mirror of
https://github.com/linyqh/NarratoAI.git
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c0e0b8ec6d
- 在 generate_short_summary.py 中添加 temperature 参数，用于控制生成文案的随机性- 修改 prompt.py 中的段落数要求，改为与字幕长度成正比 - 在 short_drama_explanation.py 中添加日志输出，记录使用的模型和温度
128 lines
4.5 KiB
Python
128 lines
4.5 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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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
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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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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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)
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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
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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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)
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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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narration_dict = json.loads(narration_script)
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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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