mirror of
https://github.com/linyqh/NarratoAI.git
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查漏补缺
This commit is contained in:
linyqh
parent
decac3b11d
commit
f04fd70ab8
1
.gitignore
vendored
1
.gitignore
vendored
@ -27,3 +27,4 @@ resource/scripts/*
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resource/videos/*
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resource/songs/*
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resource/fonts/*
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app/models/faster-whisper-large-v2/*
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16
Dockerfile
16
Dockerfile
@ -1,4 +1,3 @@
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# Use an official Python runtime as a parent image
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FROM python:3.10-slim-bullseye
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# Set the working directory in the container
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@ -12,6 +11,7 @@ ENV PYTHONPATH="/NarratoAI"
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# Install system dependencies
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RUN apt-get update && apt-get install -y \
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git \
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git-lfs \
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imagemagick \
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ffmpeg \
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wget \
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@ -29,17 +29,11 @@ RUN pip install --no-cache-dir -r requirements.txt
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# Now copy the rest of the codebase into the image
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COPY . .
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# 安装 git lfs 并下载模型到指定目录
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RUN git lfs install
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# Expose the port the app runs on
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EXPOSE 8501
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# Command to run the application
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CMD ["streamlit", "run", "./webui/Main.py","--browser.serverAddress=127.0.0.1","--server.enableCORS=True","--browser.gatherUsageStats=False"]
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# 1. Build the Docker image using the following command
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# docker build -t moneyprinterturbo .
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# 2. Run the Docker container using the following command
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## For Linux or MacOS:
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# docker run -v $(pwd)/config.toml:/NarratoAI/config.toml -v $(pwd)/storage:/NarratoAI/storage -p 8501:8501 moneyprinterturbo
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## For Windows:
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# docker run -v %cd%/config.toml:/NarratoAI/config.toml -v %cd%/storage:/NarratoAI/storage -p 8501:8501 moneyprinterturbo
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CMD ["streamlit", "run", "webui.py","--browser.serverAddress=127.0.0.1","--server.enableCORS=True","--browser.gatherUsageStats=False"]
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@ -109,26 +109,25 @@ Method = """
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def handle_exception(err):
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if isinstance(err, PermissionDenied):
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logger.error("403 用户没有权限访问该资源")
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raise Exception("403 用户没有权限访问该资源")
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elif isinstance(err, ResourceExhausted):
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logger.error("429 您的配额已用尽。请稍后重试。请考虑设置自动重试来处理这些错误")
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raise Exception("429 您的配额已用尽。请稍后重试。请考虑设置自动重试来处理这些错误")
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elif isinstance(err, InvalidArgument):
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logger.error("400 参数无效。例如，文件过大，超出了载荷大小限制。另一个事件提供了无效的 API 密钥。")
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raise Exception("400 参数无效。例如，文件过大，超出了载荷大小限制。另一个事件提供了无效的 API 密钥。")
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elif isinstance(err, AlreadyExists):
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logger.error("409 已存在具有相同 ID 的已调参模型。对新模型进行调参时，请指定唯一的模型 ID。")
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raise Exception("409 已存在具有相同 ID 的已调参模型。对新模型进行调参时，请指定唯一的模型 ID。")
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elif isinstance(err, RetryError):
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logger.error("使用不支持 gRPC 的代理时可能会引起此错误。请尝试将 REST 传输与 genai.configure(..., transport=rest) 搭配使用。")
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raise Exception("使用不支持 gRPC 的代理时可能会引起此错误。请尝试将 REST 传输与 genai.configure(..., transport=rest) 搭配使用。")
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elif isinstance(err, BlockedPromptException):
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logger.error("400 出于安全原因，该提示已被屏蔽。")
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raise Exception("400 出于安全原因，该提示已被屏蔽。")
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elif isinstance(err, BrokenResponseError):
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logger.error("500 流式传输响应已损坏。在访问需要完整响应的内容（例如聊天记录）时引发。查看堆栈轨迹中提供的错误详情。")
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raise Exception("500 流式传输响应已损坏。在访问需要完整响应的内容（例如聊天记录）时引发。查看堆栈轨迹中提供的错误详情。")
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elif isinstance(err, IncompleteIterationError):
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logger.error("500 访问需要完整 API 响应但流式响应尚未完全迭代的内容时引发。对响应对象调用 resolve() 以使用迭代器。")
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raise Exception("500 访问需要完整 API 响应但流式响应尚未完全迭代的内容时引发。对响应对象调用 resolve() 以使用迭代器。")
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elif isinstance(err, ConnectionError):
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logger.error("网络连接错误，请检查您的网络连接。")
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raise Exception("网络连接错误, 请检查您的网络连接(建议使用 NarratoAI 官方提供的 url)")
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else:
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logger.error(f"大模型请求失败, 下面是具体报错信息: \n{traceback.format_exc()}")
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return ""
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raise Exception(f"大模型请求失败, 下面是具体报错信息: \n\n{traceback.format_exc()}")
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def _generate_response(prompt: str, llm_provider: str = None) -> str:
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@ -398,9 +397,6 @@ def compress_video(input_path: str, output_path: str):
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input_path: 输入视频文件路径
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output_path: 输出压缩后的视频文件路径
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"""
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# 指定 ffmpeg 的完整路径
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ffmpeg_path = os.getenv("FFMPEG_PATH") or config.app.get("ffmpeg_path") or "ffmpeg"
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# 如果压缩后的视频文件已经存在，则直接使用
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if os.path.exists(output_path):
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logger.info(f"压缩视频文件已存在: {output_path}")
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@ -409,17 +405,6 @@ def compress_video(input_path: str, output_path: str):
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try:
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clip = VideoFileClip(input_path)
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clip.write_videofile(output_path, codec='libx264', audio_codec='aac', bitrate="500k", audio_bitrate="128k")
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# command = [
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# ffmpeg_path,
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# "-i", input_path,
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# "-c:v", "h264",
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# "-b:v", "500k",
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# "-c:a", "aac",
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# "-b:a", "128k",
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# output_path
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# ]
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# logger.info(f"执行命令: {' '.join(command)}")
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# subprocess.run(command, check=True)
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except subprocess.CalledProcessError as e:
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logger.error(f"视频压缩失败: {e}")
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raise
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@ -440,41 +425,45 @@ def generate_script(
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Returns:
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str: 生成的脚本
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"""
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# 1. 压缩视频
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compressed_video_path = f"{os.path.splitext(video_path)[0]}_compressed.mp4"
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compress_video(video_path, compressed_video_path)
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try:
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# 1. 压缩视频
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compressed_video_path = f"{os.path.splitext(video_path)[0]}_compressed.mp4"
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compress_video(video_path, compressed_video_path)
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# 在关键步骤更新进度
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if progress_callback:
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progress_callback(15, "压缩完成") # 例如,在压缩视频后
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# 2. 转录视频
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transcription = gemini_video_transcription(
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video_name=video_name,
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video_path=compressed_video_path,
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language=language,
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llm_provider_video="gemini",
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progress_callback=progress_callback
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)
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if progress_callback:
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progress_callback(60, "生成解说文案...") # 例如,在转录视频后
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# 3. 编写解说文案
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script = writing_short_play(video_plot, video_name, "openai", count=300)
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# 在关键步骤更新进度
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if progress_callback:
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progress_callback(70, "匹配画面...") # 例如,在生成脚本后
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# 4. 文案匹配画面
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if transcription != "":
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matched_script = screen_matching(huamian=transcription, wenan=script, llm_provider="openai")
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# 在关键步骤更新进度
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if progress_callback:
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progress_callback(80, "匹配成功")
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return matched_script
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else:
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return ""
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progress_callback(15, "压缩完成") # 例如,在压缩视频后
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# 2. 转录视频
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transcription = gemini_video_transcription(
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video_name=video_name,
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video_path=compressed_video_path,
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language=language,
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llm_provider_video="gemini",
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progress_callback=progress_callback
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)
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if progress_callback:
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progress_callback(60, "生成解说文案...") # 例如,在转录视频后
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# 3. 编写解说文案
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script = writing_short_play(video_plot, video_name, "openai", count=300)
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# 在关键步骤更新进度
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if progress_callback:
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progress_callback(70, "匹配画面...") # 例如,在生成脚本后
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# 4. 文案匹配画面
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if transcription != "":
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matched_script = screen_matching(huamian=transcription, wenan=script, llm_provider="openai")
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# 在关键步骤更新进度
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if progress_callback:
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progress_callback(80, "匹配成功")
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return matched_script
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else:
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return ""
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except Exception as e:
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handle_exception(e)
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raise
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def gemini_video_transcription(video_name: str, video_path: str, language: str, llm_provider_video: str, progress_callback=None):
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@ -6,6 +6,7 @@ services:
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build:
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context: .
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dockerfile: Dockerfile
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image: linyq1/narratoai:latest
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container_name: "webui"
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ports:
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- "8501:8501"
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@ -18,10 +19,11 @@ services:
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build:
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context: .
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dockerfile: Dockerfile
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image: linyq1/narratoai:latest
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container_name: "api"
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ports:
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- "8502:22"
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command: [ "sleep", "48h" ]
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- "8502:8080"
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command: [ "python3", "main.py" ]
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volumes: *common-volumes
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environment:
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- "VPN_PROXY_URL=http://host.docker.internal:7890"
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@ -14,7 +14,7 @@ pillow~=10.3.0
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pydantic~=2.6.3
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g4f~=0.3.0.4
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dashscope~=1.15.0
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google.generativeai>=0.7.2
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google.generativeai>=0.8.2
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python-multipart~=0.0.9
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redis==5.0.3
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# if you use pillow~=10.3.0, you will get "PIL.Image' has no attribute 'ANTIALIAS'" error when resize video
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@ -25,3 +25,4 @@ opencv-python~=4.9.0.80
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azure-cognitiveservices-speech~=1.37.0
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git-changelog~=2.5.2
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watchdog==5.0.2
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pydub==0.25.1
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10
webui.py
10
webui.py
@ -66,6 +66,8 @@ 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", system_locale)
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if 'subclip_videos' not in st.session_state:
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st.session_state['subclip_videos'] = {}
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def get_all_fonts():
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@ -404,8 +406,8 @@ with left_panel:
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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 e:
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st.error(f"生成过程中发生错误: {traceback.format_exc()}")
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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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finally:
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time.sleep(2) # 给用户一些时间查看最终状态
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progress_bar.empty()
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@ -445,7 +447,7 @@ with left_panel:
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st.session_state['video_clip_json'] = data
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st.session_state['video_clip_json_path'] = save_path
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# 刷新页面
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# st.rerun()
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st.rerun()
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# 裁剪视频
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with button_columns[1]:
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@ -677,7 +679,7 @@ with right_panel:
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# 视频编辑面板
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with st.expander(tr("Video Check"), expanded=False):
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try:
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video_list = st.session_state['video_script_list']
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video_list = st.session_state.video_clip_json
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except KeyError as e:
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video_list = []
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19
webui.sh
19
webui.sh
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#!/bin/bash
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# 从环境变量中加载VPN代理的配置URL
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vpn_proxy_url="http://127.0.0.1:7890"
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vpn_proxy_url="$VPN_PROXY_URL"
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# 检查是否成功加载
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if [ -z "$vpn_proxy_url" ]; then
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echo "VPN代理配置URL未设置，请检查环境变量VPN_PROXY_URL"
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@ -44,7 +44,24 @@ for url in "${!urls_paths[@]}"; do
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echo "下载失败: $url" >&2
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}
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done
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# 安装 git lfs 并下载模型到指定目录
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git lfs install
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mkdir -p /NarratoAI/app/models
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cd /NarratoAI/app/models
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if [ ! -d "faster-whisper-large-v2" ] || [ -z "$(ls -A faster-whisper-large-v2)" ]; then
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if git clone https://huggingface.co/guillaumekln/faster-whisper-large-v2; then
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echo "下载faster-whisper-large-v2成功"
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else
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echo "下载faster-whisper-large-v2失败" >&2
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exit 1
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fi
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else
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echo "faster-whisper-large-v2 已存在，跳过下载"
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fi
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# 等待所有后台任务完成
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wait
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echo "所有文件已成功下载到指定目录"
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cd /NarratoAI/
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streamlit run webui.py --browser.serverAddress="0.0.0.0" --server.enableCORS=True --server.maxUploadSize=2048 --browser.gatherUsageStats=False
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