mirror of
https://github.com/linyqh/NarratoAI.git
synced 2025-12-11 10:32:49 +00:00
693 lines
26 KiB
Python
693 lines
26 KiB
Python
import traceback
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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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from loguru import logger
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def validate_api_key(api_key: str, provider: str) -> tuple[bool, str]:
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"""验证API密钥格式"""
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if not api_key or not api_key.strip():
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return False, f"{provider} API密钥不能为空"
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# 基本长度检查
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if len(api_key.strip()) < 10:
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return False, f"{provider} API密钥长度过短，请检查是否正确"
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return True, ""
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def validate_base_url(base_url: str, provider: str) -> tuple[bool, str]:
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"""验证Base URL格式"""
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if not base_url or not base_url.strip():
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return True, "" # base_url可以为空
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base_url = base_url.strip()
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if not (base_url.startswith('http://') or base_url.startswith('https://')):
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return False, f"{provider} Base URL必须以http://或https://开头"
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return True, ""
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def validate_model_name(model_name: str, provider: str) -> tuple[bool, str]:
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"""验证模型名称"""
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if not model_name or not model_name.strip():
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return False, f"{provider} 模型名称不能为空"
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return True, ""
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def show_config_validation_errors(errors: list):
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"""显示配置验证错误"""
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if errors:
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for error in errors:
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st.error(error)
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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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# 获取当前代理状态
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proxy_enabled = config.proxy.get("enabled", False)
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proxy_url_http = config.proxy.get("http")
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proxy_url_https = config.proxy.get("https")
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# 添加代理开关
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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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# 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 and HTTPS_PROXY:
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config.proxy["http"] = HTTP_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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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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import requests
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if provider.lower() == 'gemini':
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# 原生Gemini API测试
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try:
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# 构建请求数据
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request_data = {
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"contents": [{
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"parts": [{"text": "直接回复我文本'当前网络可用'"}]
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}],
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"generationConfig": {
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"temperature": 1.0,
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"topK": 40,
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"topP": 0.95,
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"maxOutputTokens": 100,
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},
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"safetySettings": [
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{
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"category": "HARM_CATEGORY_HARASSMENT",
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"threshold": "BLOCK_NONE"
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},
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{
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"category": "HARM_CATEGORY_HATE_SPEECH",
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"threshold": "BLOCK_NONE"
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},
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{
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"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
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"threshold": "BLOCK_NONE"
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},
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{
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"category": "HARM_CATEGORY_DANGEROUS_CONTENT",
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"threshold": "BLOCK_NONE"
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}
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]
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}
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# 构建请求URL
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api_base_url = base_url or "https://generativelanguage.googleapis.com/v1beta"
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url = f"{api_base_url}/models/{model_name}:generateContent?key={api_key}"
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# 发送请求
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response = requests.post(
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url,
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json=request_data,
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headers={"Content-Type": "application/json"},
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timeout=30
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)
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if response.status_code == 200:
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return True, tr("原生Gemini模型连接成功")
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else:
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return False, f"{tr('原生Gemini模型连接失败')}: HTTP {response.status_code}"
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except Exception as e:
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return False, f"{tr('原生Gemini模型连接失败')}: {str(e)}"
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elif provider.lower() == 'gemini(openai)':
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# OpenAI兼容的Gemini代理测试
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try:
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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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test_url = f"{base_url.rstrip('/')}/chat/completions"
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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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"stream": False
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}
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response = requests.post(test_url, headers=headers, json=test_data, timeout=10)
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if response.status_code == 200:
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return True, tr("OpenAI兼容Gemini代理连接成功")
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else:
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return False, f"{tr('OpenAI兼容Gemini代理连接失败')}: HTTP {response.status_code}"
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except Exception as e:
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return False, f"{tr('OpenAI兼容Gemini代理连接失败')}: {str(e)}"
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elif provider.lower() == 'narratoapi':
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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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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,
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)
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response = client.chat.completions.create(
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model=model_name,
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messages=[
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{
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"role": "system",
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"content": [{"type": "text", "text": "You are a helpful assistant."}],
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},
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{
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"role": "user",
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"content": [
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{
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"type": "image_url",
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"image_url": {
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"url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"
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},
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},
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{"type": "text", "text": "回复我网络可用即可"},
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],
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},
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],
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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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# logger.debug(api_key)
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# logger.debug(base_url)
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# logger.debug(model_name)
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return False, f"{tr('QwenVL model is not available')}: {str(e)}"
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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 = ['Siliconflow', 'Gemini', 'Gemini(OpenAI)', 'QwenVL', 'OpenAI']
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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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# 处理特殊的提供商名称映射
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if vision_provider == 'gemini(openai)':
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vision_config_key = 'vision_gemini_openai'
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else:
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vision_config_key = f'vision_{vision_provider}'
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vision_api_key = config.app.get(f"{vision_config_key}_api_key", "")
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vision_base_url = config.app.get(f"{vision_config_key}_base_url", "")
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vision_model_name = config.app.get(f"{vision_config_key}_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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# 根据不同提供商设置默认值和帮助信息
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if vision_provider == '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 or "https://generativelanguage.googleapis.com/v1beta",
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help=tr("原生Gemini API端点，默认: https://generativelanguage.googleapis.com/v1beta")
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)
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st_vision_model_name = st.text_input(
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tr("Vision Model Name"),
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value=vision_model_name or "gemini-2.0-flash-exp",
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help=tr("原生Gemini模型，默认: gemini-2.0-flash-exp")
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)
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elif vision_provider == 'gemini(openai)':
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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 or "https://generativelanguage.googleapis.com/v1beta/openai",
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help=tr("OpenAI兼容的Gemini代理端点，如: https://your-proxy.com/v1")
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)
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st_vision_model_name = st.text_input(
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tr("Vision Model Name"),
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value=vision_model_name or "gemini-2.0-flash-exp",
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help=tr("OpenAI格式的Gemini模型名称，默认: gemini-2.0-flash-exp")
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)
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elif vision_provider == 'qwenvl':
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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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help=tr("Default: https://dashscope.aliyuncs.com/compatible-mode/v1")
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)
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st_vision_model_name = st.text_input(
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tr("Vision Model Name"),
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value=vision_model_name or "qwen-vl-max-latest",
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help=tr("Default: qwen-vl-max-latest")
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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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# 先验证配置
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test_errors = []
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if not st_vision_api_key:
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test_errors.append("请先输入API密钥")
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if not st_vision_model_name:
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test_errors.append("请先输入模型名称")
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if test_errors:
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for error in test_errors:
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st.error(error)
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else:
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with st.spinner(tr("Testing connection...")):
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try:
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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(message)
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else:
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st.error(message)
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except Exception as e:
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st.error(f"测试连接时发生错误: {str(e)}")
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logger.error(f"视频分析模型连接测试失败: {str(e)}")
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# 验证和保存视觉模型配置
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validation_errors = []
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config_changed = False
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# 验证API密钥
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if st_vision_api_key:
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is_valid, error_msg = validate_api_key(st_vision_api_key, f"视频分析({vision_provider})")
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if is_valid:
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config.app[f"{vision_config_key}_api_key"] = st_vision_api_key
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st.session_state[f"{vision_config_key}_api_key"] = st_vision_api_key
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config_changed = True
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else:
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validation_errors.append(error_msg)
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# 验证Base URL
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if st_vision_base_url:
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is_valid, error_msg = validate_base_url(st_vision_base_url, f"视频分析({vision_provider})")
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if is_valid:
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config.app[f"{vision_config_key}_base_url"] = st_vision_base_url
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st.session_state[f"{vision_config_key}_base_url"] = st_vision_base_url
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config_changed = True
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else:
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validation_errors.append(error_msg)
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# 验证模型名称
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if st_vision_model_name:
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is_valid, error_msg = validate_model_name(st_vision_model_name, f"视频分析({vision_provider})")
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if is_valid:
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config.app[f"{vision_config_key}_model_name"] = st_vision_model_name
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st.session_state[f"{vision_config_key}_model_name"] = st_vision_model_name
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config_changed = True
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else:
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validation_errors.append(error_msg)
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# 显示验证错误
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show_config_validation_errors(validation_errors)
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# 如果配置有变化且没有验证错误，保存到文件
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if config_changed and not validation_errors:
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try:
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config.save_config()
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if st_vision_api_key or st_vision_base_url or st_vision_model_name:
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st.success(f"视频分析模型({vision_provider})配置已保存")
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except Exception as e:
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st.error(f"保存配置失败: {str(e)}")
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logger.error(f"保存视频分析配置失败: {str(e)}")
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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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# 特殊处理Gemini
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if provider.lower() == 'gemini':
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# 原生Gemini API测试
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try:
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# 构建请求数据
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request_data = {
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"contents": [{
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"parts": [{"text": "直接回复我文本'当前网络可用'"}]
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}],
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"generationConfig": {
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"temperature": 1.0,
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"topK": 40,
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"topP": 0.95,
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"maxOutputTokens": 100,
|
||
},
|
||
"safetySettings": [
|
||
{
|
||
"category": "HARM_CATEGORY_HARASSMENT",
|
||
"threshold": "BLOCK_NONE"
|
||
},
|
||
{
|
||
"category": "HARM_CATEGORY_HATE_SPEECH",
|
||
"threshold": "BLOCK_NONE"
|
||
},
|
||
{
|
||
"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
|
||
"threshold": "BLOCK_NONE"
|
||
},
|
||
{
|
||
"category": "HARM_CATEGORY_DANGEROUS_CONTENT",
|
||
"threshold": "BLOCK_NONE"
|
||
}
|
||
]
|
||
}
|
||
|
||
# 构建请求URL
|
||
api_base_url = base_url or "https://generativelanguage.googleapis.com/v1beta"
|
||
url = f"{api_base_url}/models/{model_name}:generateContent?key={api_key}"
|
||
|
||
# 发送请求
|
||
response = requests.post(
|
||
url,
|
||
json=request_data,
|
||
headers={"Content-Type": "application/json"},
|
||
timeout=30
|
||
)
|
||
|
||
if response.status_code == 200:
|
||
return True, tr("原生Gemini模型连接成功")
|
||
else:
|
||
return False, f"{tr('原生Gemini模型连接失败')}: HTTP {response.status_code}"
|
||
except Exception as e:
|
||
return False, f"{tr('原生Gemini模型连接失败')}: {str(e)}"
|
||
|
||
elif provider.lower() == 'gemini(openai)':
|
||
# OpenAI兼容的Gemini代理测试
|
||
test_url = f"{base_url.rstrip('/')}/chat/completions"
|
||
test_data = {
|
||
"model": model_name,
|
||
"messages": [
|
||
{"role": "user", "content": "直接回复我文本'当前网络可用'"}
|
||
],
|
||
"stream": False
|
||
}
|
||
|
||
response = requests.post(test_url, headers=headers, json=test_data, timeout=10)
|
||
if response.status_code == 200:
|
||
return True, tr("OpenAI兼容Gemini代理连接成功")
|
||
else:
|
||
return False, f"{tr('OpenAI兼容Gemini代理连接失败')}: HTTP {response.status_code}"
|
||
else:
|
||
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)}"
|
||
|
||
|
||
def render_text_llm_settings(tr):
|
||
"""渲染文案生成模型设置"""
|
||
st.subheader(tr("Text Generation Model Settings"))
|
||
|
||
# 文案生成模型提供商选择
|
||
text_providers = ['OpenAI', 'Siliconflow', 'DeepSeek', 'Gemini', 'Gemini(OpenAI)', 'Qwen', 'Moonshot']
|
||
saved_text_provider = config.app.get("text_llm_provider", "OpenAI").lower()
|
||
saved_provider_index = 0
|
||
|
||
for i, provider in enumerate(text_providers):
|
||
if provider.lower() == saved_text_provider:
|
||
saved_provider_index = i
|
||
break
|
||
|
||
text_provider = st.selectbox(
|
||
tr("Text Model Provider"),
|
||
options=text_providers,
|
||
index=saved_provider_index
|
||
)
|
||
text_provider = text_provider.lower()
|
||
config.app["text_llm_provider"] = text_provider
|
||
|
||
# 获取已保存的文本模型配置
|
||
text_api_key = config.app.get(f"text_{text_provider}_api_key")
|
||
text_base_url = config.app.get(f"text_{text_provider}_base_url")
|
||
text_model_name = config.app.get(f"text_{text_provider}_model_name")
|
||
|
||
# 渲染文本模型配置输入框
|
||
st_text_api_key = st.text_input(tr("Text API Key"), value=text_api_key, type="password")
|
||
|
||
# 根据不同提供商设置默认值和帮助信息
|
||
if text_provider == 'gemini':
|
||
st_text_base_url = st.text_input(
|
||
tr("Text Base URL"),
|
||
value=text_base_url or "https://generativelanguage.googleapis.com/v1beta",
|
||
help=tr("原生Gemini API端点，默认: https://generativelanguage.googleapis.com/v1beta")
|
||
)
|
||
st_text_model_name = st.text_input(
|
||
tr("Text Model Name"),
|
||
value=text_model_name or "gemini-2.0-flash-exp",
|
||
help=tr("原生Gemini模型，默认: gemini-2.0-flash-exp")
|
||
)
|
||
elif text_provider == 'gemini(openai)':
|
||
st_text_base_url = st.text_input(
|
||
tr("Text Base URL"),
|
||
value=text_base_url or "https://generativelanguage.googleapis.com/v1beta/openai",
|
||
help=tr("OpenAI兼容的Gemini代理端点，如: https://your-proxy.com/v1")
|
||
)
|
||
st_text_model_name = st.text_input(
|
||
tr("Text Model Name"),
|
||
value=text_model_name or "gemini-2.0-flash-exp",
|
||
help=tr("OpenAI格式的Gemini模型名称，默认: gemini-2.0-flash-exp")
|
||
)
|
||
else:
|
||
st_text_base_url = st.text_input(tr("Text Base URL"), value=text_base_url)
|
||
st_text_model_name = st.text_input(tr("Text Model Name"), value=text_model_name)
|
||
|
||
# 添加测试按钮
|
||
if st.button(tr("Test Connection"), key="test_text_connection"):
|
||
# 先验证配置
|
||
test_errors = []
|
||
if not st_text_api_key:
|
||
test_errors.append("请先输入API密钥")
|
||
if not st_text_model_name:
|
||
test_errors.append("请先输入模型名称")
|
||
|
||
if test_errors:
|
||
for error in test_errors:
|
||
st.error(error)
|
||
else:
|
||
with st.spinner(tr("Testing connection...")):
|
||
try:
|
||
success, message = test_text_model_connection(
|
||
api_key=st_text_api_key,
|
||
base_url=st_text_base_url,
|
||
model_name=st_text_model_name,
|
||
provider=text_provider,
|
||
tr=tr
|
||
)
|
||
|
||
if success:
|
||
st.success(message)
|
||
else:
|
||
st.error(message)
|
||
except Exception as e:
|
||
st.error(f"测试连接时发生错误: {str(e)}")
|
||
logger.error(f"文案生成模型连接测试失败: {str(e)}")
|
||
|
||
# 验证和保存文本模型配置
|
||
text_validation_errors = []
|
||
text_config_changed = False
|
||
|
||
# 验证API密钥
|
||
if st_text_api_key:
|
||
is_valid, error_msg = validate_api_key(st_text_api_key, f"文案生成({text_provider})")
|
||
if is_valid:
|
||
config.app[f"text_{text_provider}_api_key"] = st_text_api_key
|
||
text_config_changed = True
|
||
else:
|
||
text_validation_errors.append(error_msg)
|
||
|
||
# 验证Base URL
|
||
if st_text_base_url:
|
||
is_valid, error_msg = validate_base_url(st_text_base_url, f"文案生成({text_provider})")
|
||
if is_valid:
|
||
config.app[f"text_{text_provider}_base_url"] = st_text_base_url
|
||
text_config_changed = True
|
||
else:
|
||
text_validation_errors.append(error_msg)
|
||
|
||
# 验证模型名称
|
||
if st_text_model_name:
|
||
is_valid, error_msg = validate_model_name(st_text_model_name, f"文案生成({text_provider})")
|
||
if is_valid:
|
||
config.app[f"text_{text_provider}_model_name"] = st_text_model_name
|
||
text_config_changed = True
|
||
else:
|
||
text_validation_errors.append(error_msg)
|
||
|
||
# 显示验证错误
|
||
show_config_validation_errors(text_validation_errors)
|
||
|
||
# 如果配置有变化且没有验证错误，保存到文件
|
||
if text_config_changed and not text_validation_errors:
|
||
try:
|
||
config.save_config()
|
||
if st_text_api_key or st_text_base_url or st_text_model_name:
|
||
st.success(f"文案生成模型({text_provider})配置已保存")
|
||
except Exception as e:
|
||
st.error(f"保存配置失败: {str(e)}")
|
||
logger.error(f"保存文案生成配置失败: {str(e)}")
|
||
|
||
# # Cloudflare 特殊配置
|
||
# if text_provider == 'cloudflare':
|
||
# st_account_id = st.text_input(
|
||
# tr("Account ID"),
|
||
# value=config.app.get(f"text_{text_provider}_account_id", "")
|
||
# )
|
||
# if st_account_id:
|
||
# config.app[f"text_{text_provider}_account_id"] = st_account_id
|