mirror of
https://github.com/linyqh/NarratoAI.git
synced 2025-12-31 10:18:12 +00:00
117 lines
4.6 KiB
Python
117 lines
4.6 KiB
Python
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.upload_validation import ensure_existing_file, InputValidationError
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def generate_script_short(tr, params, custom_clips=5):
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"""
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生成短视频脚本
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Args:
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tr: 翻译函数
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params: 视频参数对象
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custom_clips: 自定义片段数量，默认为5
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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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# ========== 严格验证：必须上传视频和字幕（与短剧解说保持一致）==========
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# 1. 验证视频文件
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video_path = getattr(params, "video_origin_path", None)
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if not video_path or not str(video_path).strip():
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st.error("请先选择视频文件")
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st.stop()
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try:
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ensure_existing_file(
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str(video_path),
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label="视频",
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allowed_exts=(".mp4", ".mov", ".avi", ".flv", ".mkv"),
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)
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except InputValidationError as e:
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st.error(str(e))
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st.stop()
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# 2. 验证字幕文件（移除推断逻辑，必须上传）
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subtitle_path = st.session_state.get("subtitle_path")
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if not subtitle_path or not str(subtitle_path).strip():
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st.error("请先上传字幕文件")
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st.stop()
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try:
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subtitle_path = ensure_existing_file(
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str(subtitle_path),
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label="字幕",
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allowed_exts=(".srt",),
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)
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except InputValidationError as e:
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st.error(str(e))
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st.stop()
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logger.info(f"使用用户上传的字幕文件: {subtitle_path}")
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# ========== 获取 LLM 配置 ==========
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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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vision_llm_provider = st.session_state.get('vision_llm_providers') or config.app.get('vision_llm_provider', 'gemini')
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vision_llm_provider = vision_llm_provider.lower()
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vision_api_key = st.session_state.get(f'vision_{vision_llm_provider}_api_key') or config.app.get(f'vision_{vision_llm_provider}_api_key', "")
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vision_model = st.session_state.get(f'vision_{vision_llm_provider}_model_name') or config.app.get(f'vision_{vision_llm_provider}_model_name', "")
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vision_base_url = st.session_state.get(f'vision_{vision_llm_provider}_base_url') or config.app.get(f'vision_{vision_llm_provider}_base_url', "")
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update_progress(20, "开始准备生成脚本")
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# ========== 调用后端生成脚本 ==========
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from app.services.SDP.generate_script_short import generate_script_result
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result = generate_script_result(
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api_key=text_api_key,
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model_name=text_model,
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output_path="resource/scripts/merged_subtitle.json",
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base_url=text_base_url,
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custom_clips=custom_clips,
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provider=text_provider,
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subtitle_file_path=subtitle_path,
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)
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if result.get("status") != "success":
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st.error(result.get("message", "生成脚本失败，请检查日志"))
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st.stop()
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script = result.get("script")
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logger.info(f"脚本生成完成 {json.dumps(script, ensure_ascii=False, indent=4)}")
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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(80, "脚本生成完成")
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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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progress_bar.progress(100)
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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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