mirror of
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(webfeatui): 重构视觉分析功能并添加新模型支持
- 移除了对 QwenVL模型的特定逻辑，改为更通用的实现 - 添加了对 OpenAI 视觉模型的支持- 更新了视觉模型设置界面，增加了新的模型选项 - 重构了测试连接和创建分析器的代码，提高了可维护性 - 调整了配置文件结构，简化了视觉模型的配置
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linyq
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@ -237,28 +237,28 @@ if __name__ == '__main__':
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video_frame_description_path = "/Users/apple/Desktop/home/NarratoAI/storage/temp/analysis/frame_analysis_20250508_1139.json"
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# 测试新的JSON文件
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test_file_path = "/Users/apple/Desktop/home/NarratoAI/storage/temp/analysis/frame_analysis_20250508_1458.json"
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test_file_path = "/Users/apple/Desktop/home/NarratoAI/storage/temp/analysis/frame_analysis_20250508_2258.json"
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markdown_output = parse_frame_analysis_to_markdown(test_file_path)
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# print(markdown_output)
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# 输出到文件以便检查格式
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output_file = "/Users/apple/Desktop/home/NarratoAI/storage/temp/narration_script.md"
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output_file = "/Users/apple/Desktop/home/NarratoAI/storage/temp/家里家外1-5.md"
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with open(output_file, 'w', encoding='utf-8') as f:
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f.write(markdown_output)
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# print(f"\n已将Markdown输出保存到: {output_file}")
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# 生成解说文案
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narration = generate_narration(
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markdown_output,
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text_api_key,
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base_url=text_base_url,
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model=text_model
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)
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# 保存解说文案
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print(narration)
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print(type(narration))
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narration_file = "/Users/apple/Desktop/home/NarratoAI/storage/temp/final_narration_script.json"
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with open(narration_file, 'w', encoding='utf-8') as f:
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f.write(narration)
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print(f"\n已将解说文案保存到: {narration_file}")
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# # 生成解说文案
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# narration = generate_narration(
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# markdown_output,
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# text_api_key,
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# base_url=text_base_url,
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# model=text_model
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# )
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#
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# # 保存解说文案
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# print(narration)
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# print(type(narration))
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# narration_file = "/Users/apple/Desktop/home/NarratoAI/storage/temp/final_narration_script.json"
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# with open(narration_file, 'w', encoding='utf-8') as f:
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# f.write(narration)
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# print(f"\n已将解说文案保存到: {narration_file}")
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@ -1,175 +1,85 @@
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[app]
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project_version="0.6.0"
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# 支持视频理解的大模型提供商
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# gemini
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# qwenvl
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vision_llm_provider="qwenvl"
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# gemini (谷歌, 需要 VPN)
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# siliconflow (硅基流动)
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# qwenvl (通义千问)
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vision_llm_provider="Siliconflow"
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########## Vision Gemini API Key
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########## Gemini 视觉模型
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vision_gemini_api_key = ""
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vision_gemini_model_name = "gemini-2.0-flash"
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vision_gemini_model_name = "gemini-2.0-flash-lite"
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########## Vision Qwen API Key (默认使用“硅基流动”的QwenVL模型)
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########## QwenVL 视觉模型
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vision_qwenvl_api_key = ""
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vision_qwenvl_model_name = "Qwen/Qwen2.5-VL-32B-Instruct"
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vision_qwenvl_base_url = "https://api.siliconflow.cn/v1"
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vision_qwenvl_model_name = "qwen2.5-vl-32b-instruct"
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vision_qwenvl_base_url = "https://dashscope.aliyuncs.com/compatible-mode/v1"
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########### Vision NarratoAPI Key
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########## siliconflow 视觉模型
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vision_siliconflow_api_key = ""
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vision_siliconflow_model_name = "Qwen/Qwen2.5-VL-32B-Instruct"
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vision_siliconflow_base_url = "https://api.siliconflow.cn/v1"
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########## OpenAI 视觉模型
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vision_openai_api_key = ""
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vision_openai_model_name = "gpt-4.1-nano-2025-04-14"
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vision_openai_base_url = "https://api.openai.com/v1"
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########### NarratoAPI 微调模型 (未发布)
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narrato_api_key = "ggyY91BAO-_ULvAqKum3XexcyN1G3dP86DEzvjZDcrg"
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narrato_api_url = "https://narratoinsight.scsmtech.cn/api/v1"
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narrato_vision_model = "gemini-1.5-flash"
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narrato_vision_key = ""
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narrato_llm_model = "gpt-4o"
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narrato_llm_key = ""
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narrato_model = "narra-1.0-2025-05-09"
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# 用于生成文案的大模型支持的提供商 (Supported providers):
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# openai (默认)
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# deepseek (默认使用“硅基流动”的模型)
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# moonshot (月之暗面)
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# openai (默认, 需要 VPN)
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# siliconflow (硅基流动)
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# deepseek (深度求索)
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# gemini (谷歌, 需要 VPN)
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# qwen (通义千问)
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# gemini
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text_llm_provider="deepseek"
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# moonshot (月之暗面)
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text_llm_provider="openai"
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########## OpenAI API Key
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# Get your API key at https://platform.openai.com/api-keys
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text_openai_api_key = ""
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text_openai_base_url = "https://api.openai.com/v1"
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text_openai_model_name = "gpt-4o-mini"
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text_openai_model_name = "gpt-4.1-mini-2025-04-14"
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# 使用 硅基流动 第三方 API Key，使用手机号注册：https://cloud.siliconflow.cn/i/pyOKqFCV
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# 访问 https://cloud.siliconflow.cn/account/ak 获取你的 API 密钥
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text_siliconflow_api_key = ""
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text_siliconflow_base_url = "https://api.siliconflow.cn/v1"
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text_siliconflow_model_name = "deepseek-ai/DeepSeek-R1"
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########## DeepSeek API Key
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# 使用 硅基流动 第三方 API Key，使用手机号注册：https://cloud.siliconflow.cn/i/pyOKqFCV
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# 访问 https://platform.deepseek.com/api_keys 获取你的 API 密钥
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text_deepseek_api_key = ""
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text_deepseek_base_url = "https://api.siliconflow.cn/v1"
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text_deepseek_model_name = "deepseek-ai/DeepSeek-V3"
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########## Moonshot API Key
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# Visit https://platform.moonshot.cn/console/api-keys to get your API key.
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text_moonshot_api_key=""
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text_moonshot_base_url = "https://api.moonshot.cn/v1"
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text_moonshot_model_name = "moonshot-v1-8k"
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########## G4F
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# Visit https://github.com/xtekky/gpt4free to get more details
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# Supported model list: https://github.com/xtekky/gpt4free/blob/main/g4f/models.py
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text_g4f_model_name = "gpt-3.5-turbo"
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########## Azure API Key
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# Visit https://learn.microsoft.com/zh-cn/azure/ai-services/openai/ to get more details
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# API documentation: https://learn.microsoft.com/zh-cn/azure/ai-services/openai/reference
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text_azure_api_key = ""
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text_azure_base_url=""
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text_azure_model_name="gpt-35-turbo" # replace with your model deployment name
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text_azure_api_version = "2024-02-15-preview"
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text_deepseek_base_url = "https://api.deepseek.com"
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text_deepseek_model_name = "deepseek-chat"
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########## Gemini API Key
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text_gemini_api_key=""
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text_gemini_model_name = "gemini-1.5-flash"
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text_gemini_model_name = "gemini-2.0-flash"
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########## Qwen API Key
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# Visit https://dashscope.console.aliyun.com/apiKey to get your API key
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# Visit below links to get more details
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# https://tongyi.aliyun.com/qianwen/
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# https://help.aliyun.com/zh/dashscope/developer-reference/model-introduction
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# 访问 https://bailian.console.aliyun.com/?tab=model#/api-key 获取你的 API 密钥
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text_qwen_api_key = ""
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text_qwen_model_name = "qwen-plus-1127"
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text_qwen_base_url = "https://dashscope.aliyuncs.com/compatible-mode/v1"
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# 字幕提供商、可选，支持 whisper 和 faster-whisper-large-v2"whisper"
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# 默认为 faster-whisper-large-v2 模型地址：https://huggingface.co/guillaumekln/faster-whisper-large-v2
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subtitle_provider = "faster-whisper-large-v2"
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subtitle_enabled = true
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# ImageMagick
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# 安装后，将自动检测到 ImageMagick，Windows 除外！
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# 例如，在 Windows 上 "C:\Program Files (x86)\ImageMagick-7.1.1-Q16-HDRI\magick.exe"
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# 下载位置 https://imagemagick.org/archive/binaries/ImageMagick-7.1.1-29-Q16-x64-static.exe
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# imagemagick_path = "C:\\Program Files (x86)\\ImageMagick-7.1.1-Q16\\magick.exe"
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# FFMPEG
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#
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# 通常情况下，ffmpeg 会被自动下载，并且会被自动检测到。
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# 但是如果你的环境有问题，无法自动下载，可能会遇到如下错误：
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# RuntimeError: No ffmpeg exe could be found.
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# Install ffmpeg on your system, or set the IMAGEIO_FFMPEG_EXE environment variable.
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# 此时你可以手动下载 ffmpeg 并设置 ffmpeg_path，下载地址：https://www.gyan.dev/ffmpeg/builds/
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# ffmpeg_path = "C:\\Users\\harry\\Downloads\\ffmpeg.exe"
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#########################################################################################
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# 当视频生成成功后，API服务提供的视频下载接入点，默认为当前服务的地址和监听端口
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# 比如 http://127.0.0.1:8080/tasks/6357f542-a4e1-46a1-b4c9-bf3bd0df5285/final-1.mp4
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# 如果你需要使用域名对外提供服务（一般会用nginx做代理），则可以设置为你的域名
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# 比如 https://xxxx.com/tasks/6357f542-a4e1-46a1-b4c9-bf3bd0df5285/final-1.mp4
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# endpoint="https://xxxx.com"
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# When the video is successfully generated, the API service provides a download endpoint for the video, defaulting to the service's current address and listening port.
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# For example, http://127.0.0.1:8080/tasks/6357f542-a4e1-46a1-b4c9-bf3bd0df5285/final-1.mp4
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# If you need to provide the service externally using a domain name (usually done with nginx as a proxy), you can set it to your domain name.
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# For example, https://xxxx.com/tasks/6357f542-a4e1-46a1-b4c9-bf3bd0df5285/final-1.mp4
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# endpoint="https://xxxx.com"
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endpoint=""
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# Video material storage location
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# material_directory = "" # Indicates that video materials will be downloaded to the default folder, the default folder is ./storage/cache_videos under the current project
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# material_directory = "/user/harry/videos" # Indicates that video materials will be downloaded to a specified folder
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# material_directory = "task" # Indicates that video materials will be downloaded to the current task's folder, this method does not allow sharing of already downloaded video materials
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# 视频素材存放位置
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# material_directory = "" #表示将视频素材下载到默认的文件夹，默认文件夹为当前项目下的 ./storage/cache_videos
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# material_directory = "/user/harry/videos" #表示将视频素材下载到指定的文件夹中
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# material_directory = "task" #表示将视频素材下载到当前任务的文件夹中，这种方式无法共享已经下载的视频素材
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material_directory = ""
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# 用于任务的状态管理
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enable_redis = false
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redis_host = "localhost"
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redis_port = 6379
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redis_db = 0
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redis_password = ""
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# 文生视频时的最大并发任务数
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max_concurrent_tasks = 5
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########## Moonshot API Key
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# 访问 https://platform.moonshot.cn/console/api-keys 获取你的 API 密钥
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text_moonshot_api_key=""
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text_moonshot_base_url = "https://api.moonshot.cn/v1"
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text_moonshot_model_name = "moonshot-v1-8k"
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# webui界面是否显示配置项
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hide_config = false
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[whisper]
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# Only effective when subtitle_provider is "whisper"
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# Run on GPU with FP16
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# model = WhisperModel(model_size, device="cuda", compute_type="float16")
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# Run on GPU with INT8
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# model = WhisperModel(model_size, device="cuda", compute_type="int8_float16")
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# Run on CPU with INT8
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# model = WhisperModel(model_size, device="cpu", compute_type="int8")
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# recommended model_size: "large-v3"
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model_size="faster-whisper-large-v2"
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# 如果要使用 GPU，请设置 device=“cuda”
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device="CPU"
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compute_type="int8"
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hide_config = true
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[proxy]
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### Use a proxy to access the Pexels API
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### Format: "http://<username>:<password>@<proxy>:<port>"
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### Example: "http://user:pass@proxy:1234"
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### Doc: https://requests.readthedocs.io/en/latest/user/advanced/#proxies
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http = "http://127.0.0.1:7890"
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https = "http://127.0.0.1:7890"
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[azure]
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# Azure Speech API Key
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# Get your API key at https://portal.azure.com/#view/Microsoft_Azure_ProjectOxford/CognitiveServicesHub/~/SpeechServices
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speech_key=""
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speech_region=""
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enabled = false
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[frames]
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# 提取关键帧的间隔时间
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4
webui.py
4
webui.py
@ -5,7 +5,7 @@ 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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review_settings, merge_settings, system_settings
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from webui.utils import cache, file_utils
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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.models.schema import VideoClipParams, VideoAspect
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@ -184,7 +184,7 @@ def render_generate_button():
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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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file_utils.open_task_folder(config.root_dir, task_id)
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# file_utils.open_task_folder(config.root_dir, task_id)
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logger.info(tr("视频生成完成"))
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@ -64,25 +64,25 @@ def render_proxy_settings(tr):
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proxy_enabled = st.checkbox(tr("Enable Proxy"), value=proxy_enabled)
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# 保存代理开关状态
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config.proxy["enabled"] = proxy_enabled
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# config.proxy["enabled"] = proxy_enabled
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# 只有在代理启用时才显示代理设置输入框
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if proxy_enabled:
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HTTP_PROXY = st.text_input(tr("HTTP_PROXY"), value=proxy_url_http)
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HTTPS_PROXY = st.text_input(tr("HTTPs_PROXY"), value=proxy_url_https)
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if HTTP_PROXY:
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if HTTP_PROXY and HTTPS_PROXY:
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config.proxy["http"] = HTTP_PROXY
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os.environ["HTTP_PROXY"] = HTTP_PROXY
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if HTTPS_PROXY:
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config.proxy["https"] = HTTPS_PROXY
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os.environ["HTTP_PROXY"] = HTTP_PROXY
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os.environ["HTTPS_PROXY"] = HTTPS_PROXY
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# logger.debug(f"代理已启用: {HTTP_PROXY}")
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else:
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# 当代理被禁用时，清除环境变量和配置
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os.environ.pop("HTTP_PROXY", None)
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os.environ.pop("HTTPS_PROXY", None)
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config.proxy["http"] = ""
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config.proxy["https"] = ""
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# config.proxy["http"] = ""
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# config.proxy["https"] = ""
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def test_vision_model_connection(api_key, base_url, model_name, provider, tr):
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@ -108,29 +108,6 @@ def test_vision_model_connection(api_key, base_url, model_name, provider, tr):
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return True, tr("gemini model is available")
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except Exception as e:
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return False, f"{tr('gemini model is not available')}: {str(e)}"
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elif provider.lower() == 'qwenvl':
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from openai import OpenAI
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try:
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client = OpenAI(
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api_key=api_key,
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base_url=base_url or "https://dashscope.aliyuncs.com/compatible-mode/v1"
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)
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# 发送一个简单的测试请求
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response = client.chat.completions.create(
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model=model_name or "qwen-vl-max-latest",
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messages=[{"role": "user", "content": "直接回复我文本'当前网络可用'"}]
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)
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if response and response.choices:
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return True, tr("QwenVL model is available")
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else:
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return False, tr("QwenVL model returned invalid response")
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except Exception as e:
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return False, f"{tr('QwenVL model is not available')}: {str(e)}"
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elif provider.lower() == 'narratoapi':
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import requests
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try:
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@ -148,9 +125,46 @@ def test_vision_model_connection(api_key, base_url, model_name, provider, tr):
|
||||
return False, f"{tr('NarratoAPI is not available')}: HTTP {response.status_code}"
|
||||
except Exception as e:
|
||||
return False, f"{tr('NarratoAPI is not available')}: {str(e)}"
|
||||
|
||||
|
||||
else:
|
||||
return False, f"{tr('Unsupported provider')}: {provider}"
|
||||
from openai import OpenAI
|
||||
try:
|
||||
client = OpenAI(
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
)
|
||||
|
||||
response = client.chat.completions.create(
|
||||
model=model_name,
|
||||
messages=[
|
||||
{
|
||||
"role": "system",
|
||||
"content": [{"type": "text", "text": "You are a helpful assistant."}],
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"type": "image_url",
|
||||
"image_url": {
|
||||
"url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"
|
||||
},
|
||||
},
|
||||
{"type": "text", "text": "回复我网络可用即可"},
|
||||
],
|
||||
},
|
||||
],
|
||||
)
|
||||
if response and response.choices:
|
||||
return True, tr("QwenVL model is available")
|
||||
else:
|
||||
return False, tr("QwenVL model returned invalid response")
|
||||
|
||||
except Exception as e:
|
||||
# logger.debug(api_key)
|
||||
# logger.debug(base_url)
|
||||
# logger.debug(model_name)
|
||||
return False, f"{tr('QwenVL model is not available')}: {str(e)}"
|
||||
|
||||
|
||||
def render_vision_llm_settings(tr):
|
||||
@ -158,7 +172,7 @@ def render_vision_llm_settings(tr):
|
||||
st.subheader(tr("Vision Model Settings"))
|
||||
|
||||
# 视频分析模型提供商选择
|
||||
vision_providers = ['Gemini', 'QwenVL', 'NarratoAPI(待发布)']
|
||||
vision_providers = ['Siliconflow', 'Gemini', 'QwenVL', 'OpenAI']
|
||||
saved_vision_provider = config.app.get("vision_llm_provider", "Gemini").lower()
|
||||
saved_provider_index = 0
|
||||
|
||||
@ -194,8 +208,8 @@ def render_vision_llm_settings(tr):
|
||||
)
|
||||
st_vision_model_name = st.text_input(
|
||||
tr("Vision Model Name"),
|
||||
value=vision_model_name or "gemini-1.5-flash",
|
||||
help=tr("Default: gemini-1.5-flash")
|
||||
value=vision_model_name or "gemini-2.0-flash-lite",
|
||||
help=tr("Default: gemini-2.0-flash-lite")
|
||||
)
|
||||
elif vision_provider == 'qwenvl':
|
||||
st_vision_base_url = st.text_input(
|
||||
@ -261,52 +275,45 @@ def test_text_model_connection(api_key, base_url, model_name, provider, tr):
|
||||
"Authorization": f"Bearer {api_key}",
|
||||
"Content-Type": "application/json"
|
||||
}
|
||||
|
||||
# 如果没有指定base_url，使用默认值
|
||||
if not base_url:
|
||||
if provider.lower() == 'openai':
|
||||
base_url = "https://api.openai.com/v1"
|
||||
elif provider.lower() == 'moonshot':
|
||||
base_url = "https://api.moonshot.cn/v1"
|
||||
elif provider.lower() == 'deepseek':
|
||||
base_url = "https://api.deepseek.com"
|
||||
|
||||
# 构建测试URL
|
||||
test_url = f"{base_url.rstrip('/')}/chat/completions"
|
||||
|
||||
|
||||
# 特殊处理Gemini
|
||||
if provider.lower() == 'gemini':
|
||||
import google.generativeai as genai
|
||||
try:
|
||||
genai.configure(api_key=api_key)
|
||||
model = genai.GenerativeModel(model_name or 'gemini-pro')
|
||||
model = genai.GenerativeModel(model_name)
|
||||
model.generate_content("直接回复我文本'当前网络可用'")
|
||||
return True, tr("Gemini model is available")
|
||||
except Exception as e:
|
||||
return False, f"{tr('Gemini model is not available')}: {str(e)}"
|
||||
|
||||
# 构建测试消息
|
||||
test_data = {
|
||||
"model": model_name,
|
||||
"messages": [
|
||||
{"role": "user", "content": "直接回复我文本'当前网络可用'"}
|
||||
],
|
||||
"stream": False
|
||||
}
|
||||
|
||||
# 发送测试请求
|
||||
response = requests.post(
|
||||
test_url,
|
||||
headers=headers,
|
||||
json=test_data,
|
||||
)
|
||||
|
||||
if response.status_code == 200:
|
||||
return True, tr("Text model is available")
|
||||
else:
|
||||
return False, f"{tr('Text model is not available')}: HTTP {response.status_code}"
|
||||
test_url = f"{base_url.rstrip('/')}/chat/completions"
|
||||
|
||||
# 构建测试消息
|
||||
test_data = {
|
||||
"model": model_name,
|
||||
"messages": [
|
||||
{"role": "user", "content": "直接回复我文本'当前网络可用'"}
|
||||
],
|
||||
"stream": False
|
||||
}
|
||||
|
||||
# 发送测试请求
|
||||
response = requests.post(
|
||||
test_url,
|
||||
headers=headers,
|
||||
json=test_data,
|
||||
)
|
||||
# logger.debug(model_name)
|
||||
# logger.debug(api_key)
|
||||
# logger.debug(test_url)
|
||||
if response.status_code == 200:
|
||||
return True, tr("Text model is available")
|
||||
else:
|
||||
return False, f"{tr('Text model is not available')}: HTTP {response.status_code}"
|
||||
|
||||
except Exception as e:
|
||||
logger.error(traceback.format_exc())
|
||||
return False, f"{tr('Connection failed')}: {str(e)}"
|
||||
|
||||
|
||||
@ -315,8 +322,8 @@ def render_text_llm_settings(tr):
|
||||
st.subheader(tr("Text Generation Model Settings"))
|
||||
|
||||
# 文案生成模型提供商选择
|
||||
text_providers = ['DeepSeek', 'OpenAI', 'Siliconflow', 'Qwen', 'Moonshot', 'Gemini']
|
||||
saved_text_provider = config.app.get("text_llm_provider", "DeepSeek").lower()
|
||||
text_providers = ['OpenAI', 'Siliconflow', 'DeepSeek', 'Gemini', 'Qwen', 'Moonshot']
|
||||
saved_text_provider = config.app.get("text_llm_provider", "OpenAI").lower()
|
||||
saved_provider_index = 0
|
||||
|
||||
for i, provider in enumerate(text_providers):
|
||||
@ -344,8 +351,6 @@ def render_text_llm_settings(tr):
|
||||
|
||||
# 添加测试按钮
|
||||
if st.button(tr("Test Connection"), key="test_text_connection"):
|
||||
logger.debug(st_text_base_url)
|
||||
logger.debug(st_text_model_name)
|
||||
with st.spinner(tr("Testing connection...")):
|
||||
success, message = test_text_model_connection(
|
||||
api_key=st_text_api_key,
|
||||
|
||||
@ -24,15 +24,13 @@ def create_vision_analyzer(provider, api_key, model, base_url):
|
||||
"""
|
||||
if provider == 'gemini':
|
||||
return gemini_analyzer.VisionAnalyzer(model_name=model, api_key=api_key)
|
||||
elif provider == 'qwenvl':
|
||||
else:
|
||||
# 只传入必要的参数
|
||||
return qwenvl_analyzer.QwenAnalyzer(
|
||||
model_name=model,
|
||||
api_key=api_key,
|
||||
base_url=base_url
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"不支持的视觉分析提供商: {provider}")
|
||||
|
||||
|
||||
def get_batch_timestamps(batch_files, prev_batch_files=None):
|
||||
|
||||
@ -4,16 +4,12 @@ import json
|
||||
import time
|
||||
import asyncio
|
||||
import traceback
|
||||
import requests
|
||||
from app.utils import video_processor
|
||||
import streamlit as st
|
||||
from loguru import logger
|
||||
from requests.adapters import HTTPAdapter
|
||||
from datetime import datetime
|
||||
|
||||
from app.config import config
|
||||
from app.utils.script_generator import ScriptProcessor
|
||||
from app.utils import utils, video_processor, qwenvl_analyzer
|
||||
from app.utils import utils, video_processor
|
||||
from webui.tools.base import create_vision_analyzer, get_batch_files, get_batch_timestamps, chekc_video_config
|
||||
|
||||
|
||||
@ -111,12 +107,10 @@ def generate_script_docu(params):
|
||||
vision_api_key = st.session_state.get('vision_gemini_api_key')
|
||||
vision_model = st.session_state.get('vision_gemini_model_name')
|
||||
vision_base_url = st.session_state.get('vision_gemini_base_url')
|
||||
elif vision_llm_provider == 'qwenvl':
|
||||
vision_api_key = st.session_state.get('vision_qwenvl_api_key')
|
||||
vision_model = st.session_state.get('vision_qwenvl_model_name', 'qwen-vl-max-latest')
|
||||
vision_base_url = st.session_state.get('vision_qwenvl_base_url')
|
||||
else:
|
||||
raise ValueError(f"不支持的视觉分析提供商: {vision_llm_provider}")
|
||||
vision_api_key = st.session_state.get(f'vision_{vision_llm_provider}_api_key')
|
||||
vision_model = st.session_state.get(f'vision_{vision_llm_provider}_model_name')
|
||||
vision_base_url = st.session_state.get(f'vision_{vision_llm_provider}_base_url')
|
||||
|
||||
# 创建视觉分析器实例
|
||||
analyzer = create_vision_analyzer(
|
||||
@ -354,7 +348,6 @@ def generate_script_docu(params):
|
||||
# 整理帧分析数据
|
||||
markdown_output = parse_frame_analysis_to_markdown(analysis_json_path)
|
||||
|
||||
# 生成文案
|
||||
# 生成解说文案
|
||||
narration = generate_narration(
|
||||
markdown_output,
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user