mirror of
https://github.com/linyqh/NarratoAI.git
synced 2026-01-27 06:38:16 +00:00
linyq
GitHub
commit
782c163bde
@ -66,6 +66,51 @@ def render_proxy_settings(tr):
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os.environ["HTTPS_PROXY"] = HTTPS_PROXY
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def test_vision_model_connection(api_key, base_url, model_name, provider, tr):
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"""测试视觉模型连接
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Args:
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api_key: API密钥
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base_url: 基础URL
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model_name: 模型名称
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provider: 提供商名称
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Returns:
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bool: 连接是否成功
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str: 测试结果消息
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"""
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if provider.lower() == 'gemini':
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import google.generativeai as genai
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try:
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genai.configure(api_key=api_key)
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model = genai.GenerativeModel(model_name)
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model.generate_content("直接回复我文本'当前网络可用'")
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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() == 'narratoapi':
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import requests
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try:
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# 构建测试请求
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headers = {
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"Authorization": f"Bearer {api_key}"
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}
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test_url = f"{base_url.rstrip('/')}/health"
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response = requests.get(test_url, headers=headers, timeout=10)
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if response.status_code == 200:
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return True, tr("NarratoAPI is available")
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else:
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return False, f"{tr('NarratoAPI is not available')}: HTTP {response.status_code}"
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except Exception as e:
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return False, f"{tr('NarratoAPI is not available')}: {str(e)}"
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else:
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return False, f"{tr('Unsupported provider')}: {provider}"
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def render_vision_llm_settings(tr):
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"""渲染视频分析模型设置"""
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st.subheader(tr("Vision Model Settings"))
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@ -99,6 +144,22 @@ def render_vision_llm_settings(tr):
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st_vision_base_url = st.text_input(tr("Vision Base URL"), value=vision_base_url)
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st_vision_model_name = st.text_input(tr("Vision Model Name"), value=vision_model_name)
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# 在配置输入框后添加测试按钮
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if st.button(tr("Test Connection"), key="test_vision_connection"):
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with st.spinner(tr("Testing connection...")):
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success, message = test_vision_model_connection(
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api_key=st_vision_api_key,
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base_url=st_vision_base_url,
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model_name=st_vision_model_name,
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provider=vision_provider,
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tr=tr
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)
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if success:
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st.success(tr(message))
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else:
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st.error(tr(message))
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# 保存视觉模型配置
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if st_vision_api_key:
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config.app[f"vision_{vision_provider}_api_key"] = st_vision_api_key
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@ -110,80 +171,80 @@ def render_vision_llm_settings(tr):
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config.app[f"vision_{vision_provider}_model_name"] = st_vision_model_name
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st.session_state[f"vision_{vision_provider}_model_name"] = st_vision_model_name
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# NarratoAPI 特殊配置
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if vision_provider == 'narratoapi':
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st.subheader(tr("Narrato Additional Settings"))
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# Narrato API 基础配置
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narrato_api_key = st.text_input(
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tr("Narrato API Key"),
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value=config.app.get("narrato_api_key", ""),
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type="password",
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help="用于访问 Narrato API 的密钥"
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)
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if narrato_api_key:
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config.app["narrato_api_key"] = narrato_api_key
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st.session_state['narrato_api_key'] = narrato_api_key
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narrato_api_url = st.text_input(
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tr("Narrato API URL"),
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value=config.app.get("narrato_api_url", "http://127.0.0.1:8000/api/v1/video/analyze")
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)
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if narrato_api_url:
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config.app["narrato_api_url"] = narrato_api_url
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st.session_state['narrato_api_url'] = narrato_api_url
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# 视频分析模型配置
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st.markdown("##### " + tr("Vision Model Settings"))
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narrato_vision_model = st.text_input(
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tr("Vision Model Name"),
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value=config.app.get("narrato_vision_model", "gemini-1.5-flash")
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)
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narrato_vision_key = st.text_input(
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tr("Vision Model API Key"),
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value=config.app.get("narrato_vision_key", ""),
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type="password",
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help="用于视频分析的模型 API Key"
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)
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if narrato_vision_model:
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config.app["narrato_vision_model"] = narrato_vision_model
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st.session_state['narrato_vision_model'] = narrato_vision_model
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if narrato_vision_key:
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config.app["narrato_vision_key"] = narrato_vision_key
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st.session_state['narrato_vision_key'] = narrato_vision_key
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# 文案生成模型配置
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st.markdown("##### " + tr("Text Generation Model Settings"))
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narrato_llm_model = st.text_input(
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tr("LLM Model Name"),
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value=config.app.get("narrato_llm_model", "qwen-plus")
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)
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narrato_llm_key = st.text_input(
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tr("LLM Model API Key"),
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value=config.app.get("narrato_llm_key", ""),
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type="password",
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help="用于文案生成的模型 API Key"
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)
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if narrato_llm_model:
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config.app["narrato_llm_model"] = narrato_llm_model
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st.session_state['narrato_llm_model'] = narrato_llm_model
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if narrato_llm_key:
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config.app["narrato_llm_key"] = narrato_llm_key
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st.session_state['narrato_llm_key'] = narrato_llm_key
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# 批处理配置
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narrato_batch_size = st.number_input(
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tr("Batch Size"),
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min_value=1,
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max_value=50,
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value=config.app.get("narrato_batch_size", 10),
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help="每批处理的图片数量"
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)
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if narrato_batch_size:
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config.app["narrato_batch_size"] = narrato_batch_size
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st.session_state['narrato_batch_size'] = narrato_batch_size
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# # NarratoAPI 特殊配置
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# if vision_provider == 'narratoapi':
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# st.subheader(tr("Narrato Additional Settings"))
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#
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# # Narrato API 基础配置
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# narrato_api_key = st.text_input(
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# tr("Narrato API Key"),
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# value=config.app.get("narrato_api_key", ""),
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# type="password",
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# help="用于访问 Narrato API 的密钥"
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# )
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# if narrato_api_key:
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# config.app["narrato_api_key"] = narrato_api_key
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# st.session_state['narrato_api_key'] = narrato_api_key
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#
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# narrato_api_url = st.text_input(
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# tr("Narrato API URL"),
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# value=config.app.get("narrato_api_url", "http://127.0.0.1:8000/api/v1/video/analyze")
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# )
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# if narrato_api_url:
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# config.app["narrato_api_url"] = narrato_api_url
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# st.session_state['narrato_api_url'] = narrato_api_url
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#
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# # 视频分析模型配置
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# st.markdown("##### " + tr("Vision Model Settings"))
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# narrato_vision_model = st.text_input(
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# tr("Vision Model Name"),
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# value=config.app.get("narrato_vision_model", "gemini-1.5-flash")
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# )
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# narrato_vision_key = st.text_input(
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# tr("Vision Model API Key"),
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# value=config.app.get("narrato_vision_key", ""),
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# type="password",
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# help="用于视频分析的模型 API Key"
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# )
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#
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# if narrato_vision_model:
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# config.app["narrato_vision_model"] = narrato_vision_model
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# st.session_state['narrato_vision_model'] = narrato_vision_model
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# if narrato_vision_key:
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# config.app["narrato_vision_key"] = narrato_vision_key
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# st.session_state['narrato_vision_key'] = narrato_vision_key
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#
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# # 文案生成模型配置
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# st.markdown("##### " + tr("Text Generation Model Settings"))
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# narrato_llm_model = st.text_input(
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# tr("LLM Model Name"),
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# value=config.app.get("narrato_llm_model", "qwen-plus")
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# )
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# narrato_llm_key = st.text_input(
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# tr("LLM Model API Key"),
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# value=config.app.get("narrato_llm_key", ""),
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# type="password",
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# help="用于文案生成的模型 API Key"
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# )
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#
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# if narrato_llm_model:
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# config.app["narrato_llm_model"] = narrato_llm_model
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# st.session_state['narrato_llm_model'] = narrato_llm_model
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# if narrato_llm_key:
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# config.app["narrato_llm_key"] = narrato_llm_key
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# st.session_state['narrato_llm_key'] = narrato_llm_key
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#
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# # 批处理配置
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# narrato_batch_size = st.number_input(
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# tr("Batch Size"),
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# min_value=1,
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# max_value=50,
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# value=config.app.get("narrato_batch_size", 10),
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# help="每批处理的图片数量"
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# )
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# if narrato_batch_size:
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# config.app["narrato_batch_size"] = narrato_batch_size
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# st.session_state['narrato_batch_size'] = narrato_batch_size
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def render_text_llm_settings(tr):
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@ -205,6 +205,40 @@ def render_video_details(tr):
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def render_script_buttons(tr, params):
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"""渲染脚本操作按钮"""
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# 新增三个输入框，放在同一行
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input_cols = st.columns(3)
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with input_cols[0]:
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skip_seconds = st.number_input(
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"skip_seconds",
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min_value=0,
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value=st.session_state.get('skip_seconds', config.frames.get('skip_seconds', 0)),
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help=tr("Skip the first few seconds"),
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key="skip_seconds_input"
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)
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st.session_state['skip_seconds'] = skip_seconds
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with input_cols[1]:
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threshold = st.number_input(
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"threshold",
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min_value=0,
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value=st.session_state.get('threshold', config.frames.get('threshold', 30)),
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help=tr("Difference threshold"),
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key="threshold_input"
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)
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st.session_state['threshold'] = threshold
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with input_cols[2]:
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vision_batch_size = st.number_input(
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"vision_batch_size",
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min_value=1,
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max_value=20,
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value=st.session_state.get('vision_batch_size', config.frames.get('vision_batch_size', 5)),
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help=tr("Vision processing batch size"),
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key="vision_batch_size_input"
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)
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st.session_state['vision_batch_size'] = vision_batch_size
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# 生成/加载按钮
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script_path = st.session_state.get('video_clip_json_path', '')
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if script_path == "auto":
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@ -287,7 +321,6 @@ def generate_script(tr, params):
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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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st.stop()
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return
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# ===================提取键帧===================
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@ -323,8 +356,8 @@ def generate_script(tr, params):
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# 处理视频并提取关键帧
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processor.process_video_pipeline(
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output_dir=video_keyframes_dir,
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skip_seconds=config.frames.get("skip_seconds", 0),
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threshold=config.frames.get("threshold", 30)
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skip_seconds=st.session_state.get('skip_seconds'),
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threshold=st.session_state.get('threshold')
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)
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else:
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processor = video_processor.VideoProcessor(params.video_origin_path)
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@ -353,7 +386,7 @@ def generate_script(tr, params):
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except Exception as cleanup_err:
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logger.error(f"清理失败的关键帧目录时出错: {cleanup_err}")
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raise Exception(f"关键帧提取<EFBFBD><EFBFBD>败: {str(e)}")
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raise Exception(f"关键帧提取失败: {str(e)}")
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# 根据不同的 LLM 提供商处理
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vision_llm_provider = st.session_state.get('vision_llm_providers').lower()
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@ -374,7 +407,7 @@ def generate_script(tr, params):
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analyzer = vision_analyzer.VisionAnalyzer(
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model_name=vision_model,
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api_key=vision_api_key
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api_key=vision_api_key,
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)
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update_progress(40, "正在分析关键帧...")
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@ -388,7 +421,7 @@ def generate_script(tr, params):
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analyzer.analyze_images(
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images=keyframe_files,
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prompt=config.app.get('vision_analysis_prompt'),
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batch_size=config.frames.get("vision_batch_size", 5)
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batch_size=config.frames.get("vision_batch_size", st.session_state.get('vision_batch_size', 5))
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)
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)
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loop.close()
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@ -125,6 +125,15 @@
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"Text API Key": "文案生成 API 密钥",
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"Text Base URL": "文案生成接口地址",
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"Text Model Name": "文案生成模型名称",
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"Account ID": "账户 ID"
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"Account ID": "账户 ID",
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"Skip the first few seconds": "跳过开头多少秒",
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"Difference threshold": "差异阈值",
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"Vision processing batch size": "视觉处理批次大小",
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"Test Connection": "测试连接",
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"gemini model is available": "Gemini 模型可用",
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"gemini model is not available": "Gemini 模型不可用",
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"NarratoAPI is available": "NarratoAPI 可用",
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"NarratoAPI is not available": "NarratoAPI 不可用",
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"Unsupported provider": "不支持的提供商"
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}
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}
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}
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