mirror of
https://github.com/linyqh/NarratoAI.git
synced 2025-12-13 12:12:50 +00:00
395 lines
14 KiB
Python
395 lines
14 KiB
Python
import streamlit as st
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import os
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from app.config import config
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from app.utils import utils
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def render_basic_settings(tr):
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"""渲染基础设置面板"""
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with st.expander(tr("Basic Settings"), expanded=False):
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config_panels = st.columns(3)
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left_config_panel = config_panels[0]
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middle_config_panel = config_panels[1]
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right_config_panel = config_panels[2]
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with left_config_panel:
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render_language_settings(tr)
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render_proxy_settings(tr)
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with middle_config_panel:
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render_vision_llm_settings(tr) # 视频分析模型设置
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with right_config_panel:
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render_text_llm_settings(tr) # 文案生成模型设置
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def render_language_settings(tr):
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st.subheader(tr("Proxy Settings"))
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"""渲染语言设置"""
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system_locale = utils.get_system_locale()
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i18n_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), "i18n")
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locales = utils.load_locales(i18n_dir)
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display_languages = []
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selected_index = 0
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for i, code in enumerate(locales.keys()):
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display_languages.append(f"{code} - {locales[code].get('Language')}")
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if code == st.session_state.get('ui_language', system_locale):
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selected_index = i
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selected_language = st.selectbox(
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tr("Language"),
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options=display_languages,
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index=selected_index
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)
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if selected_language:
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code = selected_language.split(" - ")[0].strip()
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st.session_state['ui_language'] = code
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config.ui['language'] = code
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def render_proxy_settings(tr):
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"""渲染代理设置"""
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proxy_url_http = config.proxy.get("http", "") or os.getenv("VPN_PROXY_URL", "")
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proxy_url_https = config.proxy.get("https", "") or os.getenv("VPN_PROXY_URL", "")
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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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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["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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# 视频分析模型提供商选择
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vision_providers = ['Gemini', 'NarratoAPI(待发布)', 'QwenVL(待发布)']
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saved_vision_provider = config.app.get("vision_llm_provider", "Gemini").lower()
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saved_provider_index = 0
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for i, provider in enumerate(vision_providers):
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if provider.lower() == saved_vision_provider:
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saved_provider_index = i
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break
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vision_provider = st.selectbox(
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tr("Vision Model Provider"),
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options=vision_providers,
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index=saved_provider_index
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)
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vision_provider = vision_provider.lower()
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config.app["vision_llm_provider"] = vision_provider
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st.session_state['vision_llm_providers'] = vision_provider
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# 获取已保存的视觉模型配置
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vision_api_key = config.app.get(f"vision_{vision_provider}_api_key", "")
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vision_base_url = config.app.get(f"vision_{vision_provider}_base_url", "")
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vision_model_name = config.app.get(f"vision_{vision_provider}_model_name", "")
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# 渲染视觉模型配置输入框
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st_vision_api_key = st.text_input(tr("Vision API Key"), value=vision_api_key, type="password")
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# 当选择 Gemini 时禁用 base_url 输入
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if vision_provider.lower() == 'gemini':
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st_vision_base_url = st.text_input(
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tr("Vision Base URL"),
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value=vision_base_url,
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disabled=True,
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help=tr("Gemini API does not require a base URL")
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)
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else:
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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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st.session_state[f"vision_{vision_provider}_api_key"] = st_vision_api_key # 用于script_settings.py
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if st_vision_base_url:
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config.app[f"vision_{vision_provider}_base_url"] = st_vision_base_url
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st.session_state[f"vision_{vision_provider}_base_url"] = st_vision_base_url
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if st_vision_model_name:
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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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#
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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 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 test_text_model_connection(api_key, base_url, model_name, provider, tr):
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"""测试文本模型连接
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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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import requests
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try:
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# 构建统一的测试请求（遵循OpenAI格式）
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json"
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}
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# 如果没有指定base_url，使用默认值
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if not base_url:
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if provider.lower() == 'openai':
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base_url = "https://api.openai.com/v1"
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elif provider.lower() == 'moonshot':
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base_url = "https://api.moonshot.cn/v1"
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elif provider.lower() == 'deepseek':
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base_url = "https://api.deepseek.com/v1"
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# 构建测试URL
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test_url = f"{base_url.rstrip('/')}/chat/completions"
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# 特殊处理Gemini
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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 or 'gemini-pro')
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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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# 构建测试消息
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test_data = {
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"model": model_name,
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"messages": [
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{"role": "user", "content": "直接回复我文本'当前网络可用'"}
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],
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"max_tokens": 10
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}
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# 发送测试请求
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response = requests.post(
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test_url,
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headers=headers,
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json=test_data,
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timeout=10
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)
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if response.status_code == 200:
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return True, tr("Text model is available")
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else:
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return False, f"{tr('Text model is not available')}: HTTP {response.status_code}"
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except Exception as e:
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return False, f"{tr('Connection failed')}: {str(e)}"
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def render_text_llm_settings(tr):
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"""渲染文案生成模型设置"""
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st.subheader(tr("Text Generation Model Settings"))
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# 文案生成模型提供商选择
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text_providers = ['OpenAI', 'Qwen', 'Moonshot', 'DeepSeek', 'Gemini']
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saved_text_provider = config.app.get("text_llm_provider", "OpenAI").lower()
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saved_provider_index = 0
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for i, provider in enumerate(text_providers):
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if provider.lower() == saved_text_provider:
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saved_provider_index = i
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break
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text_provider = st.selectbox(
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tr("Text Model Provider"),
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options=text_providers,
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index=saved_provider_index
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)
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text_provider = text_provider.lower()
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config.app["text_llm_provider"] = text_provider
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# 获取已保存的文本模型配置
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text_api_key = config.app.get(f"text_{text_provider}_api_key", "")
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text_base_url = config.app.get(f"text_{text_provider}_base_url", "")
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text_model_name = config.app.get(f"text_{text_provider}_model_name", "")
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# 渲染文本模型配置输入框
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st_text_api_key = st.text_input(tr("Text API Key"), value=text_api_key, type="password")
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st_text_base_url = st.text_input(tr("Text Base URL"), value=text_base_url)
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st_text_model_name = st.text_input(tr("Text Model Name"), value=text_model_name)
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# 添加测试按钮
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if st.button(tr("Test Connection"), key="test_text_connection"):
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with st.spinner(tr("Testing connection...")):
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success, message = test_text_model_connection(
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api_key=st_text_api_key,
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base_url=st_text_base_url,
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model_name=st_text_model_name,
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provider=text_provider,
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tr=tr
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)
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if success:
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st.success(message)
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else:
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st.error(message)
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# 保存文本模型配置
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if st_text_api_key:
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config.app[f"text_{text_provider}_api_key"] = st_text_api_key
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if st_text_base_url:
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config.app[f"text_{text_provider}_base_url"] = st_text_base_url
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if st_text_model_name:
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config.app[f"text_{text_provider}_model_name"] = st_text_model_name
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# Cloudflare 特殊配置
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if text_provider == 'cloudflare':
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st_account_id = st.text_input(
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tr("Account ID"),
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value=config.app.get(f"text_{text_provider}_account_id", "")
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)
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if st_account_id:
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config.app[f"text_{text_provider}_account_id"] = st_account_id
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