mirror of
https://github.com/linyqh/NarratoAI.git
synced 2025-12-10 18:02:51 +00:00
292 lines
10 KiB
Python
292 lines
10 KiB
Python
import streamlit as st
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import os
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import sys
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from loguru import logger
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from app.config import config
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from webui.components import basic_settings, video_settings, audio_settings, subtitle_settings, script_settings, \
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system_settings
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# from webui.utils import cache, file_utils
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from app.utils import utils
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from app.utils import ffmpeg_utils
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from app.models.schema import VideoClipParams, VideoAspect
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# 初始化配置 - 必须是第一个 Streamlit 命令
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st.set_page_config(
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page_title="NarratoAI",
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page_icon="📽️",
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layout="wide",
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initial_sidebar_state="auto",
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menu_items={
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"Report a bug": "https://github.com/linyqh/NarratoAI/issues",
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'About': f"# Narrato:blue[AI] :sunglasses: 📽️ \n #### Version: v{config.project_version} \n "
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f"自动化影视解说视频详情请移步：https://github.com/linyqh/NarratoAI"
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},
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)
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# 设置页面样式
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hide_streamlit_style = """
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<style>#root > div:nth-child(1) > div > div > div > div > section > div {padding-top: 2rem; padding-bottom: 10px; padding-left: 20px; padding-right: 20px;}</style>
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"""
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st.markdown(hide_streamlit_style, unsafe_allow_html=True)
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def init_log():
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"""初始化日志配置"""
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from loguru import logger
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logger.remove()
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_lvl = "INFO" # 改为 INFO 级别，过滤掉 DEBUG 日志
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def format_record(record):
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# 简化日志格式化处理，不尝试按特定字符串过滤torch相关内容
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file_path = record["file"].path
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relative_path = os.path.relpath(file_path, config.root_dir)
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record["file"].path = f"./{relative_path}"
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record['message'] = record['message'].replace(config.root_dir, ".")
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_format = '<green>{time:%Y-%m-%d %H:%M:%S}</> | ' + \
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'<level>{level}</> | ' + \
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'"{file.path}:{line}":<blue> {function}</> ' + \
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'- <level>{message}</>' + "\n"
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return _format
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# 添加日志过滤器
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def log_filter(record):
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"""过滤不必要的日志消息"""
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# 过滤掉启动时的噪音日志（即使在 DEBUG 模式下也可以选择过滤）
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ignore_patterns = [
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"Examining the path of torch.classes raised",
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"torch.cuda.is_available()",
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"CUDA initialization"
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]
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return not any(pattern in record["message"] for pattern in ignore_patterns)
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logger.add(
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sys.stdout,
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level=_lvl,
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format=format_record,
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colorize=True,
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filter=log_filter
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)
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# 应用启动后，可以再添加更复杂的过滤器
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def setup_advanced_filters():
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"""在应用完全启动后设置高级过滤器"""
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try:
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for handler_id in logger._core.handlers:
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logger.remove(handler_id)
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# 重新添加带有高级过滤的处理器
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def advanced_filter(record):
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"""更复杂的过滤器，在应用启动后安全使用"""
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ignore_messages = [
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"Examining the path of torch.classes raised",
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"torch.cuda.is_available()",
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"CUDA initialization"
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]
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return not any(msg in record["message"] for msg in ignore_messages)
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logger.add(
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sys.stdout,
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level=_lvl,
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format=format_record,
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colorize=True,
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filter=advanced_filter
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)
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except Exception as e:
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# 如果过滤器设置失败，确保日志仍然可用
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logger.add(
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sys.stdout,
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level=_lvl,
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format=format_record,
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colorize=True
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)
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logger.error(f"设置高级日志过滤器失败: {e}")
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# 将高级过滤器设置放到启动主逻辑后
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import threading
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threading.Timer(5.0, setup_advanced_filters).start()
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def init_global_state():
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"""初始化全局状态"""
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if 'video_clip_json' not in st.session_state:
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st.session_state['video_clip_json'] = []
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if 'video_plot' not in st.session_state:
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st.session_state['video_plot'] = ''
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if 'ui_language' not in st.session_state:
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st.session_state['ui_language'] = config.ui.get("language", utils.get_system_locale())
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# 移除subclip_videos初始化 - 现在使用统一裁剪策略
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def tr(key):
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"""翻译函数"""
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i18n_dir = os.path.join(os.path.dirname(__file__), "webui", "i18n")
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locales = utils.load_locales(i18n_dir)
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loc = locales.get(st.session_state['ui_language'], {})
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return loc.get("Translation", {}).get(key, key)
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def render_generate_button():
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"""渲染生成按钮和处理逻辑"""
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if st.button(tr("Generate Video"), use_container_width=True, type="primary"):
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from app.services import task as tm
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from app.services import state as sm
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from app.models import const
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import threading
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import time
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import uuid
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config.save_config()
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# 移除task_id检查 - 现在使用统一裁剪策略，不再需要预裁剪
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# 直接检查必要的文件是否存在
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if not st.session_state.get('video_clip_json_path'):
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st.error(tr("脚本文件不能为空"))
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return
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if not st.session_state.get('video_origin_path'):
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st.error(tr("视频文件不能为空"))
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return
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# 获取所有参数
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script_params = script_settings.get_script_params()
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video_params = video_settings.get_video_params()
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audio_params = audio_settings.get_audio_params()
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subtitle_params = subtitle_settings.get_subtitle_params()
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# 合并所有参数
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all_params = {
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**script_params,
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**video_params,
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**audio_params,
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**subtitle_params
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}
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# 创建参数对象
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params = VideoClipParams(**all_params)
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# 生成一个新的task_id用于本次处理
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task_id = str(uuid.uuid4())
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# 创建进度条
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progress_bar = st.progress(0)
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status_text = st.empty()
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def run_task():
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try:
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tm.start_subclip_unified(
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task_id=task_id,
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params=params
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)
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except Exception as e:
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logger.error(f"任务执行失败: {e}")
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sm.state.update_task(task_id, state=const.TASK_STATE_FAILED, message=str(e))
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# 在新线程中启动任务
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thread = threading.Thread(target=run_task)
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thread.start()
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# 轮询任务状态
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while True:
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task = sm.state.get_task(task_id)
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if task:
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progress = task.get("progress", 0)
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state = task.get("state")
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# 更新进度条
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progress_bar.progress(progress / 100)
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status_text.text(f"Processing... {progress}%")
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if state == const.TASK_STATE_COMPLETE:
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status_text.text(tr("视频生成完成"))
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progress_bar.progress(1.0)
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# 显示结果
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video_files = task.get("videos", [])
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try:
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if video_files:
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player_cols = st.columns(len(video_files) * 2 + 1)
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for i, url in enumerate(video_files):
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player_cols[i * 2 + 1].video(url)
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except Exception as e:
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logger.error(f"播放视频失败: {e}")
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st.success(tr("视频生成完成"))
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break
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elif state == const.TASK_STATE_FAILED:
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st.error(f"任务失败: {task.get('message', 'Unknown error')}")
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break
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time.sleep(0.5)
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def main():
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"""主函数"""
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init_log()
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init_global_state()
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# ===== 显式注册 LLM 提供商（最佳实践）=====
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# 在应用启动时立即注册，确保所有 LLM 功能可用
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if 'llm_providers_registered' not in st.session_state:
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try:
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from app.services.llm.providers import register_all_providers
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register_all_providers()
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st.session_state['llm_providers_registered'] = True
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logger.info("✅ LLM 提供商注册成功")
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except Exception as e:
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logger.error(f"❌ LLM 提供商注册失败: {str(e)}")
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import traceback
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logger.error(traceback.format_exc())
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st.error(f"⚠️ LLM 初始化失败: {str(e)}\n\n请检查配置文件和依赖是否正确安装。")
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# 不抛出异常，允许应用继续运行（但 LLM 功能不可用）
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# 检测FFmpeg硬件加速，但只打印一次日志（使用 session_state 持久化）
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if 'hwaccel_logged' not in st.session_state:
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st.session_state['hwaccel_logged'] = False
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hwaccel_info = ffmpeg_utils.detect_hardware_acceleration()
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if not st.session_state['hwaccel_logged']:
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if hwaccel_info["available"]:
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logger.info(f"FFmpeg硬件加速检测结果: 可用 | 类型: {hwaccel_info['type']} | 编码器: {hwaccel_info['encoder']} | 独立显卡: {hwaccel_info['is_dedicated_gpu']}")
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else:
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logger.warning(f"FFmpeg硬件加速不可用: {hwaccel_info['message']}, 将使用CPU软件编码")
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st.session_state['hwaccel_logged'] = True
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# 仅初始化基本资源，避免过早地加载依赖PyTorch的资源
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# 检查是否能分解utils.init_resources()为基本资源和高级资源(如依赖PyTorch的资源)
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try:
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utils.init_resources()
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except Exception as e:
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logger.warning(f"资源初始化时出现警告: {e}")
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st.title(f"Narrato:blue[AI]:sunglasses: 📽️")
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st.write(tr("Get Help"))
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# 首先渲染不依赖PyTorch的UI部分
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# 渲染基础设置面板
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basic_settings.render_basic_settings(tr)
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# 渲染主面板
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panel = st.columns(3)
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with panel[0]:
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script_settings.render_script_panel(tr)
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with panel[1]:
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audio_settings.render_audio_panel(tr)
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with panel[2]:
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video_settings.render_video_panel(tr)
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subtitle_settings.render_subtitle_panel(tr)
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# 放到最后渲染可能使用PyTorch的部分
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# 渲染系统设置面板
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with panel[2]:
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system_settings.render_system_panel(tr)
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# 放到最后渲染生成按钮和处理逻辑
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render_generate_button()
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if __name__ == "__main__":
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main()
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