚本时发生错误\n{traceback.format_exc()}")
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finally:
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time.sleep(2)
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progress_bar.empty()
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status_text.empty()
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